# FlavoRotor research corpus

Canonical documentation for the FlavoRotor rotating hydroponic research platform.

## Research overview

- Canonical URL: https://flavorotor.com/research
- Document ID: FR-001
- Group: Start here
- Version: 1.2
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/overview
- JSON: https://flavorotor.com/research/data/chapters/overview.json

A complete guide to the FlavoRotor machine, controlled cultivation variables, plant responses, experimental methods and repeatable crop recipes.

System FlavoRotor is a rotating hydroponic research platform for controlled plant cultivation. The physical cultivation platform. The rotating chamber, plant positions and central light module are visible in the current prototype. What the system controls FlavoRotor controls the environment delivered to a plant: light, photoperiod, nutrient additions, pH management, electrical conductivity, root-zone temperature, water exposure and rotation. Sensors and actuator logs record what the plant received over time. Set a treatment → Calibrate delivery → Grow the plant → Measure chemistry and sensory response → Repeat How flavour enters the experiment Taste, aroma, colour and texture are plant responses. They are measured after a defined cultivation treatment; they are not inferred from a pump command or a single sensor value. Published crop studies show that spectrum, nutrient composition, solution strength and root-zone temperature can change relevant chemical or quality measurements under controlled conditions. [R01] [R11] [R12] [R13] What this documentation contains The following chapters explain the machine, the cultivation variables, crop-specific mechanisms, equations, calibration procedures, experimental designs, chemical analysis, sensory testing and recipe transfer. The complete document is designed to be read continuously from this point.

Visual records:
- From a controlled treatment to a repeatable crop recipe (Mermaid-compatible system diagram). Sources: I01, I02. Mermaid source: https://flavorotor.com/research/data/diagrams/cultivation-control-loop.mmd.

References: I01, I02, R01, R02, R05, R06, R09, R11, R12, R13, R14

## Research roadmap

- Canonical URL: https://flavorotor.com/research/research-roadmap
- Document ID: FR-RMP-001
- Group: Start here
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/research-roadmap
- JSON: https://flavorotor.com/research/data/chapters/research-roadmap.json

The ordered validation sequence from machine calibration to independently reproducible sensory recipes.

In brief The ordered validation sequence from machine calibration to independently reproducible sensory recipes. Objective FlavoRotor is developed as a programmable cultivation platform. The research objective is to measure how cultivation inputs change plant outcomes, then reproduce the useful responses with the same crop and recorded conditions. Validation stages Stage Question Release gate 1. Engineering calibration Does each sensor and actuator reproduce its command within a declared uncertainty? Calibration report and raw data 2. Empty-system mapping What spatial and temporal gradients exist before plants are added? Light, temperature, humidity, rotation and reservoir maps 3. Biological baseline Can one cultivar be grown repeatedly with one fixed recipe? At least three independent cycles 4. Single-factor screening Which controllable factor produces a measurable effect? Preregistered control and treatment comparison 5. Chemical and sensory confirmation Is the effect chemically measurable and perceptible? Instrumental analysis plus blinded sensory test 6. Interaction model How do selected factors interact? Factorial or response-surface experiment 7. Recipe replication Can the result be reproduced on another cycle or unit? Replication report 8. Transfer Can the recipe be translated to a larger system using physical variables? Scale-transfer report Publication rule Each released result remains linked to its protocol version, biological material, system identifier, calibration records, raw dataset, processing code and conclusion linked to the tested conditions. This structure follows reusable plant-experiment metadata and FAIR data principles. [R23] [R24]

References: R23, R24

## Terminology and units

- Canonical URL: https://flavorotor.com/research/terminology-units
- Document ID: REF-UNIT-001
- Group: Start here
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/terminology-units
- JSON: https://flavorotor.com/research/data/chapters/terminology-units.json

Definitions and unit conventions used throughout the FlavoRotor research record.

In brief Definitions and unit conventions used throughout the FlavoRotor research record. Core definitions Term Operational definition Taste Basic gustatory perception such as sweet, sour, bitter, salty or umami Aroma Olfactory contribution arising predominantly from volatile compounds Flavour Combined taste, aroma, texture, trigeminal and contextual perception Texture Mechanical and structural perception measured instrumentally and/or sensorially Recipe Versioned schedule of measurable cultivation setpoints and actions Biological replicate An independently grown plant or experimental unit Technical replicate Repeated measurement of the same biological sample Independent cycle A cultivation run started at a separate time with a new biological batch Required units Quantity Symbol Unit Hydrogen-ion activity pH dimensionless logarithmic activity scale Electrical conductivity EC mS/cm, temperature reported Photon flux density PPFD µmol·m⁻²·s⁻¹ Daily light integral DLI mol·m⁻²·d⁻¹ Temperature T °C Relative humidity RH % Vapour-pressure deficit VPD kPa Angular speed n rev/min Angular velocity ω rad/s Flow Q mL/min Concentration c mmol/L or mg/L, species stated Fresh/dry mass m g Reporting rules EC is always reported with solution temperature or temperature compensation. pH is reported with calibration date, buffers and electrode identifier. Light is reported at plant position, not inferred only from electrical wattage. Concentration values identify the chemical species and basis, for example mg/L K rather than “potassium EC”. Mean values include variability, sample size and the experimental unit. Metrology source Accuracy, precision, repeatability, resolution, calibration and uncertainty are used according to international metrology vocabulary and guidance. [R38] [R39]

References: R38, R39

## Experimental platform

- Canonical URL: https://flavorotor.com/research/platform
- Document ID: TR-SYS-001
- Group: Experimental platform
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/platform
- JSON: https://flavorotor.com/research/data/chapters/platform.json

System-level architecture of the rotating cultivation drum, reservoir, lighting, sensing, dosing and data systems.

In brief System-level architecture of the rotating cultivation drum, reservoir, lighting, sensing, dosing and data systems. System decomposition. The exploded view identifies the mechanical and functional layers that must be validated independently. Source: internal engineering record. [I02] Platform purpose The platform is designed to expose multiple plants to a common, logged environment while allowing programmable changes in cultivation inputs. Its role is not to assume a flavour outcome; its role is to deliver and record treatments with sufficient repeatability to test one. Subsystems Subsystem Controlled or observed quantity Required validation Rotating drum speed, direction, duty cycle, immersion sequence RPM trace, position repeatability, vibration, load test Magnetic drive transmission ratio and overload slip static slip torque and loaded endurance Axial lighting spectrum, PPFD, photoperiod spectroradiometric map and DLI Nutrient reservoir volume, level, temperature, pH, EC mixing time, drift, leak and sanitation test Four-channel dosing stock-liquid volume channel-specific gravimetric calibration Imaging repeatable plant image fixed geometry, exposure and colour reference Data system timestamped observations and commands clock, schema, missing-data and audit-log tests Cultivation cycle Recipe load → Pre-flight checks → Growth and logging → Treatment window → Standardised harvest → Analysis Internal engineering records The supplied v1 project report documents printed mechanical parts, sensor electronics, PCB fabrication, software monitoring and an incompletely assembled final device due to delayed components. The v2 report documents a revised magnetic-drive and four-pump design. This site therefore reports the platform as a documented engineering development; hardware performance is promoted only when a dedicated calibration report exists. FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I01] [I02]

References: I01, I02

## Version 1.0 to 2.0

- Canonical URL: https://flavorotor.com/research/version-evolution
- Document ID: TR-HIST-001
- Group: Experimental platform
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/version-evolution
- JSON: https://flavorotor.com/research/data/chapters/version-evolution.json

The engineering changes between the initial rotating prototype and the v2.0 research-platform concept.

In brief The engineering changes between the initial rotating prototype and the v2.0 research-platform concept. Initial prototype record. A manufactured rotor component documented during the first build phase. It establishes fabrication progress, not validated cultivation performance. [I01] v2.0 architecture. The revised assembly introduces the magnetic drive, central optical module and separate dosing subsystem. It records design intent, not a completed comparative test. [I02] Subsystem evolution Subsystem v1 record v2 record Research significance Drive conventional stepper/roller architecture magnetic coupling concept requires new torque and speed calibration Nutrient delivery manual/general solution control four custom peristaltic channels enables versioned experimental dosing after calibration Imaging monitoring concept central camera/CNN concept requires repeatable capture and device-specific dataset Lighting axial LED concept specified blue/red/far-red/white concept requires measured spectrum and spatial map Exterior functional prototype frame stationary and rotating design layers may affect airflow and optical distribution Software dashboard and taste-profile prototype recipe/feedback concept must separate measured variables from sensory outcomes Research lesson Every mechanical redesign can change the experimental environment. A recipe validated on v1 cannot be assumed valid on v2 unless light, root-zone exposure, air flow and control performance are shown equivalent. FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I01] [I02]

References: I01, I02

## Rotating cultivation drum

- Canonical URL: https://flavorotor.com/research/rotating-drum
- Document ID: TR-MEC-001
- Group: Experimental platform
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/rotating-drum
- JSON: https://flavorotor.com/research/data/chapters/rotating-drum.json

Mechanical function, plant-module geometry, sequential immersion and the measurements required before biological comparison.

In brief Mechanical function, plant-module geometry, sequential immersion and the measurements required before biological comparison. Explanation Plants are arranged around a cylindrical drum. As the drum turns, each root module passes through the nutrient reservoir and then drains in air. A central light source is intended to keep the plant positions at similar radial distance from the source. Functions to validate Function Engineering metric Biological risk if uncontrolled Rotation mean RPM, within-cycle variation, direction unequal immersion and mechanical stimulus Immersion time in solution, depth, interval unequal water and nutrient exposure Drainage retained volume and drain time root-zone oxygen differences Position balance radial mass distribution vibration and speed modulation Plant retention module force and displacement plant damage or loss Cleanability accessible wetted surfaces biofilm and cross-cycle contamination Immersion timing M-1 T rev = 60 / n Rotation period in seconds for drum speed n in revolutions per minute. M-2 t imm = (θ bath / 2π) · T rev Immersion time for a measured bath-contact angular span θbath in radians. The immersion time must be calculated from the measured contact angle and verified with video or position sensing. It must not be inferred from RPM alone. Biological comparison requirement A rotation trial needs a static or movement-matched control with equivalent mean DLI, root-zone exposure and air flow. Otherwise light, watering and mechanical effects remain confounded. FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I01] [I02]

References: I01, I02

## Magnetic drive system

- Canonical URL: https://flavorotor.com/research/magnetic-drive
- Document ID: TR-MAG-001
- Group: Experimental platform
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/magnetic-drive
- JSON: https://flavorotor.com/research/data/chapters/magnetic-drive.json

Transmission ratio, overload behaviour, preliminary calculations and the required slip-torque validation.

In brief Transmission ratio, overload behaviour, preliminary calculations and the required slip-torque validation. Magnetic coupling interface. CAD record; transmitted torque remains subject to bench measurement. [I02] Driving pinion construction. Original component view from the v2.0 report. [I02] Driven-ring magnet layout. Geometry supports the analytical transmission model; slip torque and endurance require measurement. [I02] Design definition Parameter Design record Driving wheel teeth 15 Driven wheel teeth 146 Nominal ratio 146/15 = 9.733:1 Magnet type NdFeB N42, Ø8 × 3 mm in the v2 specification Nominal air gap 2.5 mm in the v2 specification Intended behaviour non-contact torque transfer with overload slip Kinematic model MAG-1 i = Z₂ / Z₁ = 146 / 15 = 9.733 Nominal transmission ratio. MAG-2 n₂ = n₁ / i Nominal driven speed if synchronism is maintained. MAG-3 T₂ = 60 / n₂ Driven-wheel rotation period in seconds. Why the earlier force estimate is not a final result A magnetic dipole approximation can support preliminary sizing, but the short separation, finite cylindrical magnets, alternating polarities, tooth geometry and simultaneous interactions violate the simplest far-field assumptions. The resulting torque must therefore be treated as an analytical estimate, not a verified 3.9 N·m capability. Required validation Test Method Reported output Static slip torque force gauge at known radius torque-angle curve and peak slip torque Starting load incremental drum load minimum starting torque and motor current Speed stability encoder or video tachometry mean RPM, SD and periodic ripple Endurance loaded operation over defined hours slip events, temperature and drift Misalignment controlled axial/radial offset torque margin and failure threshold MAG-4 SF = τ slip,measured / τ required,max Safety factor based on measured slip torque and measured worst-case required torque. FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I02]

References: I02

## Nutrient reservoir and root-zone exposure

- Canonical URL: https://flavorotor.com/research/nutrient-reservoir
- Document ID: TR-RTZ-001
- Group: Experimental platform
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/nutrient-reservoir
- JSON: https://flavorotor.com/research/data/chapters/nutrient-reservoir.json

Reservoir volume, mixing, sequential immersion, oxygenation, sanitation and the variables required for repeatable root-zone exposure.

In brief Reservoir volume, mixing, sequential immersion, oxygenation, sanitation and the variables required for repeatable root-zone exposure. Function The reservoir is both the nutrient-solution storage volume and the sequential root-contact zone. This reduces the need for a separate recirculation circuit, but it makes liquid level, mixing, temperature, oxygen and carry-over central experimental variables. Required reservoir state Variable Why it matters Minimum record Working volume converts dose volume into concentration change pre- and post-dose volume or level Liquid level sets immersion depth and time continuous or per-cycle level Temperature affects roots, electrode response and oxygen solubility logged °C pH affects nutrient speciation and uptake calibrated pH trace EC bulk ionic-strength proxy temperature-corrected EC trace Dissolved oxygen root-zone aeration indicator DO where instrumentation is available Mixing time determines when feedback is valid step-response test Sanitation state controls biological carry-over cleaning batch and verification Mixing validation Inject a harmless conductivity tracer or a small controlled nutrient-stock dose at the normal dosing point. Measure EC at the control sensor and at representative reservoir positions until all readings remain within the predefined mixing tolerance. The maximum observed stabilisation time becomes the minimum feedback delay. Volume balance RTZ-1 V R,k+1 = V R,k + Σv dose + v water − v sampling − v loss Reservoir working-volume update for a control interval. FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I01] [I02]

References: I01, I02

## Light, spectrum and DLI

- Canonical URL: https://flavorotor.com/research/lighting
- Document ID: MTH-LGT-001
- Group: Experimental platform
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/lighting
- JSON: https://flavorotor.com/research/data/chapters/lighting.json

Spectral composition, PPFD, DLI, photoperiod, plant position and the distinction between electrical power and photon exposure.

In brief Spectral composition, PPFD, DLI, photoperiod, plant position and the distinction between electrical power and photon exposure. Central optical module. Original v2.0 design image. Plant-level photon exposure must be established by a measured PPFD and spectral map. [I02] Required metrics Metric Definition Spectrum photon distribution by wavelength at plant position PPFD instantaneous 400–700 nm photon flux density DLI daily integrated photosynthetic photon exposure Photoperiod scheduled light duration Uniformity spatial distribution across plant positions Leaf temperature thermal outcome at the tissue, not only room temperature L-1 DLI = PPFD · t h · 3600 / 10⁶ DLI in mol·m⁻²·d⁻¹ for constant PPFD and photoperiod th in hours. Published basil study In hydroponic Italian Large Leaf basil, controlled supplemental-light spectra altered key aroma volatiles under a defined experimental environment. [R01] The transferable conclusion is that spectrum is a valid treatment variable. The exact result requires matching cultivar, DLI and other conditions. Why wattage is insufficient A 50 W electrical rating and manufacturer efficacy estimate cannot define PPFD inside a cylindrical system. Optical distribution, distance, angle, reflection, obstruction, thermal state and plant position must be measured.

References: I02, R01

## Perimeter status lighting

- Canonical URL: https://flavorotor.com/research/perimeter-status-lighting
- Document ID: TR-LGT-002
- Group: Experimental platform
- Version: 1.0
- Updated: 2026-07-27
- Markdown: https://flavorotor.com/research/markdown/perimeter-status-lighting
- JSON: https://flavorotor.com/research/data/chapters/perimeter-status-lighting.json

Electrical, thermal and optical boundaries for the addressable perimeter lighting used as a system-status interface.

In brief The perimeter LED assembly is documented as a status and interaction layer. It is not treated as a calibrated horticultural light source. Purpose The perimeter lighting communicates operating state, warnings, service conditions and user interactions without changing the central cultivation-light recipe. Colour assignments are interface states and are versioned in the software configuration. Function Required behaviour Research relevance Normal operation stable, low-glare indication must not alter a declared dark period Warning visible, distinct state event is written to the operating record Critical fault unambiguous alert associated actuator state and timestamp are preserved Service mode local identification of the active module prevents maintenance events from being hidden in a trial Engineering requirements exact LED family and revision recorded in the bill of materials; maximum and typical channel current measured on the installed assembly; voltage drop measured at the first, middle and final segment; surface and enclosure temperature recorded at worst-case command; power-supply headroom, connector rating and conductor cross-section documented; brightness limited for night operation and camera acquisition; status meanings remain accessible through text or the dashboard and do not rely on colour alone. Validation plan Bench validation records current, voltage, temperature and command latency for representative patterns. Optical validation records spectrum and PPFD at plant positions with the central lamp off and on. Trial protocols state whether perimeter lighting was disabled, constant or included in the measured recipe. Permitted claims The website may state that the v2.0 architecture includes addressable perimeter status lighting. It may not state that the subsystem supplements photosynthesis, improves flowering or delivers a defined wavelength until a component-specific spectrum and plant-position photon map are published.

References: I02, R33

## Environmental sensing and calibration

- Canonical URL: https://flavorotor.com/research/sensing-calibration
- Document ID: PR-SEN-001
- Group: Experimental platform
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/sensing-calibration
- JSON: https://flavorotor.com/research/data/chapters/sensing-calibration.json

Calibration and verification requirements for pH, EC, temperature, level, light and rotation measurements.

In brief Calibration and verification requirements for pH, EC, temperature, level, light and rotation measurements. Sensing architecture. Original project diagram showing the intended data path. [I01] Prototype sensor assembly. Internal build evidence; measurement traceability depends on the published calibration record. [I01] Measurement principle A sensor reading becomes research data only when its identity, calibration, range, sampling interval, temperature conditions and failure rules are recorded. Minimum calibration plan Sensor Calibration/verification Frequency trigger pH two- or three-point buffers bracketing operation; slope and offset retained before a trial, after cleaning, on drift or according to electrode stability EC certified conductivity standard near operating range; temperature compensation checked before a trial and after probe maintenance Solution temperature comparison with traceable reference in stirred bath before deployment and on replacement Level measured-volume additions across working range after geometry or sensor position changes PPFD reference quantum sensor/spectroradiometer mapping after light, optics or geometry changes Rotation encoder or video reference across commanded speeds after drive or load changes pH electrode model SEN-1 E = E⁰ − (2.303RT/F) · pH Ideal Nernst response of a hydrogen-ion-sensitive electrode; practical slope and offset are fitted during calibration. Calibration record calibration_id, sensor_id, sensor_model, serial_number, reference_standard, reference_lot, reference_value, measured_value, solution_temperature, fitted_slope, fitted_offset, residual, operator, timestamp, firmware_version Fault rules Out-of-range, non-finite, implausibly fast-changing or stale measurements disable automatic correction. The system logs the rejected value and the reason; it does not silently replace it with a plausible number. Traceability Calibration records identify reference material, method, environmental conditions, corrections and uncertainty. [R38] [R39] [R43] [R46]

References: I01, R38, R39, R43, R46

## Data acquisition architecture

- Canonical URL: https://flavorotor.com/research/data-acquisition
- Document ID: TR-DAT-001
- Group: Experimental platform
- Version: 1.2
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/data-acquisition
- JSON: https://flavorotor.com/research/data/chapters/data-acquisition.json

The event, sensor and recipe records required to reconstruct every FlavoRotor cultivation run.

In brief The event, sensor and recipe records required to reconstruct every FlavoRotor cultivation run. Monitoring interface. Original software prototype screen from the project report. [I01] Historical record view. The interface supports traceability only when stored values retain sensor, calibration and recipe identifiers. [I01] Reconstruction requirement An independent analyst must be able to reconstruct what the system was commanded to do, what it actually measured, what deviations occurred and which samples were harvested. Core data streams Stream Examples Primary key System state mode, faults, firmware, system version timestamp + system_id Sensors pH, EC, temperature, level, PPFD reference timestamp + sensor_id Actuators pump steps, channel, rotation command, light state event_id Recipe time-indexed setpoints and limits recipe_id + version Biological material species, cultivar, seed lot, position sample_id Observations mass, image, colour, chemistry, sensory observation_id Calibration model coefficients and validity calibration_id Time integrity The controller and server clocks are synchronised before a trial. Records use UTC internally and retain the local timezone for human-readable reports. Missing intervals are represented explicitly; time series are never filled silently. Metadata standard The study package follows MIAPPE concepts for investigation, study, biological material, environment and observed variables, and FAIR principles for durable identifiers and machine-readable metadata. [R23] [R24] FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I01]

Visual records:
- Every published result remains traceable to the cultivation run (Mermaid-compatible data-lineage diagram). Sources: I01, R23, R24. Mermaid source: https://flavorotor.com/research/data/diagrams/research-data-lineage.mmd.

References: I01, R23, R24

## Camera, plant phenotyping and machine learning

- Canonical URL: https://flavorotor.com/research/imaging-plant-health
- Document ID: TR-IMG-001
- Group: Experimental platform
- Version: 3.0
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/imaging-plant-health
- JSON: https://flavorotor.com/research/data/chapters/imaging-plant-health.json

Indexed camera geometry, longitudinal plant measurements, real image datasets, reproducible augmentation, grouped validation and measured MobileNetV2, EfficientNetB0 and ResNet50 performance.

Plant imaging The central camera photographs the same plant position repeatedly and stores every image with its plant, cultivation cycle and camera settings. Central camera. The camera remains stationary while the rotor brings each plant to its recorded image position. [I02] Camera and light module. One acquisition record identifies camera pose, plant position and illumination state. [I02] Camera geometry The camera is fixed to the central module. The rotor stops at a known encoder position and presents one plant module to the lens. At that position, the optical axis meets the local plant plane at 90°. The distance, lens and framing therefore remain comparable when the same plant returns for its next image. The acquisition record stores camera version, lens, focus, working distance, encoder position and image dimensions. [I02] [R52] What is stored with each image The image file alone is not enough. Its record identifies the plant, growth cycle, rotor position, exposure, gain, white balance and light state. A scale reference makes pixel measurements comparable. A colour target reveals changes in illumination or camera response. Focus, clipping and occlusion are stored as visible image-quality fields. Field group Stored values Reason Plant plant_id, crop, cultivar, seed lot, cycle_id keeps repeated images attached to one biological specimen Position position_id, encoder index, camera pose, working distance shows where and how the image was taken Camera camera_version, lens, focus, exposure, gain, white balance separates plant change from camera change Growing conditions recipe_version, light state, temperature, pH, EC, rotation state connects the image to the measured environment File history timestamp_utc, SHA-256, annotation version, operator identifies the exact file and label version Images used for model development Three image sources have different jobs. ImageNet supplies the general visual weights used to initialise MobileNetV2. PlantVillage supplies clean, labelled leaf images. PlantDoc adds leaves photographed with natural backgrounds and changing viewpoints. FlavoRotor images represent the camera, lighting and plant geometry in which the model operates. Results from these sources remain separate because a clean single-leaf photograph is different from a plant growing inside the machine. [R30] [R51] [R53] [R56] Images Content Use ImageNet-1K [R53] general photographs from many object classes initial weights for edges, textures and shapes PlantVillage [R30] 54,306 controlled RGB leaf images covering healthy tissue and plant diseases controlled leaf-classification benchmark PlantDoc [R51] 2,598 plant images from 13 species and 27 healthy or disease classes comparison under natural backgrounds and variable framing FlavoRotor [I02] indexed images from the central camera device-specific plant tracking and evaluation The reproducible web example downloads the complete PlantVillage Strawberry RGB subset at repository commit 7f7ecc7 : 456 healthy images and 1,109 leaf-scorch images. The repository's published leaf map identifies 1,232 of those images as observations of 190 physical leaves. The Python script records every selected filename, SHA-256 hash, class, leaf group and transform parameter. Split by physical leaf Photographs of the same physical leaf stay together. The deterministic seed 20260729 assigns leaf groups to 70% training, 15% validation and 15% test partitions. This prevents near-duplicate photographs of one leaf from appearing in both training and test data. Class Training Validation Test Healthy 316 images / 80 leaves 68 images / 17 leaves 72 images / 18 leaves Leaf scorch 534 images / 52 leaves 115 images / 11 leaves 127 images / 12 leaves Total 850 images / 132 leaves 183 images / 28 leaves 199 images / 30 leaves Image augmentation The Python preprocessing script creates the examples below from one published leaf-scorch image. Rotation, scale, brightness, contrast, saturation, blur and sensor noise change the complete frame by a recorded amount. They do not paint new spots or remove existing tissue. Only the training partition receives random augmentation; validation and test images keep their original pixels apart from the fixed resize and normalisation. [R54] MobileNetV2 and the MLP classifier The reference model receives a 224 × 224 sRGB image. MobileNetV2, initialised with ImageNet-1K weights, converts the image into a 7 × 7 × 1,280 feature map. Global average pooling produces a 1,280-value vector. The MLP maps that vector to 256 ReLU6 units, applies dropout 0.25, and produces one logit for every declared plant-health class. [R31] [R53] Stage Shape Operation Input 224 × 224 × 3 sRGB image with ImageNet normalisation Encoder 7 × 7 × 1,280 MobileNetV2 features Pooling 1,280 global average pooling Hidden layer 256 linear layer, ReLU6 and dropout 0.25 Output C one logit per class Probability C softmax followed by validation-set temperature scaling Why this model fits the camera task MobileNetV2 uses depthwise convolutions and inverted residual blocks. With the 256-unit classifier, it contains 2,586,434 parameters. The same camera is evaluated at every indexed plant position, so model size and single-image latency matter alongside classification performance. [R31] The classifier head Global average pooling converts the 7 × 7 × 1,280 encoder output into one 1,280-value feature vector. The multilayer perceptron learns combinations of those features that separate the declared classes. ReLU6 bounds each hidden activation between 0 and 6; dropout removes 25% of hidden activations at random during fitting. The final linear layer returns two logits, which softmax converts into class probabilities. CV-MLP h = Dropout(ReLU6(W₁z + b₁), 0.25), ℓ = W₂h + b₂ z is the 1,280-value MobileNetV2 feature vector, h contains 256 hidden activations and ℓ contains one logit per class. The 1,280 × 256 hidden layer has 327,680 weights and 256 biases. The 256 × 2 output has 512 weights and 2 biases. The MLP therefore contributes 328,450 trainable parameters. Encoder comparison MobileNetV2, EfficientNetB0 and ResNet50 were tested with the same frozen ImageNet protocol, 256-unit ReLU6 MLP, physical-leaf split and three deterministic seeds. All three reached 100% mean accuracy on the 199-image controlled test. The natural-background PlantDoc check separated them: mean healthy-class recall was 84.38% for MobileNetV2, 74.65% for EfficientNetB0 and 55.90% for ResNet50. MobileNetV2 also required the fewest parameters and had the lowest measured CPU latency: 2.59 million parameters and 60.59 ms median, compared with 4.38 million and 83.35 ms for EfficientNetB0, and 24.11 million and 171.27 ms for ResNet50. Latency was measured with TensorFlow 2.18 inside a three-core Docker limit on an Intel Xeon E5-2699 v3. Processor choice changes throughput, not the mathematical definition of accuracy. Five-fold grouped cross-validation A second test used all 1,232 images tied to 190 physical leaves. The folds were stratified by class; every leaf appeared in one test fold and never in that fold’s training or validation data. MobileNetV2 and the MLP were refitted in every fold. Aggregate accuracy was 99.84%, balanced accuracy 99.87% and macro F1 99.83%; two of 776 leaf-scorch images were assigned healthy and all 456 healthy images were assigned correctly. Mean PlantDoc healthy recall across the five fitted models was 80.00% with a standard deviation of 8.38 percentage points. The image classifier assigns one of its declared visual labels to the current frame. Biomass forecasting, nutrient-state estimation and stress attribution use separate models with repeated images and measured cultivation variables. Model fitting Fit the MLP. The ImageNet encoder remains frozen while the classifier learns from the grouped training images. Fine-tune the final encoder blocks. A lower learning rate adjusts the highest-level visual features; validation macro F1 controls early stopping. Calibrate probability. One temperature value is fitted to validation logits after the model weights stop changing. [R55] Lock the test. Architecture, preprocessing, class thresholds and the low-confidence rule are fixed before test images are opened. Report each image domain separately. PlantVillage, PlantDoc and FlavoRotor results use separate tables, because their camera conditions differ. Controlled-image benchmark The reproducible TensorFlow 2.18.0 run completed on 29 July 2026. After five epochs, the locked model classified all 199 images in the grouped PlantVillage test partition correctly: 72 healthy and 127 leaf scorch. The test partition contains 30 physical leaf groups that were absent from training and validation. Accuracy, macro F1 and balanced accuracy are each 1.0000 for this controlled two-class benchmark. The fitted temperature is 0.500584 and the ten-bin expected calibration error is 0.0000361. PlantVillage photographs isolated leaves against a controlled background. The result above therefore measures discrimination between those two published Strawberry classes under the same acquisition style; it is not substituted for a measurement from the FlavoRotor camera. [R30] [R56] Uncertainty around the measured scores Every classification score is estimated from a finite test set. The controlled test observed 199 correct assignments from 199 images, but its exact two-sided 95% interval is 98.16–100%. Class recall has wider intervals because each class contains fewer observations: 95.01–100% for 72 healthy images and 97.14–100% for 127 leaf-scorch images. The interval states how much precision the test count provides; it does not change the observed confusion matrix. Natural-background check The frozen model was then applied without retraining to all 96 images in PlantDoc's Strawberry leaf class. These photographs contain natural backgrounds, changing scale, partial leaves and varied lighting. The model assigned 79 images to healthy and 17 to leaf scorch, which gives healthy-class recall of 79 / 96 = 0.8229. PlantDoc does not publish a matching Strawberry leaf-scorch class, so this check reports recall for its healthy class rather than two-class accuracy. [R51] [R56] Metric definitions Precision answers: of the images assigned to one class, how many are correct? Recall answers: of the images that truly belong to that class, how many were found? F1 combines both values. Macro F1 gives every class the same weight, so the larger leaf-scorch class cannot hide weak performance on healthy leaves. The report also contains the complete confusion matrix, balanced accuracy, image count and physical-leaf count for every class. [R55] [R56] CV-F1 F1 = 2 · precision · recall / (precision + recall) F1 combines precision and recall. Macro F1 is the arithmetic mean of the class-level F1 values, so a large class cannot hide poor performance on a smaller class. CV-ECE ECE = Σ |Bm| / n · |accuracy(Bm) − confidence(Bm)| Predictions are grouped into confidence bins. ECE measures the weighted difference between observed accuracy and mean reported confidence in those bins. Following one plant through time Repeated images of the same plant form a time series. Projected canopy area, calibrated colour, developmental stage and image quality are stored beside temperature, light, pH, EC and recipe version at the same timestamp. Growth rate compares the same plant at two recorded times. It does not compare unrelated plants photographed on different days. [R52] Output Reference annotation Use Canopy area and growth rate manual masks and a physical scale reference tracks the plant's visible growth Developmental stage crop-specific, expert-reviewed labels aligns treatment timing with plant development Colour index colour target and matching laboratory measurements measures visible colour change Plant-health class expert label and supporting laboratory result where required records class and probability for review Image quality focus, exposure, occlusion and pose labels identifies unsuitable images Datasets used to test longitudinal methods Dataset Repeated observations Role in the research programme Aalto lettuce [R57] 18 identified heads, 30 biomass days, 731 canopy images and 1,443 environmental records implemented three-day biomass forecast with plant-wise validation HydroGrowNet [R61] three 30-day Batavia cycles and more than 390,000 segmented images aligned with pH, EC and water temperature independent multimodal growth and anomaly dataset Multi-sensor lettuce phenotyping [R62] 45 plants over 42 days, two cultivars, three nitrogen levels and two irrigation rates external RGB, 3D, multispectral, SPAD, fluorescence and morphology dataset A separate model for each measured endpoint Question Model Reason Reference value Does the current leaf image match a declared visual class? MobileNetV2 + 256-unit MLP compact image encoder; class probabilities can be calibrated and reviewed expert or published class label What fresh biomass is expected three days from now? ridge autoregression uses repeated mass and recent growth increments while regularising a small dataset measured fresh biomass [R57] How did cultivar and nutrient solution change tissue chemistry? factorial ANOVA tests cultivar, treatment and their interaction directly laboratory nitrogen, sulphate, organic acid and chlorophyll measurements [R59] Is the plant departing from its expected trajectory? forecast residual plus consecutive-capture rule requires persistence through time and retains the sensor and image context next measured observation Stored model output Every model output stores the image hash, plant ID, model version, preprocessing version, probability for every class, calibrated confidence and image-quality score. A low-quality or low-confidence image is marked for review together with the reason. The original image and complete class-probability vector remain available beside the final label.

Visual records:
- Indexed camera geometry (Mermaid-compatible optical-geometry diagram). Sources: I02, R52. Mermaid source: https://flavorotor.com/research/data/diagrams/ai-camera-geometry.mmd.
- PlantVillage strawberry images used in the reproducible example (interactive source-image gallery). Sources: R30. Data manifest: https://flavorotor.com/research/datasets/plantvillage-strawberry/manifest.json.
- Strawberry subset and leaf-group coverage (interactive bar chart). Sources: R30. Exact data: https://flavorotor.com/research/datasets/plantvillage-strawberry/class-distribution.csv.
- Image capture, model and review (Mermaid-compatible machine-learning pipeline). Sources: R30, R31, R51, R53, R55, R56. Mermaid source: https://flavorotor.com/research/data/diagrams/ai-camera-pipeline.mmd.
- Deterministic augmentation examples (Python-generated image augmentation gallery). Sources: R30, R54. Data manifest: https://flavorotor.com/research/datasets/plantvillage-strawberry/manifest.json.
- Training and validation accuracy by epoch (interactive training-history chart). Sources: R30, R31, R53. Exact data: https://flavorotor.com/research/datasets/plantvillage-strawberry/training-history.csv. Static image: https://flavorotor.com/research/datasets/plantvillage-strawberry/training-accuracy.webp. Scalable figure: https://flavorotor.com/research/datasets/plantvillage-strawberry/training-accuracy.svg.
- Controlled Strawberry test-set classifications (interactive confusion matrix). Sources: R30, R31. Exact data: https://flavorotor.com/research/datasets/plantvillage-strawberry/confusion-matrix.csv. Run summary: https://flavorotor.com/research/datasets/plantvillage-strawberry/training-summary.json. Static image: https://flavorotor.com/research/datasets/plantvillage-strawberry/confusion-matrix.webp. Scalable figure: https://flavorotor.com/research/datasets/plantvillage-strawberry/confusion-matrix.svg.
- Natural-background Strawberry images from PlantDoc (interactive source-image and prediction gallery). Sources: R51. Data manifest: https://flavorotor.com/research/datasets/plantdoc-strawberry/summary.json.
- PlantDoc healthy-class predictions (interactive prediction-count chart). Sources: R51. Exact data: https://flavorotor.com/research/datasets/plantdoc-strawberry/predictions.csv. Run summary: https://flavorotor.com/research/datasets/plantdoc-strawberry/summary.json. Static image: https://flavorotor.com/research/datasets/plantdoc-strawberry/healthy-class-predictions.webp. Scalable figure: https://flavorotor.com/research/datasets/plantdoc-strawberry/healthy-class-predictions.svg.
- Performance estimates with exact 95% confidence intervals (interactive exact confidence-interval plot). Sources: R30, R51, R55. Exact data: https://flavorotor.com/research/datasets/plantvision-performance/performance-intervals.csv.
- Image encoder comparison (interactive encoder performance and compute table). Sources: R30, R31, R51, R53. Exact data: https://flavorotor.com/research/datasets/plantvision-performance/backbone-benchmark/model-comparison.csv. Run summary: https://flavorotor.com/research/datasets/plantvision-performance/backbone-benchmark/summary.json.
- Five-fold physical-leaf cross-validation (interactive five-fold physical-leaf validation chart). Sources: R30, R31, R51. Exact data: https://flavorotor.com/research/datasets/plantvision-performance/grouped-cv/fold-results.csv. Run summary: https://flavorotor.com/research/datasets/plantvision-performance/grouped-cv/summary.json.

References: I02, R30, R31, R51, R52, R53, R54, R55, R56, R57, R59, R61, R62

## Industrial design and serviceability

- Canonical URL: https://flavorotor.com/research/industrial-design-serviceability
- Document ID: TR-IND-001
- Group: Experimental platform
- Version: 1.0
- Updated: 2026-07-27
- Markdown: https://flavorotor.com/research/markdown/industrial-design-serviceability
- JSON: https://flavorotor.com/research/data/chapters/industrial-design-serviceability.json

How enclosure geometry, access, cleaning and maintenance are separated from biological performance claims.

In brief The enclosure is treated as an engineering system that must preserve access, cleaning, observation and safe operation around the rotating cultivation drum. Scope Industrial design is included in the research documentation because enclosure decisions can alter access, airflow, light distribution, camera visibility, contamination risk and mechanical balance. Aesthetic intent is recorded separately from measured cultivation performance. Layered architecture The v2.0 concept separates a stationary external frame from the rotating cultivation layer. This allows plant movement to remain visible while fixed guards, service panels, lighting and cable routes remain referenced to the structure. The original prototype and v2.0 reports provide the primary engineering record. [I01] [I02] Serviceability requirements Area Requirement Verification output Plant modules individual removal without disturbing unrelated samples tool list, access sequence and measured service time Nutrient reservoir inspection, draining and cleaning without wetting electronics drain test and cleaning record Pump tubing replacement with channel identity preserved replacement procedure and post-service calibration check Lighting and camera fixed optical reference after service position check and image/light revalidation Rotating assembly guard clearance under maximum declared load clearance and interference inspection Materials compatibility with moisture, nutrient solution and cleaning method material record and inspection interval Verification plan Verification includes assembly and disassembly trials, loaded rotation, splash observation, cable-clearance inspection, access-time measurement, cleaning inspection and confirmation that service work does not invalidate sensor, pump, camera or light calibration. Deviations are recorded as maintenance events in the experiment log.

References: I01, I02

## Peristaltic pump development

- Canonical URL: https://flavorotor.com/research/peristaltic-pump
- Document ID: TR-PMP-001
- Group: Nutrient dosing
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/peristaltic-pump
- JSON: https://flavorotor.com/research/data/chapters/peristaltic-pump.json

The custom three-roller pump, its first-order displacement model and the strict distinction between motor command resolution and delivered-volume accuracy.

In brief The custom three-roller pump, its first-order displacement model and the strict distinction between motor command resolution and delivered-volume accuracy. Custom pump architecture. Original CAD from the supplied pump package. Geometry supports the first-order model; delivery performance requires gravimetric calibration. [I03] Explanation A three-roller rotor compresses a flexible tube. Advancing the compression moves liquid while the liquid remains inside the replaceable tube. The motor provides a precise command, but only calibration determines the volume that actually exits the tube. Documented design Parameter v2 design value Classification Pump type three-roller peristaltic design architecture Tube 3.2 mm ID / 6.4 mm OD silicone design specification Nominal channel radius 18 mm CAD specification Drive NEMA 17, 1.8° full step, direct drive component specification Command mode 1/16 microstepping firmware design Housing PETG prototype geometry CAD specification System channels four independent pump modules system design First-order model P-1 A t = πd i ² / 4 Nominal undeformed internal tube area. P-2 V rev,ideal = A t · L eff · N e Ideal displacement per rotor revolution using an effective displaced length Leff and displacement-event count Ne. With d i = 3.2 mm, L eff = 25 mm and N e = 3, the report model gives A t ≈ 8.04 mm² and V rev,ideal ≈ 0.603 mL/rev. P-3 V rev,meas = η v · V rev,ideal Measured displacement represented by a fitted volumetric-efficiency term. ηv may depend on speed, pressure, tube and age. P-4 Q = V rev,meas · n Mean flow at rotor speed n in rev/min. Motor-command increment P-5 N µstep/rev = (360° / 1.8°) · 16 = 3200 Microstep commands per direct-drive rotor revolution. P-6 ΔV cmd,nom = 0.603 mL / 3200 ≈ 0.188 µL/command Nominal geometric displacement assigned to one command. Engineering note 0.188 µL per microstep is not accuracy, repeatability, minimum dose or experimentally resolved liquid volume. Why calibration is mandatory Tube recovery, occlusion, viscosity, suction head, outlet pressure, roller geometry, motor torque, microstep non-linearity and tube wear all alter delivered volume. Peristaltic-pump modelling and published multi-channel systems therefore use physical calibration rather than geometry alone. [R18] [R19] Progress classification The pump geometry and four-channel module are substantial v2 engineering progress. The supplied report documents CAD, component selection and the analytical model. No traceable FlavoRotor gravimetric dataset accompanies the report, so delivered-volume performance remains unclaimed until CR-PMP-001 is published. FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I02] [I03] CAD documentation Roller and tube path. Internal CAD record. [I03] Exploded pump assembly. Internal CAD record showing serviceable components. [I03]

References: I02, I03, R18, R19

## Gravimetric pump calibration

- Canonical URL: https://flavorotor.com/research/pump-calibration
- Document ID: PR-PMP-001
- Group: Nutrient dosing
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/pump-calibration
- JSON: https://flavorotor.com/research/data/chapters/pump-calibration.json

A complete channel-specific procedure for converting motor commands into measured liquid volume with uncertainty and drift.

In brief A complete channel-specific procedure for converting motor commands into measured liquid volume with uncertainty and drift. Principle Each pump dispenses into a vessel on a calibrated balance. Mass gain is converted to volume using fluid density at the measured temperature. The test is repeated across channel, dose, speed, tube condition and hydraulic head. C-1 V i = (m after,i − m before,i ) / ρ(T) Delivered volume for repetition i. C-2 Q i = V i / Δt i Mean flow for repetition i. The gravimetric chain follows traceable liquid-volume and uncertainty principles: balance performance, test-liquid density, evaporation, timing, repeatability and calibration state are recorded. Large published peristaltic-pump datasets show why repeated measurements and drift analysis are necessary, but their performance values are not transferred to FlavoRotor. [R37] [R38] [R47] Test matrix Factor Levels Channel 1, 2, 3, 4 Rotor speed 5, 15, 30 and 60 rev/min Commanded dose 0.5, 1, 2, 5 and 10 mL Repetitions minimum 20 per primary condition Fluid deionised water and each representative stock class Tube state new, mid-life and replacement threshold Hydraulic condition minimum, nominal and maximum inlet head; installed outlet path Direction forward; reverse purge characterised separately Calibration statistics C-3 V̄ = (1/N) ΣV i Mean delivered volume. C-4 bias = V̄ − V set Absolute systematic error at a test point. C-5 CV = 100 · s / V̄ Coefficient of variation for repeatability. C-6 RMSE = √[(1/N)Σ(V i − V set )²] Combined deviation from the requested volume. Repeatability, bias, residual analysis and method precision are reported using declared statistical procedures rather than a single R² value. [R46] [R50] Channel model C-7 V̂ j = a j N cmd + b j First candidate model for channel j; residuals determine whether speed, pressure or nonlinear terms are required. Predefined engineering acceptance gates Metric Gate for initial reservoir dosing Relative bias ≤ ±3% for doses ≥1 mL within the declared range Repeatability CV ≤2% for doses ≥1 mL Channel model residual structure absent and R² reported, not used alone Drift ≤5% before recalibration or tube replacement Cross-channel contamination none detected above method limit Backflow/siphon no uncontrolled transfer in the installed hydraulic range These are FlavoRotor acceptance criteria, not claimed achieved performance. Published multi-channel pump data guide the method but are not copied as FlavoRotor results. [R19] FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I03]

References: I03, R19, R37, R38, R46, R47, R50

## Four-channel nutrient dosing module

- Canonical URL: https://flavorotor.com/research/four-channel-dosing
- Document ID: TR-DOS-001
- Group: Nutrient dosing
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/four-channel-dosing
- JSON: https://flavorotor.com/research/data/chapters/four-channel-dosing.json

Independent fluid channels, channel identity, mixing logic and the separation between dosing a stock solution and producing a plant sensory outcome.

In brief Independent fluid channels, channel identity, mixing logic and the separation between dosing a stock solution and producing a plant sensory outcome. System integration. Original pump-package CAD. [I03] Four independent fluid channels. Original pump-package CAD. Channel assignment is recipe-defined and requires independent calibration. [I03] Explanation Four pumps allow four liquids to be added independently. The channels control liquid volumes. They do not directly control sweetness, acidity or aroma. Architecture The documented motor-control architecture uses STEP/DIR microstepping drivers of the A4988 class. Driver selection defines command generation and protection requirements; it does not determine pump volumetric accuracy. [R44] Stock 1–4 → Calibrated channel → Injection point → Mixing delay → Reservoir measurement Safe channel definition Channel Permitted role Required metadata 1 water or defined stock fluid ID, batch and density 2 nutrient stock A full chemical composition and compatibility class 3 nutrient stock B full chemical composition and compatibility class 4 correction or experimental stock purpose, maximum dose and exclusion rules Measurement rule A channel labelled K, N or Ca still changes multiple chemical and physiological variables. Any sensory outcome is established only by a controlled crop trial. Dose traceability event_id, timestamp, system_id, recipe_id, recipe_version, channel_id, fluid_id, fluid_batch, calibration_id, requested_volume_mL, commanded_steps, speed_rpm, direction, reservoir_volume_before_L, pH_before, EC_before, temperature_before, mixing_delay_s, pH_after, EC_after, fault_state FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I02] [I03] Module interior Module interior. Original internal CAD image. It documents channel packaging; delivered volume and cross-channel isolation remain validation items. [I03]

References: I02, I03, R44

## Nutrient stock solutions

- Canonical URL: https://flavorotor.com/research/stock-solutions
- Document ID: MTH-STK-001
- Group: Nutrient dosing
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/stock-solutions
- JSON: https://flavorotor.com/research/data/chapters/stock-solutions.json

How stock composition, compatibility, dose volume and reservoir volume define nutrient additions without inventing ion-specific EC values.

In brief How stock composition, compatibility, dose volume and reservoir volume define nutrient additions without inventing ion-specific EC values. Principle Each stock solution is defined by the concentration of every relevant chemical species, not by a marketing label. The controller calculates the amount added to the reservoir from calibrated dose volume and stock composition. STK-1 Δc i = S ij · v j / V R Increase in reservoir concentration of ion i from stock j, where Sij is stock concentration, vj is dose volume and VR is reservoir volume. STK-2 c i,k+1 = c i,k + ΣΔc i − u i,k − l i,k Ion inventory update including additions, plant uptake ui and other losses li. Stock compatibility Concentrated calcium stocks are separated from concentrated phosphate or sulphate stocks unless compatibility has been demonstrated, because precipitation can remove nutrients and obstruct tubing. Stock identity, concentration, solvent, preparation date, lot and storage conditions are recorded. Recipe solving When several stocks contribute to several ions, the system solves a constrained non-negative dosing problem rather than assigning one pump to one sensory attribute. STK-3 min ||S·v − Δc target ||² subject to v ≥ 0 and v ≤ v max Constrained stock-volume selection for a target ion-change vector. Control rule EC is used as a bulk consistency and safety check. It cannot verify the individual ion vector S·v. Ion coupling Stock design accounts for the fact that fertilizer salts introduce coupled ions and that precise individual-ion control requires more information than bulk EC. [R34] [R35] [R36]

References: R34, R35, R36

## Nutrient dosing control strategy

- Canonical URL: https://flavorotor.com/research/dosing-control
- Document ID: TR-CTL-001
- Group: Nutrient dosing
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/dosing-control
- JSON: https://flavorotor.com/research/data/chapters/dosing-control.json

Supervisory mass balance, pH/EC feedback, mixing delays, anti-windup and safety constraints.

In brief Supervisory mass balance, pH/EC feedback, mixing delays, anti-windup and safety constraints. Control hierarchy Recipe targets → Stock mass balance → Calibrated pump commands → Mixing → pH/EC/level observation Variables suitable for direct feedback reservoir level or working volume; bulk EC, with temperature compensation; pH, with calibrated electrode and mixing delay; solution temperature; dissolved oxygen if a validated sensor is installed. Sweetness, acidity of harvested tissue and aroma are not direct loop variables because they are not measured continuously in the reservoir. Discrete PI form CTL-1 e(k) = y target − y measured (k) Error for a directly measured variable y, such as EC or pH. CTL-2 u(k) = K p e(k) + K i Σe(j)Δt Candidate PI output before safety and chemical constraints. CTL-3 u safe = clip(u, u min , u max ) Dose request limited by recipe, chemistry and hardware constraints. Dose sequence Validate sensor state and reservoir volume. Calculate a bounded stock-volume request. Verify channel calibration and stock identity. Deliver dose and log actuator command. Wait the measured mixing time. Acquire stable pH/EC readings. Apply another correction only if all limits remain valid. pH control note Because pH is logarithmic and buffering varies with solution composition, the controller uses small empirical dose increments and measured response rather than converting pH error directly into a fixed acid/base volume. Open-source pH-stat work supports this calibration and logging approach. [R20]

References: R20

## Fluidic safety and maintenance

- Canonical URL: https://flavorotor.com/research/fluidic-safety
- Document ID: PR-FLD-001
- Group: Nutrient dosing
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/fluidic-safety
- JSON: https://flavorotor.com/research/data/chapters/fluidic-safety.json

Interlocks, priming, purge, leak control, stock identification, tube replacement and cleaning for the dosing subsystem.

In brief Interlocks, priming, purge, leak control, stock identification, tube replacement and cleaning for the dosing subsystem. Mandatory interlocks Condition Automatic response Invalid or stale sensor block feedback correction Reservoir below minimum level block concentrated stock dosing Reservoir above maximum level block water addition Calibration expired block volumetric automatic dosing Maximum dose/runtime exceeded stop channel and latch fault Mixing delay active block second feedback action Stock mismatch reject recipe execution Cover/service state unsafe disable pump motion where required Communication loss outputs return to defined safe state Leak detected stop all liquid actuators and alert Tube lifecycle Tube service life is not published as a fixed 1,000-hour value unless validated for the exact tube, occlusion, fluid, speed and duty cycle. Replacement is triggered by calibrated-flow drift, visible damage, loss of occlusion, contamination risk or the validated service threshold. Cleaning record cleaning_id, system_id, channel_id, cleaning_agent, concentration, contact_time, rinse_volume, verification_method, operator, timestamp, next_allowed_fluid_class FlavoRotor design provenance The system-specific configuration on this page is traced to the supplied FlavoRotor engineering records. [I03]

References: I03

## What flavour means

- Canonical URL: https://flavorotor.com/research/flavor-definition
- Document ID: FLV-DEF-001
- Group: Flavour control
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/flavor-definition
- JSON: https://flavorotor.com/research/data/chapters/flavor-definition.json

A measurement model separating taste, aroma, trigeminal sensation, texture, appearance and consumer liking.

In brief A measurement model separating taste, aroma, trigeminal sensation, texture, appearance and consumer liking. Explanation Flavour is not one sensor value. It is the combined experience produced by taste, retronasal aroma, texture, temperature, trigeminal sensations and context. FlavoRotor outcome model Outcome Example measurements What it cannot prove alone Taste sweet, sour, bitter, salty, umami intensity volatile aroma composition Aroma descriptor profile, GC–MS volatile abundance consumer preference Texture firmness, fracture, fibrousness, juiciness taste identity Appearance instrumental colour, morphology, visible defects flavour quality Difference triangle or other discrimination test direction or preference Liking consumer hedonic score chemical cause Discrimination, descriptive profiling and consumer liking answer different questions and are documented separately. [R25] [R29] [R40] [R41] [R42] [R48] [R49] Core rule Engineering note A chemical change is not automatically a sensory change, and a sensory difference is not automatically an improvement. Measurement map Term What is measured Suitable method Taste sweet, sour, bitter, salty and umami sensations trained descriptive panel or defined consumer method Aroma orthonasal and retronasal odour attributes descriptive sensory analysis; VOC analysis as complementary evidence Flavour integrated taste, aroma and trigeminal perception sensory method selected for the claim Texture firmness, crispness, fibrousness and juiciness instrumental texture plus sensory description Preference degree of liking consumer hedonic test; never inferred from chemistry alone Terminology and method selection follow sensory-analysis standards. A chemical difference can help explain perception, but it does not replace a sensory test. [R29] [R41] [R42] [R48] A measurable sensory fingerprint A target is stored as a versioned set of measurements for one crop and cultivar at a defined harvest stage. It combines a small number of primary chemical and sensory variables with physical plant state and the complete cultivation history. Layer Examples of recorded variables Method Chemistry selected volatile compounds, sugars, organic acids, pigments validated chromatographic or spectrometric method Sensory sweet, sour, bitter, named aromas, texture, trigeminal sensations coded and blinded sensory protocol Physical state developmental stage, colour, fresh and dry mass, water content calibrated imaging and physical measurements Process history light, temperature, humidity, nutrient, pH, EC, rotation and harvest history timestamped sensor and actuator records

References: R25, R29, R40, R41, R42, R48, R49

## How cultivation steers flavour

- Canonical URL: https://flavorotor.com/research/flavor-control-chain
- Document ID: FLV-CTL-001
- Group: Flavour control
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/flavor-control-chain
- JSON: https://flavorotor.com/research/data/chapters/flavor-control-chain.json

How measured cultivation inputs are linked to plant chemistry and sensory response, then converted into a reproducible crop recipe.

In brief The complete engineering and biological chain used to turn a desired sensory target into a reproducible cultivation recipe. Definition of control FlavoRotor defines a reproducible flavour result when a requested target can be translated into a versioned cultivation recipe that produces a statistically and sensorially bounded outcome across independent cycles. Target sensory profile → Crop and cultivar → Measured input recipe → Calibrated execution → Chemical and sensory result → Replication Controllable inputs The platform can programme light, nutrient-stock additions, pH management, bulk EC limits, solution temperature and rotation schedule; the supplied engineering reports also document the rotating drum, sensing, monitoring and proposed four-channel dosing architecture. [I01] [I02] [I03] Measured outcomes Outcomes are crop-specific: basil may be evaluated through selected aroma volatiles and descriptive aroma; arugula through glucosinolate-related phytochemicals, pungency and bitterness; lettuce through bitterness, texture and quality; mint through essential-oil composition and menthol-related descriptors; strawberry through soluble solids, titratable acidity, volatile profile, firmness and sensory response. [R01] [R02] [R05] [R10] [R12] [R15] [R28] Recipe model FLV-1 ŷ = f(x, g, s, t) + ε ŷ is a predicted outcome; x is the measured cultivation vector; g is genotype; s is system state; t is developmental stage; ε is unexplained variation. The model is trained only after single-factor and interaction experiments. It is never seeded with invented nutrient-to-flavour coefficients. Recipe release gate A recipe is released only when the machine input was calibrated, the protocol was frozen before analysis, the result includes uncertainty and effect size, sensory evidence is appropriate to the claim, and at least one independent replication succeeds. From influence to repeatable targeting Measure influence. Change one calibrated input and measure the chemical and sensory response against a matched control. Map the response. Repeat across treatment levels and independent cycles to estimate direction, magnitude and interaction with cultivar and growth stage. Define a target. Freeze the chemical, sensory and physical acceptance ranges before a new cultivation run begins. Test prospectively. Run the frozen recipe on new biological material and compare the harvest with the predefined target. Replicate. Repeat on another cycle, unit and operator with the same physical targets and calibrated local commands.

References: I01, I02, I03, R01, R02, R05, R10, R12, R15, R28

## pH management

- Canonical URL: https://flavorotor.com/research/ph-management
- Document ID: MTH-PH-001
- Group: Flavour control
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/ph-management
- JSON: https://flavorotor.com/research/data/chapters/ph-management.json

What nutrient-solution pH changes, what it does not change directly, and how crop-specific pH experiments are designed.

In brief What nutrient-solution pH changes, what it does not change directly, and how crop-specific pH experiments are designed. Explanation pH changes the chemical environment around the roots. It affects nutrient speciation, solubility, microbial conditions and uptake. It is not a direct “sweetness dial”. PH-1 pH = −log₁₀(a H+ ) Definition in terms of hydrogen-ion activity. Published crop studies Lettuce studies show that relatively small pH changes can alter physiological performance and tissue composition. [R06] [R07] These studies justify a pH trial but do not demonstrate a universal taste setting. Operational approach Step Requirement Select setpoint crop, cultivar, formulation and literature anchor stated Calibrate buffers bracket expected operating range Measure temperature and stabilisation criteria recorded Correct small bounded acid/base increments Mix wait validated mixing time Re-read require stable repeated measurements Publish report actual pH distribution, not only nominal target How to test pH and flavour correctly Use at least three pH treatments while keeping elemental formulation, EC, DLI, temperature, harvest age and post-harvest handling matched. Measure tissue composition, biomass, chemistry and blinded sensory response. A difference in growth alone is not a flavour result. Measurement rule A published pH range is an operating or experimental condition. It is not evidence that a specific pH makes a plant sweeter, less bitter or more aromatic.

References: R06, R07

## EC and ionic balance

- Canonical URL: https://flavorotor.com/research/ec-ionic-balance
- Document ID: MTH-EC-001
- Group: Flavour control
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/ec-ionic-balance
- JSON: https://flavorotor.com/research/data/chapters/ec-ionic-balance.json

Temperature-corrected conductivity, individual-ion drift and the limits of EC-only nutrient control.

In brief Temperature-corrected conductivity, individual-ion drift and the limits of EC-only nutrient control. Explanation EC measures how well the solution conducts electricity. It is useful for detecting overall dilution or concentration, but it cannot state how much potassium, nitrate, calcium or magnesium is present individually. EC-1 EC₂₅ ≈ EC T / [1 + α(T − 25)] Approximate temperature correction to 25 °C; α must match the solution or instrument model. Ion drift Plant uptake is selective. A controller can hold bulk EC near target while individual ions diverge. This is directly documented in hydroponic nutrient-dynamics research. [R17] Required controls known initial elemental composition; logged stock additions and water additions; reservoir-volume accounting; periodic solution replacement or laboratory verification; crop-specific tissue or solution analysis for research claims; EC used as a bulk constraint, not an ion sensor. EC experiment rule An EC treatment is reproducible only when the recipe used to reach that EC is also specified. Raising EC with a balanced nutrient concentrate is biologically different from raising EC with NaCl or a single salt, even when the meter reads the same value.

References: R17

## Nutrient composition, plant chemistry and stock design

- Canonical URL: https://flavorotor.com/research/nutrient-composition
- Document ID: FLV-NUT-001
- Group: Flavour control
- Version: 2.0
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/nutrient-composition
- JSON: https://flavorotor.com/research/data/chapters/nutrient-composition.json

Elemental mass balance, real hydroponic N–P–K dose responses, cultivar-by-solution effects on lettuce chemistry and the measurements required to estimate plant nutrient state.

In brief How complete elemental recipes, coupled ions, stock compatibility and tissue measurements replace simplistic nutrient-to-taste rules. Explanation Adding one fertilizer changes every ion carried by that salt. A KNO₃ dose changes both potassium and nitrate; it is not a pure “sweetness” command. Mass balance NUT-1 nᵢ,new = nᵢ,old + Σⱼ νᵢⱼ Cⱼ Vⱼ − Uᵢ − Lᵢ Ion i changes through stock additions j, stoichiometric coefficients ν, plant uptake U and losses L. NUT-2 Cᵢ,new = nᵢ,new / Vreservoir,new Concentration follows ion amount and the final mixed reservoir volume. Separate N, P and K limitation The USDA Bibb lettuce dataset changes one target nutrient concentration at a time and reports fresh mass through day 32 after transplant. The day-32 response is not monotonic for every nutrient. Nitrogen rises from 1.16 g at 5 mg·L⁻¹ to 250.73 g at 132 mg·L⁻¹, then falls to 74.11 g at 264 mg·L⁻¹. Phosphorus rises from 9.14 g at 1 mg·L⁻¹ to 250.73 g at 31 mg·L⁻¹. The potassium series contains a wide interval at 42 mg·L⁻¹, so that treatment mean should not be read without its uncertainty. [R58] The graph preserves each nutrient's actual concentration scale and the authors' 95% confidence intervals. It demonstrates two practical points: nutrient response can be curved rather than linear, and equal EC values do not imply equal elemental availability. Nutrient solution and tissue chemistry El-Nakhel and colleagues tested green and red lettuce with calcium-, magnesium- or potassium-dominant macrocation ratios. The complete design contains three biological replicates in each of six cultivar-by-solution cells. The published measurements include total nitrogen, sulphate, six organic acids and total chlorophyll. [R59] A balanced two-way fixed-effects ANOVA was recomputed from all 18 published observations. For malate, the nutrient-solution effect was F(2, 12) = 106.39 with Holm-adjusted p = 3.94 × 10⁻⁷, and the cultivar-by-solution interaction was F(2, 12) = 21.40 with adjusted p = 9.93 × 10⁻⁴. For total chlorophyll, the interaction was F(2, 12) = 25.38 with adjusted p = 4.89 × 10⁻⁴. The interaction means the solution effect changes with cultivar; one universal nutrient-to-chemistry coefficient would discard that structure. Estimating plant nutrient state Visible colour is useful but not chemically specific. Nitrogen limitation, water stress, senescence, exposure error and disease can all alter RGB appearance. FlavoRotor therefore joins four records at the same plant and time: the delivered elemental formulation, pH and EC history, the repeated image, and a reference measurement such as tissue mineral composition or chlorophyll. A supervised model predicts a declared laboratory endpoint, not an undefined label such as “nutrient health”. NUT-STATE x̂ₜ = f(Iₜ₋ₖ:ₜ, uₜ₋ₖ:ₜ, sₜ₋ₖ:ₜ, g, d) Estimated plant state uses an image sequence I, delivered nutrient and light inputs u, measured environmental state s, cultivar g and day after transplant d over a defined history window. Evaluation keeps all observations from one plant or cultivation cycle in the same fold. The report includes MAE for continuous chemistry, balanced accuracy for declared deficiency classes, calibration of uncertainty and performance for each cultivar and growth stage. Why EC is insufficient EC is an indirect bulk response to all dissolved ions. Closed systems can maintain a target EC while individual nutrients become deficient or excessive. [R17] [R34] [R35] [R36] Four-channel implication The four FlavoRotor channels must be assigned to chemically defined and compatible fluids. Channel labels describe the liquid, not an expected flavour. The formulation must account for coupled ions, precipitation risk, source-water composition and the limited degrees of freedom available with four reservoirs. [I03] [R34] [R35] Validation Recipe trials report the full elemental formulation, source water, pH, EC, solution replacement, delivered stock volumes and tissue composition. Sensory conclusions are made only after chemical and blinded sensory measurements.

Visual records:
- Fresh mass under separate nitrogen, phosphorus and potassium limitation (interactive three-panel dose-response chart). Sources: R58. Exact data: https://flavorotor.com/research/datasets/lettuce-npk-limitation/fresh-mass-dose-response.csv.
- Nutrient-solution composition altered measured lettuce chemistry (interactive chemistry heatmap with original-value inspector). Sources: R59. Exact data: https://flavorotor.com/research/datasets/lettuce-macrocations/cell-summary.csv.

References: I03, R17, R34, R35, R36, R58, R59

## Light as a flavour-control variable

- Canonical URL: https://flavorotor.com/research/lighting-flavor
- Document ID: FLV-LGT-001
- Group: Flavour control
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/lighting-flavor
- JSON: https://flavorotor.com/research/data/chapters/lighting-flavor.json

How spectrum, PPFD, DLI, photoperiod and developmental timing are separated and tested as crop-specific sensory inputs.

In brief How spectrum, PPFD, DLI, photoperiod and developmental timing are separated and tested as crop-specific sensory inputs. Light variables Variable Required record Spectrum measured spectral photon distribution at plant positions PPFD instantaneous photon flux density and spatial map DLI integrated daily photons Photoperiod on/off schedule and transitions Far-red separate 700–750 nm photon record Timing developmental stage and pre-harvest treatment duration Hydroponic Italian Large Leaf basil provides direct evidence that lighting quality can alter key aroma volatiles. [R01] Far-red photons can contribute to canopy photosynthesis when combined with shorter wavelengths, so FlavoRotor records them rather than reducing the light description to “red/blue percentages”. [R33] Experimental design To test a spectral effect, DLI, temperature, cultivar, nutrient formulation, plant age and harvest handling remain matched. A treatment is reported as measured photon distributions, not LED control percentages. Required treatment definition A light treatment is fully specified only when spectrum, PPFD, DLI, photoperiod, fixture geometry, plant position, leaf temperature and treatment timing are recorded. Percent dimmer settings are device commands, not transferable biological units. Variable Primary measurement Potential response Spectrum spectral photon distribution morphology, volatile and secondary-metabolite profile PPFD µmol·m⁻²·s⁻¹ at plant positions instantaneous photon exposure DLI mol·m⁻²·d⁻¹ daily integrated exposure Photoperiod hours per day and schedule development and circadian response Leaf temperature contact or calibrated infrared measurement separates optical and thermal effects

References: R01, R33

## Root-zone environment

- Canonical URL: https://flavorotor.com/research/root-zone-environment
- Document ID: FLV-RTZ-001
- Group: Flavour control
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/root-zone-environment
- JSON: https://flavorotor.com/research/data/chapters/root-zone-environment.json

Solution temperature, oxygen, immersion, mixing, root architecture and reservoir age as explicit experimental variables.

In brief Solution temperature, oxygen, immersion, mixing, root architecture and reservoir age as explicit experimental variables. Scope Root-zone behaviour is defined by more than pH and EC. FlavoRotor records solution temperature, dissolved oxygen when available, immersion timing, drainage, mixing time, reservoir volume, solution age and root architecture. Temperature Root-zone temperature can alter growth and soluble-solids measurements, with cultivar-dependent responses reported in lettuce. [R11] Reservoir age and composition Recycled solution can accumulate unwanted or slowly consumed ions while bulk EC remains near target. [R36] Sequential immersion The rotating geometry introduces periodic root wetting and drainage documented in the FlavoRotor system records. This is treated as an experimental factor and potential confounder rather than assumed to improve oxygenation or flavour. [I01] [I02] Required measurements solution temperature at defined locations and intervals; reservoir volume and replacement events; mixing-time validation after each dose; root-zone exposure duration per revolution; root images and root dry mass; water and nutrient balance. Coupled variables Root-zone temperature changes oxygen solubility and root metabolism; pH changes nutrient speciation and availability; EC describes bulk conductivity but not individual ions; flow and immersion determine renewal around the root surface. These variables are therefore logged together and are not interpreted independently when they covary. Variable Control purpose Failure mode to detect Solution temperature stable root-zone condition heating, cooling or spatial gradients Dissolved oxygen root respiration support low oxygen after warming or biological load pH defined root-zone chemistry drift, probe fouling or dosing overshoot EC bulk concentration guardrail dilution, concentration or ionic imbalance hidden by total EC Immersion and drainage repeatable wetting cycle unequal contact, retained liquid or blocked drainage

References: I01, I02, R11, R36

## Salinity, water stress and multimodal detection

- Canonical URL: https://flavorotor.com/research/salinity-water-stress
- Document ID: FLV-STR-001
- Group: Flavour control
- Version: 2.0
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/salinity-water-stress
- JSON: https://flavorotor.com/research/data/chapters/salinity-water-stress.json

Biomass and concentration effects, a published 145-hour lettuce water-stress sequence, aligned image signals and a measurement protocol that separates water, nutrient and disease endpoints.

In brief How concentration effects, osmotic stress, biomass penalties and metabolite responses are separated before any flavour claim. Explanation A treatment can increase the concentration of a compound per gram while reducing total plant growth. Both outcomes must be reported. Response model STR-1 Total compound per plant = concentration per dry mass × plant dry mass Prevents a concentration increase caused only by reduced biomass from being reported as higher total production. Mint species show species-dependent essential-oil and antioxidant responses under salinity, accompanied by growth effects. [R15] Arugula EC trials report simultaneous changes in growth, nutritional quality and flavour-related phytochemicals, demonstrating why yield and chemistry must be analysed together. [R05] Measured water-stress sequence Fevgas and colleagues published 145 hourly soil-moisture records with RGB, thermal and pseudo-colour lettuce images. The normally irrigated sequence starts at 75% and ends at 73%. The non-irrigated sequence starts at 75% and ends at 8%. A difference of at least 10 percentage points persists from 14 January 2024 at 09:24:45 (UTC+2). [R60] The lower panel applies one fixed pixel rule to the authors' pseudo-colour outputs: R > 180, G > 180, B < 130 and R + G > 420. Yellow overlay occupies 35.13–52.28% of the detected canopy in the 13 non-irrigated outputs and 0.02–4.11% in the irrigated outputs. This is a measurement of the published visualisation, not a newly trained disease or stress classifier. Separating water, nutrient and disease signals A colour change is not assigned a cause from RGB alone. Water stress is checked against reservoir level, root-zone contact, temperature and moisture or water-potential measurements. Nutrient state is checked against the delivered formulation, pH, EC and tissue analysis. Disease labels require symptom-specific expert or laboratory confirmation. The same image can contribute features to each analysis, but each endpoint has its own reference measurement. Time to detection For a new cultivation run, detection time is measured from the recorded treatment change to the first alert that remains above threshold for a declared number of consecutive captures. The report includes false-alert rate in control plants, sensitivity, median detection delay and an interval across biological replicates. This distinguishes early detection from a visually strong endpoint image. FlavoRotor rule No salinity or water-stress recipe is released without biomass, tissue water, visual quality, chemical endpoints and sensory confirmation. Severe stress is not used merely to create a larger analytical signal.

Visual records:
- Water availability and image-derived stress signals change on different scales (interactive aligned sensor and image-derived time-series chart). Sources: R60. Data manifest: https://flavorotor.com/research/datasets/lettuce-water-stress/chart-data.json.
- Published pseudo-colour outputs for irrigated and non-irrigated lettuce (interactive published pseudo-colour image gallery). Sources: R60. Data manifest: https://flavorotor.com/research/datasets/lettuce-water-stress/summary.json.

References: R05, R15, R60

## Harvest and post-harvest control

- Canonical URL: https://flavorotor.com/research/harvest-postharvest
- Document ID: FLV-HRV-001
- Group: Flavour control
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/harvest-postharvest
- JSON: https://flavorotor.com/research/data/chapters/harvest-postharvest.json

Harvest age, time of day, sample location, storage and preparation rules required to preserve a valid cultivation comparison.

In brief Harvest age, time of day, sample location, storage and preparation rules required to preserve a valid cultivation comparison. Why this matters A cultivation treatment can be overwhelmed by differences introduced during harvest, storage or sample preparation. Post-harvest conditions are therefore controlled as part of the experiment rather than treated as logistics. Lettuce quality and shelf-life responses depend on nutrient treatment, season and post-harvest handling. [R26] Required harvest record Field Requirement Developmental age days after sowing and transplanting Time of harvest clock time and light-cycle position Sample location defined leaf, fruit or canopy position Pre-analysis delay minutes or hours Storage temperature, humidity, package and duration Preparation washing, cutting, mass and serving temperature Sensory samples Samples receive blind random codes, balanced serving order and identical preparation. Test-room conditions and the selected discrimination, descriptive or hedonic method are documented. [R40] [R41] [R42] [R48] [R49]

References: R26, R40, R41, R42, R48, R49

## Recipe and control algorithm

- Canonical URL: https://flavorotor.com/research/recipe-control-algorithm
- Document ID: FLV-ALG-001
- Group: Flavour control
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/recipe-control-algorithm
- JSON: https://flavorotor.com/research/data/chapters/recipe-control-algorithm.json

A safe hierarchy separating user targets, recipe parameters, measured feedback, actuator calibration and learned sensory models.

In brief A safe hierarchy separating user targets, recipe parameters, measured feedback, actuator calibration and learned sensory models. Control hierarchy User target → Validated recipe ID → Setpoints and schedules → Calibrated actuators → Measured environment → Outcome model Direct feedback loops Closed-loop control is appropriate for directly measured variables such as pH, reservoir level, solution temperature, rotation speed and bulk EC. Individual-ion control requires ion-specific measurement or a constrained mass-balance model validated by chemical analysis. [R17] [R34] [R35] [R36] RCP-1 e(k) = ytarget − ymeasured(k) Error for a directly measured controlled variable. RCP-2 u(k) = clip[Kp e(k) + Ki Σ e(j)Δt, umin, umax] Bounded PI action with explicit actuator and safety limits. Learned sensory mapping The sensory predictor is trained from completed FlavoRotor experiments. Its input data include genotype, developmental stage, measured environmental history, solution composition and harvest handling. Cross-validation is separated by cultivation cycle to prevent samples from the same run appearing in training and test sets. Safety constraints no automatic dose with an expired channel calibration; no correction while the mixing delay is active; bounded dose and runtime per event; sensor plausibility and redundancy checks; fault-safe state after communication loss; full event logging with recipe and firmware versions. State-conditioned treatment A recipe contains time limits and plant-state conditions. For example, a light phase can begin when a validated imaging model detects the required developmental stage, provided plant-health checks pass and the minimum and maximum calendar limits are respected. RCP-3 x(t+1) = f(x(t), u(t), d(t), g, θ) + w(t) x(t) is plant state; u(t) contains controlled inputs; d(t) contains measured disturbances; g identifies genotype; θ contains model parameters; and w(t) represents process variation. RCP-4 y(t) = h(x(t)) + v(t) The camera, sensors and laboratory measurements observe only part of the plant state; v(t) represents measurement error.

References: R17, R34, R35, R36

## Rotation, gravitropism and mechanical exposure

- Canonical URL: https://flavorotor.com/research/rotation-gravity
- Document ID: RSP-ROT-001
- Group: Rotation and gravity
- Version: 2.0
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/rotation-gravity
- JSON: https://flavorotor.com/research/data/chapters/rotation-gravity.json

Horizontal-axis rotation expressed as an angle-time history, its relation to plant gravity sensing, measured mechanical acceleration, root-zone coupling and controlled biological comparisons.

Rotation in the plant frame The FlavoRotor drum turns around a horizontal axis. Gravity remains vertical and close to 9.81 m·s⁻², while each plant module changes orientation relative to that vector. Rotation therefore produces a periodic directional stimulus, not reduced gravity. The encoder record links angle, angular speed and direction to every image and sensor sample. [I01] [I02] [R22] [R32] ROT-1 ω = 2πn / 60 Angular velocity ω in rad·s⁻¹ from drum speed n in rev·min⁻¹. ROT-3 θ(t) = θ₀ + ωt The plant-module angle follows the encoder angle θ₀ and measured angular velocity. ROT-4 T = 2π / ω = 60 / n One complete orientation cycle lasts 120 s at 0.5 rpm and 30 s at 2 rpm. How a plant detects reorientation Gravity-sensing cells contain dense, starch-rich amyloplasts. After reorientation, the amyloplasts move towards the new lower side of the cell. That physical change alters gravity signalling and directional auxin transport. Unequal growth on opposite sides of the organ produces curvature: primary roots usually bend with gravity, while shoots usually bend against it. [R63] Stage Root Shoot Measurement Gravity sensing columella cells in the root cap endodermal cells module angle and time after reorientation Signal asymmetric auxin transport towards the lower flank directional auxin redistribution organ angle and curvature over time Growth response positive gravitropic bending negative gravitropic bending root-tip angle, shoot angle and elongation rate The biological input depends on both orientation and exposure time. A slow cycle permits a longer dwell at each angle; a faster cycle changes direction more often. Drum speed is therefore reported together with acceleration ramps, stop duration and the complete angle-time series. Mechanical acceleration ROT-2 a c = ω²r Centripetal acceleration at radial distance r. ROT-5 a eff (t) = g + a c (t) + a vibration (t) The measured acceleration at a plant module combines gravity, rotation and vibration as vectors. Speed Cycle period Radius Centripetal acceleration Fraction of g 0.5 rpm 120 s 0.15 m 0.000411 m·s⁻² 0.0000419 2.0 rpm 30 s 0.15 m 0.00658 m·s⁻² 0.000671 At these example settings, centripetal acceleration is less than 0.07% of g. The dominant physical input is the changing direction of the gravity vector in plant coordinates. Vibration, airflow, liquid movement and start-stop transients are measured separately because they can also change plant growth. [R21] Rotation and root-zone exposure Drum angle also determines when each root module enters and leaves the nutrient solution. For every position, the run record stores immersion depth, immersed duration, drainage duration and retained liquid mass. A biological comparison must match average light and root-zone exposure between rotating and control plants; otherwise orientation, illumination and hydroponic contact change together. Controlled rotation experiment Group Variable isolated Static plant with matched mean light and root exposure baseline Rotating plant combined periodic orientation treatment Static plant with matched time-varying light light distribution Static plant with matched vibration mechanical vibration Rotating plant with slow acceleration ramps start-stop transient Primary endpoints are chosen before cultivation: root-tip angle or shoot curvature for orientation response, plus one growth or chemistry endpoint. Encoder angle, three-axis acceleration, plant images, light exposure, immersion and air velocity are synchronised by timestamp. The analysis uses the plant or independent cultivation cycle as the experimental unit. [R02] [R21] [R63]

Visual records:
- Gravity direction during one drum revolution (interactive rotation and gravity-vector diagram). Sources: I01, I02, R63.

References: I01, I02, R02, R21, R22, R32, R63

## Rotation measurement and biological comparison

- Canonical URL: https://flavorotor.com/research/rotation-validation
- Document ID: PR-ROT-001
- Group: Rotation and gravity
- Version: 2.0
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/rotation-validation
- JSON: https://flavorotor.com/research/data/chapters/rotation-validation.json

Encoder, accelerometer, light and root-zone measurements used to compare rotating and matched static plants.

Mechanical record The rotation record contains commanded and measured speed, angular position, direction, acceleration and stop duration. A three-axis accelerometer fixed at the plant module records vibration and transient acceleration. Each test reports mean speed, speed ripple, peak acceleration, RMS vibration and the difference between commanded and measured angle. Measurement Method Reported value Angular speed encoder count divided by elapsed time mean, SD, minimum and maximum Angular position encoder index at each timestamp position error and missed counts Acceleration ramp encoder and accelerometer time series ramp duration and peak acceleration Vibration three-axis accelerometer at the plant module axis-specific RMS and peak acceleration Endurance loaded continuous run temperature, stalls, slip events and speed drift Plant exposure record Mechanical measurements are synchronised with PPFD, air velocity and root-zone contact. One plant-position record therefore identifies the gravity direction in plant coordinates, incident light, immersion state and local air movement at the same time. This prevents a response caused by light or root-zone exposure from being assigned to rotation alone. Control group Matched variables Difference retained Static control crop, cultivar, age, mean PPFD, DLI and root-zone exposure no periodic reorientation Time-varying-light control light sequence and root-zone exposure static plant orientation Matched-vibration control measured vibration spectrum and cultivation conditions no drum rotation Rotating treatment cultivation conditions and sampling schedule periodic orientation cycle Plant response Root-tip angle, shoot curvature and elongation are measured from indexed image sequences. Growth, root architecture and plant chemistry are analysed as separate endpoints. The experimental unit is one plant or one independent cultivation cycle; repeated frames from the same plant are not counted as independent biological replicates. Gravitropic interpretation follows the measured angle-time history and the known statolith–auxin response of roots and shoots. [R21] [R63] Reporting The report publishes the complete speed profile, acceleration trace, light and immersion records, sample count, biological replicate count and analysis code. Effect estimates are reported with confidence intervals. A rotation setting is identified by drum speed, direction, radius, ramp profile, operating duration and stop schedule rather than by a device preset name.

References: I02, R21, R63

## Mechanical stimulation and plant response

- Canonical URL: https://flavorotor.com/research/mechanical-stimulation
- Document ID: FLV-MEC-001
- Group: Rotation and gravity
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/mechanical-stimulation
- JSON: https://flavorotor.com/research/data/chapters/mechanical-stimulation.json

Rotation-induced movement, vibration and airflow as measurable stimuli with matched controls.

In brief Rotation-induced movement, vibration and airflow as measurable stimuli with matched controls. Biological basis Plants can change morphology, growth and metabolism in response to repeated mechanical stimulation, a field commonly described through thigmomorphogenesis. [R21] Controlled mechanical stimulation in basil has been associated with metabolic and sensory changes, which provides a direct rationale for a FlavoRotor trial. [R02] What rotation may introduce periodic reorientation of stems and leaves; airflow relative to the canopy; vibration from drive components; acceleration and deceleration events; leaf-to-leaf or leaf-to-structure contact; periodic root immersion and drainage. Required controls A valid experiment uses a static light-matched control, a vibration-matched control where feasible, measured airflow, identical root-zone exposure and recorded acceleration profiles. Rotation cannot be isolated by comparing two systems that also differ in light and watering. Mechanisms to separate Mechanism FlavoRotor source Matched control Periodic reorientation drum motion static system with equivalent light exposure Vibration drive, bearings and acceleration events static plant exposed to measured vibration Air movement motion through local airflow fan treatment matched by air speed Leaf contact canopy interaction or enclosure contact contact-free geometry or standardised touch Root wetting sequential immersion matched wetting schedule without rotation Mechanical stimulation is biologically plausible, but the published basil stimulus is not equivalent to FlavoRotor motion. The experiment must measure and match the physical stimulus before attributing a plant response to rotation. [R02] [R21]

References: R02, R21

## Crop selection for FlavoRotor

- Canonical URL: https://flavorotor.com/research/crop-selection
- Document ID: CROP-SEL-001
- Group: Crop programmes
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/crop-selection
- JSON: https://flavorotor.com/research/data/chapters/crop-selection.json

Why basil, arugula, lettuce, mint and strawberry occupy different positions in the validation programme.

In brief Why basil, arugula, lettuce, mint and strawberry occupy different positions in the validation programme. Selection criteria A reference crop is selected by device fit, cycle length, commercial relevance, measurable sensory chemistry, published evidence and experimental tractability. Crop Programme role Primary endpoint Basil first aroma programme volatile profile and aroma discrimination Arugula pungency and nutrient-strength programme flavour-related phytochemicals and sensory pungency Lettuce system repeatability and texture programme growth, bitterness, texture and quality Mint essential-oil programme menthol-related volatile profile and aroma intensity Strawberry phase-two fruit-quality programme soluble solids, titratable acidity, VOCs, firmness and sensory response Basil has the strongest initial combination of short cycle, hospitality relevance and direct light/mechanical sensory literature. [R01] [R02] [R03] Strawberry is retained as a high-value application but follows root-zone and flowering validation because cultivation-system and cultivar effects are substantial. [R12] [R13] [R14] [R28]

References: R01, R02, R03, R12, R13, R14, R28

## Basil research programme

- Canonical URL: https://flavorotor.com/research/basil
- Document ID: CROP-BAS-001
- Group: Crop programmes
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/basil
- JSON: https://flavorotor.com/research/data/chapters/basil.json

A cultivar-specific programme for aroma, light, nutrient formulation and mechanical stimulation, centred on Italian Large Leaf basil.

In brief A cultivar-specific programme for aroma, light, nutrient formulation and mechanical stimulation, centred on Italian Large Leaf basil. Why basil first Basil fits the device geometry, has a relatively short cycle, is used directly in premium hospitality and has published hydroponic evidence linking controlled light to aroma volatiles and mechanical treatment to sensory/metabolic response. [R01] [R02] FlavoRotor starting condition Variable Initial protocol decision Basis Cultivar Italian Large Leaf matches [R01] pH target 5.9; operational band 5.8–6.0 [R01] maintained pH 5.9; [R03] used pH 6.0 EC record the EC produced by the defined elemental recipe; do not invent an aroma EC optimum [R01] / [R03] specify nutrient conditions; [R08] supplies separate cultivar-specific EC evidence EC screening after baseline 0.9, 1.2 and 1.5 mS/cm with one fixed stock formulation centres the screening around [R08] hydroponic basil range Light baseline measured spectrum, PPFD, DLI and 16 h photoperiod initially FlavoRotor baseline; all values measured before trial Rotation single measured baseline schedule; no rotation claim during first repeatability cycles engineering isolation Engineering note The pH value is a literature-matched operating condition, not a pH-for-aroma rule. EC treatments are experimental levels, not recommendations. BAS-LGT-001 — spectral treatment Use matched DLI and environmental conditions while changing a predefined spectral component. Primary outcome: selected volatile compounds by GC–MS. Secondary outcomes: fresh/dry mass, colour and blinded aroma discrimination. [R01] provides the literature anchor, not the expected FlavoRotor result. [R01] BAS-MEC-001 — rotation/mechanical treatment Compare rotating and matched static controls after engineering light and root-zone equivalence has been demonstrated. Primary outcome: a predefined volatile or sensory descriptor; secondary outcomes: morphology and biomass. [R02] [R21] BAS-NUT-001 — nutrient formulation Use complete elemental formulations and tissue analysis. Do not alter one stock bottle and label the response “potassium sweetness”. Cultivar, total ionic strength and nitrogen form remain explicit. [R03] [R04] [R08]

References: R01, R02, R03, R04, R08, R21

## Arugula research programme

- Canonical URL: https://flavorotor.com/research/arugula
- Document ID: CROP-ARU-001
- Group: Crop programmes
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/arugula
- JSON: https://flavorotor.com/research/data/chapters/arugula.json

A defined EC experiment for arugula cultivar Standard, measuring yield, nitrate, glucosinolates and sensory pungency.

In brief A defined EC experiment for arugula cultivar Standard, measuring yield, nitrate, glucosinolates and sensory pungency. Literature-matched starting condition Variable Condition Classification Cultivar Standard required to transfer [R05] directly pH 5.8 ± 0.1 study operating condition EC baseline 1.5 mS/cm low-middle study treatment EC treatments 1.2, 1.5, 1.8, 2.1 mS/cm exact [R05] treatment levels Recipe same balanced formulation scaled to target EC required for interpretability [R05] found the balance of growth and quality in the named cultivar was strongest around 1.5–1.8 mS/cm, while 2.1 mS/cm is better treated as a higher-strength experimental condition rather than a default. [R05] Outcomes Category Measurement Production fresh/dry mass, leaf area, harvest time Safety/quality nitrate concentration Flavour-related chemistry glucosinolates and selected phenolics Sensory pungency, bitterness, green aroma, overall liking Resource use water, nutrient additions and electricity per harvest mass Claim rule A higher glucosinolate concentration can support a mechanism for changed pungency, but sensory testing is still required. “More phytochemical” is not automatically “better tasting”.

References: R05

## Lettuce growth, forecasting and cultivation trials

- Canonical URL: https://flavorotor.com/research/lettuce
- Document ID: CROP-LET-001
- Group: Crop programmes
- Version: 2.0
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/lettuce
- JSON: https://flavorotor.com/research/data/chapters/lettuce.json

Longitudinal biomass forecasting with leave-one-plant-out validation, independent multimodal datasets and cultivar-specific pH, nutrition, flavour and root-zone temperature trials.

In brief A genotype-specific programme for pH, nutrient strength, root-zone temperature and short pre-harvest flavour treatments. Longitudinal lettuce growth A growth model needs repeated measurements from the same plant. Karimzadeh and Ahamed's dataset links 18 identified lettuce heads to 30 daily biomass measurements, RGB canopy images and environmental readings. The sequence contains 540 biomass observations and 1,443 environmental records. [R57] The mean fresh biomass rises from 3.37 g on day 1 after transplant to 234.56 g on day 30. The figure also retains the daily minimum and maximum, because a mean alone hides variation between plants. Three-day biomass forecast The forecast uses the most recent five daily masses to estimate biomass three days later. Three models are evaluated on the same 414 cases: persistence carries the latest mass forward; a five-day line extrapolates the local trend; ridge autoregression uses five masses, four daily mass increments and day after transplant. Each outer fold withholds one complete plant. The ridge penalty is selected inside the training fold by withholding each of the remaining plants in turn. Measurements from the evaluated plant therefore cannot choose its coefficients or regularisation strength. Model MAE RMSE MAPE R² Persistence 17.20 g 18.47 g 24.50% 0.8378 Five-day linear trend 5.56 g 6.86 g 8.07% 0.9777 Nested-CV ridge autoregression 2.89 g 3.67 g 4.18% 0.9936 GRW-MAE MAE = (1/n) Σᵢ |yᵢ − ŷᵢ| Mean absolute error is the average absolute difference between measured and forecast fresh biomass. GRW-RMSE RMSE = √[(1/n) Σᵢ (yᵢ − ŷᵢ)²] Root mean squared error gives more weight to large forecast errors. The ridge model reduces mean absolute error by 83.2% relative to persistence and by 48.0% relative to the five-day linear trend. Its plant-cluster bootstrap 95% interval is 2.67–3.10 g. This benchmark measures interpolation within one published cultivation study. A FlavoRotor growth model is re-evaluated by crop, cultivar, camera geometry and cultivation cycle. How the forecast is used The forecast creates an expected mass and an uncertainty range for the next observation. A measured plant that repeatedly falls outside that range is inspected together with its image sequence, pH, EC, light, temperature, dose history and root-zone record. The residual identifies an unusual trajectory; it does not name the cause by itself. GRW-RES eₜ₊ₕ = yₜ₊ₕ − ŷₜ₊ₕ The forecast residual is measured biomass minus predicted biomass at horizon h. Its sign and persistence show whether growth is ahead of or behind the fitted trajectory. Independent longitudinal datasets HydroGrowNet follows three 30-day Batavia lettuce cycles with daily images, pH, EC and water temperature. A second multi-sensor dataset follows 45 plants over 42 days under three nitrogen concentrations and two irrigation rates, with RGB, 3D, multispectral, SPAD and fluorescence records. These datasets add camera, cultivar and treatment variation that is absent from the 18-plant forecast benchmark. [R61] [R62] Cultivar rule Closed-soilless lettuce research also shows that genotype and macrocation supply interact in shaping the bioactive profile. This supports factorial crop-by-nutrient experiments rather than a universal nutrient rule. [R27] Lettuce responses are strongly cultivar-dependent. Every trial names the cultivar and does not combine cultivars as interchangeable replicates. LET-PH-001 Parameter Design pH levels 5.5, 6.0 and 6.5 EC/formulation fixed across pH treatments Primary outcome fresh/dry mass or a predefined physiological endpoint Secondary outcomes tissue minerals, colour, phenolics and sensory bitterness Literature basis [R06] tested pH 5.0–6.5; [R07] separates pH and alkalinity LET-EC-001 Use a named cultivar and treatment strengths derived from [R08] or [R09] rather than a universal 1.2–1.6 mS/cm statement. In [R08] , growth response differed between the tested lettuce and basil cultivars; [R09] showed functional-metabolite responses were genotype-dependent. [R08] [R09] LET-FLV-001 A confirmatory experiment can test the combined pre-harvest nitrogen limitation and controlled-light treatment reported by [R10] . Primary outcomes should include sensory sweetness/bitterness and the chemical variables used in the source study. [R10] LET-RTZ-001 Root-zone temperature is explicitly controlled because [R11] found cultivar-dependent effects on growth and °Brix. °Brix is reported as an instrumental endpoint, not automatically as perceived sweetness. [R11]

Visual records:
- The same controlled cultivation study observed through time (interactive longitudinal source-image gallery). Sources: R57. Data manifest: https://flavorotor.com/research/datasets/lettuce-growth-aalto/summary.json.
- Fresh-biomass trajectory across 18 identified lettuce heads (interactive mean and observed-range time-series chart). Sources: R57. Data manifest: https://flavorotor.com/research/datasets/lettuce-growth-aalto/growth-chart.json.
- Three-day biomass forecast tested on plants withheld from fitting (interactive model-error and confidence-interval chart). Sources: R57. Exact data: https://flavorotor.com/research/datasets/lettuce-growth-aalto/forecast-predictions.csv.
- Lettuce response to root-zone temperature (interactive dual-axis line chart). Sources: R11. Exact data: https://flavorotor.com/research/data/derived/r11-lettuce-temperature-table-3.csv.

References: R06, R07, R08, R09, R10, R11, R27, R57, R61, R62

## Mint research programme

- Canonical URL: https://flavorotor.com/research/mint
- Document ID: CROP-MNT-001
- Group: Crop programmes
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/mint
- JSON: https://flavorotor.com/research/data/chapters/mint.json

A conservative species- and clone-specific programme for biomass, essential-oil composition, salinity response and sensory intensity.

In brief A conservative species- and clone-specific programme for biomass, essential-oil composition, salinity response and sensory intensity. Initial FlavoRotor screening Variable Screening definition Classification Species/clone one named Mentha species and clonal source mandatory biological identity pH 5.8 controlled with a 5.7–5.9 operating band internal starting setpoint, not a literature optimum EC levels 1.2 and 1.6 mS/cm using the same balanced formulation internal feasibility screen, not a recommendation Salinity trial separate NaCl treatment only after baseline mechanism-specific experiment Primary outcome fresh/dry mass and selected essential-oil compounds predefined Sensory outcome mint intensity, freshness, bitterness and liking blinded and separate from chemistry Required trade-off analysis Report essential-oil concentration, total essential-oil amount per plant and biomass. Stress can increase concentration while reducing total usable yield.

References: R15, R16

## Strawberry research programme

- Canonical URL: https://flavorotor.com/research/strawberry
- Document ID: CROP-STR-001
- Group: Crop programmes
- Version: 1.2
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/strawberry
- JSON: https://flavorotor.com/research/data/chapters/strawberry.json

A later-stage programme that separates root-zone system suitability, nutrient strength, N×K interaction, fruit chemistry and sensory quality.

In brief A later-stage programme that separates root-zone system suitability, nutrient strength, N×K interaction, fruit chemistry and sensory quality. Why strawberry is phase two Strawberry requires a longer cycle, flowering and fruit set, stronger root-zone oxygen control, pollination management and post-harvest standardisation. A 2025 system comparison found the tested substrate system outperformed the tested water-culture systems, so the FlavoRotor root zone must be validated before flavour treatment claims. [R14] Two different literature anchors Anchor pH / EC Correct interpretation [R14] system comparison pH 5.5–6.5; EC 0.75–1.25 mS/cm operating range used in that multi-system study, not a taste optimum [R13] Kuemsil nutrient strength 1/3: pH 6.2, EC 1.1; 1/2: 6.0, 1.5; 2/3: 5.9, 1.9; full: 5.8, 2.5 exact treatment combinations for cultivar Kuemsil and that formulation/system [R13] reported the two-thirds treatment as the best compromise for the tested Kuemsil crop, but that result is not universal. [R13] Programme sequence Validate survival, flowering, fruit set and root-zone oxygen under one conservative recipe. Compare root support/medium configurations before nutrient-strength optimisation. Test nutrient strength in one named cultivar. Run an N×K factorial only after stable baseline production. Measure yield, °Brix, titratable acidity, firmness, volatiles and blinded sensory profile together. [R12] supports the N×K interaction design; [R28] supports combining volatile, quality and sensory analysis across cultivars. [R12] [R28]

Visual records:
- Strawberry yield and soluble solids under N and K treatments (interactive scatter chart). Sources: R12. Exact data: https://flavorotor.com/research/data/derived/r12-strawberry-nitrogen-potassium-table-1.csv.
- Strawberry fruit quality by nutrient-solution strength (interactive treatment table in published units). Sources: R13. Exact data: https://flavorotor.com/research/data/derived/r13-strawberry-strength-table-5.csv.

References: R12, R13, R14, R28

## Baseline cultivation protocol

- Canonical URL: https://flavorotor.com/research/baseline-cultivation
- Document ID: PR-CROP-000
- Group: Research methods
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/baseline-cultivation
- JSON: https://flavorotor.com/research/data/chapters/baseline-cultivation.json

The common protocol that must succeed before any taste, aroma, nutrient or rotation treatment is interpreted.

In brief The common protocol that must succeed before any taste, aroma, nutrient or rotation treatment is interpreted. Objective Demonstrate that one crop and cultivar can be grown repeatedly under a fixed recipe with acceptable environmental and biological variability. Minimum design Element Requirement Crop named species and cultivar Seed supplier and lot Replicates at least 8–12 biological units for the initial engineering baseline, refined by variance estimates Cycles three independent cultivation cycles before recipe-level claims Positions randomised and position effect tested Harvest fixed physiological/chronological rule Environment complete pH, EC, temperature, humidity, light and rotation logs Outputs germination, survival, fresh/dry mass, morphology, images and resource use Baseline acceptance Acceptance thresholds are defined before the first cycle for sensor uptime, recipe deviations, survival, position effect and coefficient of variation. Thresholds are revised only through a versioned protocol amendment, never after seeing the treatment outcome. Recommended first crop Italian Large Leaf basil is the strongest first research crop because published hydroponic aroma-light evidence exists, growth cycles are shorter than strawberry, tissue is directly used for aroma/sensory analysis and the plant fits the intended hospitality use case. [R01]

References: R01

## Light-distribution mapping

- Canonical URL: https://flavorotor.com/research/light-mapping
- Document ID: PR-LGT-001
- Group: Research methods
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/light-mapping
- JSON: https://flavorotor.com/research/data/chapters/light-mapping.json

A polar and position-indexed method for measuring spectrum, PPFD, DLI and temporal exposure in the rotating geometry.

In brief A polar and position-indexed method for measuring spectrum, PPFD, DLI and temporal exposure in the rotating geometry. Measurement grid Measure at every cultivation position or at a justified symmetric subset covering axial level, angular position and radial plant plane. Record the detector orientation and distance from the central source. Required conditions Condition Measurement Rotor static position-by-position PPFD and spectrum Rotor operating time-resolved exposure or rotation-integrated measurement Empty system optical baseline Representative canopy self-shading and reflection effect Thermal steady state light output and leaf-temperature stability Uniformity statistics L-2 CV PPFD = 100 · s PPFD / mean(PPFD) Position-to-position coefficient of variation. L-3 U min/mean = PPFD min / mean(PPFD) Minimum-to-mean uniformity ratio. Published output The calibration report contains the raw grid, polar heat map, spectrum, measurement uncertainty, detector model, light state, system geometry and DLI calculation. A single centre-point value is insufficient. Spectral scope The mapping protocol records the measured spectrum and includes far-red photons separately where present. [R33]

References: R33

## Chemical and physical analysis

- Canonical URL: https://flavorotor.com/research/chemical-analysis
- Document ID: PR-CHEM-001
- Group: Research methods
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/chemical-analysis
- JSON: https://flavorotor.com/research/data/chapters/chemical-analysis.json

Measurements that connect cultivation treatments to plant chemistry, volatile compounds, soluble solids, acidity, colour and texture.

In brief Measurements that connect cultivation treatments to plant chemistry, volatile compounds, soluble solids, acidity, colour and texture. Measurement chain Outcome Preferred method interpretation Volatile profile HS-SPME GC–MS with internal standard and batch QC chemical abundance is not identical to perceived aroma Phenolics/target metabolites validated HPLC/LC method target list and extraction recovery reported Mineral composition ICP-OES/ICP-MS or validated equivalent dry/fresh mass basis stated Soluble solids refractometry, °Brix not universally equal to perceived sweetness Titratable acidity standardised titration more informative than tissue pH alone for acid load Colour calibrated L*a*b* imaging or colorimetry illumination and calibration controlled Texture instrumental compression/puncture plus sensory descriptor method geometry and speed reported Fresh/dry mass traceable balance and drying method concentration and total amount both reported Instrumental colour may be reported in CIE L*a*b* coordinates when acquisition, illuminant, observer, instrument geometry and calibration are fixed. [R45] Sampling control Plant position, leaf age, time of day, harvest-to-analysis delay, storage temperature and sample preparation are standardised. Lettuce research shows that nutrient solution, season and post-harvest handling can alter quality and shelf-life interpretation. [R26] Reporting concentration correctly A treatment may increase a compound per gram while reducing total biomass. Report both concentration and total content per plant where feasible.

References: R26, R45

## Sensory analysis

- Canonical URL: https://flavorotor.com/research/sensory-analysis
- Document ID: PR-SENS-001
- Group: Research methods
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/sensory-analysis
- JSON: https://flavorotor.com/research/data/chapters/sensory-analysis.json

A staged sensory programme that separates detectable difference, descriptive profile and consumer preference.

In brief A staged sensory programme that separates detectable difference, descriptive profile and consumer preference. Three different questions Stage Question Method Difference Can assessors detect that samples differ? triangle test or other discrimination test Description How do they differ? trained descriptive vocabulary and intensity ratings Preference Which is liked or preferred? consumer/target-user hedonic or paired-preference study Triangle test ISO 4120 defines the triangle test: three coded samples are presented, two identical and one different, and the assessor identifies the odd sample. [R25] It establishes a perceptible difference, not which sample is better. General controls Sample coding, order randomisation, serving amount, temperature, preparation, palate cleansing, assessor eligibility, blinding and analysis follow a written protocol consistent with general ISO sensory guidance. [R29] Sample size Panel size is calculated from the selected test, significance level, desired power and minimum proportion of discriminators. It is not fixed at an arbitrary universal number. Hospitality validation Chef or venue feedback is collected only after analytical and blinded difference testing. Expert endorsement is useful for application relevance but does not replace controlled sensory evidence.

References: R25, R29

## Statistics, metadata and data publication

- Canonical URL: https://flavorotor.com/research/statistics-data
- Document ID: MTH-STAT-001
- Group: Research methods
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/statistics-data
- JSON: https://flavorotor.com/research/data/chapters/statistics-data.json

Experimental units, randomisation, blocking, mixed models, effect sizes, multiplicity and machine-readable data publication.

In brief Experimental units, randomisation, blocking, mixed models, effect sizes, multiplicity and machine-readable data publication. Experimental unit The biological experimental unit is the smallest independently assigned unit. Multiple leaves from one plant are subsamples, not independent plants. Plants sharing one reservoir may not be independent for a nutrient-solution treatment; reservoir or run can become the true treatment unit. Design controls random assignment to position and treatment; blocking by cycle and relevant spatial factor; blinded sample coding for laboratory and sensory analysis; predefined exclusion and deviation rules; power analysis updated from pilot variance; effect size and confidence interval reported with p-values. Example mixed model STAT-1 y = μ + treatment + position + treatment×cultivar + cycle(random) + ε Example structure; the final model follows the actual experimental unit and design. Multiple outcomes Primary outcomes are declared before analysis. Secondary chemistry and sensory endpoints are labelled and multiplicity is controlled where inferential claims are made. Measurement uncertainty and precision terminology are declared separately from biological variation. Repeatability and reproducibility are not treated as synonyms. [R38] [R46] [R50] Data package Every report links raw data, processed data, analysis code, data dictionary, protocol, deviations and checksums. Metadata follow MIAPPE concepts and FAIR principles. [R23] [R24]

References: R23, R24, R38, R46, R50

## Experiment registry

- Canonical URL: https://flavorotor.com/research/experiment-registry
- Document ID: REG-001
- Group: Research outputs
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/experiment-registry
- JSON: https://flavorotor.com/research/data/chapters/experiment-registry.json

The stable identifiers, prerequisites and public states for engineering calibration and crop experiments.

In brief The stable identifiers, prerequisites and public states for engineering calibration and crop experiments. Allowed states Proposed, protocol published, preregistered, in progress, data collection complete, under analysis, completed, replication in progress, replicated, inconclusive or discontinued with reason. Current register ID Title State Prerequisite CR-PMP-001 Four-channel gravimetric calibration protocol published assembled pump channels and traceable balance CR-MAG-001 Magnetic-drive slip-torque calibration protocol defined assembled v2 drive CR-LGT-001 Spatial light map protocol published final light and geometry CR-SEN-001 pH/EC/temperature calibration protocol published installed sensors ER-BAS-BASE-001 Italian Large Leaf baseline repeatability not started engineering calibrations complete ER-BAS-LGT-001 Basil spectral treatment not started three baseline cycles ER-BAS-MEC-001 Basil rotation/mechanical treatment not started matched light/root-zone controls ER-ARU-EC-001 Arugula EC response not started baseline cycle and calibrated dosing ER-LET-PH-001 Lettuce pH response not started stable pH control ER-STR-SYS-001 Strawberry root-zone feasibility not started oxygen and sanitation validation ER-MNT-SCR-001 Mint feasibility screen not started species/clone selected Registry rule A state changes only when the required artifact exists. “Completed” requires protocol, deviations, raw data, analysis and signed result summary. Registry metadata Each experiment record retains study, biological-material and observed-variable metadata and remains linked to released data. [R23] [R24]

References: R23, R24

## Protocol library

- Canonical URL: https://flavorotor.com/research/protocol-library
- Document ID: OUT-PRO-001
- Group: Research outputs
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/protocol-library
- JSON: https://flavorotor.com/research/data/chapters/protocol-library.json

The controlled documents that define calibration, cultivation, sampling, analysis and replication before results are interpreted.

In brief The controlled documents that define calibration, cultivation, sampling, analysis and replication before results are interpreted. Principle A protocol is published and versioned before the corresponding result is interpreted. Any substantive change creates a new protocol version. Protocol Purpose Current state FR-PRO-001 Peristaltic pump gravimetric calibration documented; execution pending FR-PRO-002 Empty-system environmental baseline method defined FR-PRO-003 Reference cultivation cycle crop-specific finalisation FR-PRO-004 Rotation and matched-control validation method defined FR-PRO-005 Sensory discrimination and descriptive analysis standards-aligned design Calibration and sensory methods use explicit metrology and sensory-analysis terminology. [R37] [R38] [R39] [R40] [R41] [R48] [R49] Required protocol sections Research question and preregistered hypothesis. Experimental unit, sample size and allocation method. System, crop, cultivar and biological-material identifiers. Independent, dependent and controlled variables with units. Calibration prerequisites and equipment register. Time-indexed procedure, sampling and harvest rules. Deviation, exclusion and stopping rules. Planned statistical analysis and publication criteria. A protocol receives a permanent identifier before execution. A method change produces a new version; it does not silently overwrite the executed method.

References: R37, R38, R39, R40, R41, R48, R49

## Datasets and growing recipes

- Canonical URL: https://flavorotor.com/research/datasets-recipes
- Document ID: OUT-DAT-001
- Group: Research outputs
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/datasets-recipes
- JSON: https://flavorotor.com/research/data/chapters/datasets-recipes.json

Required files, metadata and evidence levels for raw datasets, processed data and reusable cultivation recipes.

In brief Required files, metadata and evidence levels for raw datasets, processed data and reusable cultivation recipes. Dataset release Each dataset contains raw sensor records, actuator events, calibration identifiers, plant-material metadata, treatment allocation, deviations, harvest records and analysis code or processing instructions. Recipe release A growing recipe includes crop, cultivar, seed lot, developmental timeline, physical light targets, elemental nutrient formulation, pH and EC policy, root-zone conditions, rotation schedule, harvest protocol and validation scope. Release package Object Minimum contents Raw data unaltered sensor, event, image and laboratory records Metadata system, biological material, environment, units, calibration and provenance Processing versioned scripts, parameters and generated outputs Recipe time-indexed physical targets, tolerances, safety limits and supported system Result summary tested crop, cultivar, cycles, effect size, uncertainty and replication status Datasets are designed around MIAPPE-compatible plant metadata and FAIR principles. A recipe is released only with a bounded statement of where it was tested. [R23] [R24]

References: R23, R24

## Plant Teleport: validated recipe replication and greenhouse scale transfer

- Canonical URL: https://flavorotor.com/research/scale-up-transfer
- Document ID: FLV-PT-001
- Group: Plant Teleport
- Version: 3.0
- Updated: 2026-07-30
- Markdown: https://flavorotor.com/research/markdown/scale-up-transfer
- JSON: https://flavorotor.com/research/data/chapters/scale-up-transfer.json

A complete engineering and scientific framework for replicating a validated FlavoRotor recipe on another calibrated unit and translating it to a greenhouse using measured plant-level targets, local command compilation, uncertainty-aware acceptance and preregistered outcome equivalence.

Plant Teleport Replicate a validated cultivation result on another calibrated FlavoRotor and translate it to a greenhouse by transferring measured plant-level exposure targets, not machine-specific commands. SCIENTIFIC CONCLUSION The Plant Teleport architecture is technically feasible because its components-calibration, machine-independent recipes, environmental sensing, local control, metadata, independent replication and equivalence testing-are established engineering and scientific methods. What remains to be demonstrated experimentally is the achievable fidelity for each crop, property, machine pair and greenhouse implementation. CURRENT FLAVOROTOR STATUS The public FlavoRotor record defines the transfer framework but does not yet contain a completed cross-unit or greenhouse dataset demonstrating equivalent chemical and sensory outcomes. No figure in this article is presented as measured Plant Teleport performance unless it is explicitly labelled as measured data. ## Executive conclusion Plant Teleport builds on established engineering and scientific methods. A digital recipe can store calibrated physical targets, another system can compile those targets into its own actuator commands, and the delivered environment can be measured. Modern controlled-environment systems already combine sensors, actuators, time-varying targets and model-based control. [R69] Published plant research also demonstrates that controlled cultivation inputs can change quality-related plant chemistry and sensory-relevant outcomes under specified conditions. [R01] [R71] The difficult and scientifically valuable part is not sending the file. It is proving that the destination produced a sufficiently equivalent plant outcome. Therefore, the strongest accurate statement is: > **FlavoRotor can provide the infrastructure required to encode, translate, execute and validate portable cultivation recipes. The fidelity of a particular aroma, taste, colour or texture must then be established by independent cross-system measurements.** This converts a broad development goal into a structured research programme with defined validation milestones and concrete product architecture. PT-F15 · FRAMEWORK Evidence boundary Design, external support and FlavoRotor results must not be conflated. Does not show: Any current Plant Teleport experimental result. Sources: [I01] [I02] [R64] [R67] [R69] [R70] ## Engineering feasibility decision **Decision: technically feasible as an architecture; experimentally unverified for crop-specific property fidelity.** The required operations are established independently: measure physical exposure, calibrate actuators, store versioned metadata, translate targets to local hardware, operate closed-loop control, identify independent experimental units and test a predefined equivalence hypothesis. [R69] [R65] [R70] The remaining uncertainty is empirical rather than conceptual: how closely a particular destination can reproduce a specified chemical, physical or sensory profile for a named cultivar and recipe version. This decision creates two non-interchangeable statements: | Statement | Status | |---|---| | A calibrated destination can attempt to reproduce machine-independent physical targets. | Engineering architecture supported. | | A specific aroma, taste or texture will be equivalent after transfer. | Must be demonstrated for the stated scope. | PT-F20 · FRAMEWORK Compatibility matrix Transfer scope depends jointly on biological and hardware compatibility. Does not show: A quantified probability of success or universal compatibility rule. Sources: [R09] [R11] [R14] [R64] [R78] ## In plain terms Plant Teleport is FlavoRotor’s name for **evidence-gated recipe portability**. It does not mean that a plant or molecule is literally transported. A source FlavoRotor records: - the biological material; - the time-indexed environment experienced by the plant; - the machine and calibration version; - the harvest and post-harvest protocol; - the measured chemical, physical and sensory result; - the uncertainty, deviations and evidence level. A destination system receives that package and determines whether it can reproduce the required physical targets. It then calculates its own commands, runs the cultivation under measurement and repeats the outcome analysis. PT-F01 · FRAMEWORK Plant Teleport transfer architecture Machine-independent targets can be translated into local commands and verified. Does not show: A measured cross-unit or greenhouse success rate. Sources: [I01] [I02] [R23] [R24] [R38] [R39] [R67] [R69] The product interaction can still be simple: PT-F11 · FRAMEWORK One-click workflow A single user action can orchestrate a rigorous workflow. Does not show: A guaranteed successful outcome from one action. Sources: [R38] [R39] [R43] [R65] A single click may start the workflow. It cannot legitimately skip compatibility, calibration, biological variation or validation. ## Canonical terminology and units Plant Teleport uses one canonical meaning for each term so a human, controller and LLM interpret the recipe consistently. | Term | Canonical meaning | |---|---| | **Command** | Machine-specific instruction such as PWM duty, valve time or motor setpoint. | | **Target** | Desired physical quantity at a named plant or system location. | | **Measured exposure** | Time- and position-resolved quantity actually observed during cultivation. | | **Recipe** | Versioned biological, environmental, procedural and evidence package. | | **Reference run** | Validated source execution against which a destination is compared. | | **Destination run** | Execution on another machine, site or cultivation architecture. | | **Property endpoint** | Predefined chemical, physical or sensory response used in the transfer decision. | | **Equivalence margin** | Largest acceptable difference for a named endpoint, justified before data review. | | **Bridge experiment** | Controlled comparison required when machine, site or cultivation architecture changes materially. | Canonical reporting uses physical units rather than percentages wherever possible: - PPFD in µmol·m⁻²·s⁻¹; - DLI in mol·m⁻²·d⁻¹; - temperature in °C; - relative humidity in % and VPD in kPa; - EC in mS/cm with temperature or compensation method; - pH with electrode and calibration record; - elemental concentration in mmol/L or mg/L with the named chemical species; - liquid delivery in mL or g; - rotational speed in rev/min or angular velocity in rad/s; - airflow in m/s at a defined canopy position. A percentage may remain as a local command in the destination log, but it is not the portable recipe quantity. ## Why FlavoRotor is structurally suited to recipe transfer The architecture is favourable for portability because FlavoRotor is conceived as one integrated research platform rather than a loose collection of manually operated equipment. According to the internal design records, it combines programmable lighting, nutrient dosing, pH and electrical-conductivity monitoring, temperature sensing, rotation, imaging and versioned software records. [I01] [I02] This integration offers five specific advantages. ### 1. Common timing Every relevant event can be represented on one time axis: lighting transitions, dosing, reservoir measurements, rotation, imaging and harvest. Cross-system transfer is substantially weaker when the records come from unrelated clocks or handwritten logs. ### 2. Calibration-backed actuation A recipe can refer to a physical target rather than an arbitrary interface percentage. Calibration and measurement uncertainty provide the bridge between the requested quantity and the local actuator. [R38] [R39] ### 3. Complete provenance The system can preserve the relationship between recipe version, hardware version, sensor calibration, plant lot, images, raw logs, analytical samples and conclusions. MIAPPE and FAIR principles provide relevant structures for making such data interpretable and reusable. [R23] [R24] ### 4. Closed-loop verification The destination can measure whether it is achieving the target instead of assuming that commands were delivered correctly. This is central to current recommendations for environmental reporting in plant research. [R67] ### 5. A natural path to a marketplace A recipe can be distributed together with its compatibility requirements, evidence level, supported crop material, required analyses and validated system scope. The marketplace object becomes a controlled technical specification rather than a vague promise. ## What “property transfer” means A property is a measured response. Examples include: - concentrations of selected volatile compounds; - a defined chemical fingerprint; - soluble solids or titratable acidity; - colour coordinates; - dry matter or firmness; - a trained-panel sensory profile; - a consumer-liking result, when the claim concerns preference rather than descriptive equivalence. Published studies show that lighting, nutrient supply, temperature and other pre-harvest conditions can influence quality-related responses in specified crops and conditions. [R01] [R09] [R11] [R71] Property transfer means: > Reproducing a predefined outcome profile within declared limits by reproducing the relevant plant-level exposure trajectory, biological material, developmental state, harvest procedure and measurement method. It does not mean exact identity between individual plants. Biological systems exhibit variation even under carefully standardised protocols. A ten-laboratory study demonstrated that dedicated standardisation can produce similar growth results across a core group of laboratories, while small environmental and handling differences can still affect phenotypes and metabolite profiles. [R64] PT-F09 · FRAMEWORK Causal stack for property fidelity Outcome fidelity depends on biology, exposure, architecture, timing, handling and measurement. Does not show: The relative effect size of each layer. Sources: [R09] [R11] [R14] [R64] [R66] [R67] ## The decisive distinction: target transfer versus command copying The transferable object must not be: ```text LED = 73% pump A = 12 seconds fan = 40% rotation motor = 35% ``` Those commands are properties of one machine. The transferable object should be closer to: ```text canopy spectral photon target = versioned time series PPFD and DLI target = measured at defined plant positions elemental nutrient formulation = named species and concentrations pH and EC = target trajectories with temperature and measurement method root-zone temperature and wetting cycle = defined physical exposure air temperature, RH, VPD and airflow = measured at defined locations rotation and mechanical exposure = encoder-verified schedule harvest state = objective biological and chronological criteria ``` The destination translates each target through its own calibrated hardware. PT-F02 · ILLUSTRATIVE - NOT FLAVOROTOR DATA Machine command decoupling Different machines can use different commands to deliver the same physical target. Does not show: Actual FlavoRotor pump flow, accuracy or drift. Sources: [R38] [R39] For a liquid target: PT-1 t pump = V target / Q cal Purpose: convert the required volume into a pump-specific nominal running time. Vtarget is the target volume and Qcal is the calibrated flow for that pump, tube, liquid and operating condition. DEFINITIONAL TRANSLATION - DELIVERY MUST STILL BE VERIFIED If two pumps deliver 2 mL/min and 5 mL/min, respectively, both can target 10 mL, but their nominal commands are 5 minutes and 2 minutes. The recipe remains constant while the commands change. ## The Plant Teleport contract A transferable recipe is a contract between the source evidence and the destination capability. ### A. Biological passport The recipe records: - species and cultivar; - seed lot, clone batch or other material identifier; - propagation method; - germination or rooting conditions; - developmental stage at each recipe transition; - plant density and position; - replacement and exclusion rules; - plant-health observations; - definition of the independent experimental unit. Cultivar cannot be treated as a cosmetic label. Nutrient-strength and temperature responses can depend on genotype. [R09] [R11] ### B. Time-indexed exposure targets The package stores trajectories, not only averages: - spectral photon distribution; - PPFD; - DLI; - photoperiod and light transitions; - air and leaf temperature; - relative humidity and VPD; - carbon dioxide where controlled or material; - source-water composition; - elemental nutrient formulation; - pH, EC and solution temperature; - dissolved oxygen where relevant; - irrigation, immersion, drainage and aeration timing; - air velocity; - rotation and mechanical stimulation; - stage transitions and harvest timing. Dynamic plant-environment research shows why a time-varying regime can be more informative than one static mean. [R68] ### C. Capability declaration The destination publishes: - controllable range; - measurable range; - resolution; - uncertainty; - spatial coverage; - sampling interval; - valid calibration; - actuation delay; - safety limits; - unsupported variables. The interface must refuse validated execution when a mandatory variable cannot be controlled or verified. ### D. Harvest and post-harvest protocol Chemical and sensory properties may change after harvest. The recipe therefore defines: - objective harvest state; - time of day; - sample position and mass; - washing, cutting or preparation; - storage temperature and duration; - delay before instrumental or sensory analysis; - random coding and blinding. ### E. Reference outcome fingerprint A single number called “aroma” is normally insufficient. The fingerprint can contain multiple primary and supporting endpoints. PT-F13 · FRAMEWORK Outcome fingerprint Sensory property transfer must use a declared multidimensional endpoint set. Does not show: Any universal weighting or measured FlavoRotor profile. Sources: [R01] [R41] [R71] [R73] [R75] [R76] [R77] Descriptive sensory profiling, assessor selection, sensory-room control and general sensory methodology should follow defined methods. [R41] [R73] [R74] [R75] Sensory vocabulary should be standardised. [R76] Consumer liking is a different question from descriptive equivalence and requires an appropriate consumer test. [R77] ### F. Provenance and evidence Every claim links to raw and processed records. PT-F10 · FRAMEWORK Data provenance A claim should be traceable to raw data, calibrations, analysis and scope. Does not show: Current completeness of FlavoRotor public datasets. Sources: [R23] [R24] [R38] [R39] [R43] ## Transfer status PT0–PT5 PT-F04 · FRAMEWORK Plant Teleport evidence ladder Claims increase only when independent evidence increases. Does not show: Current achievement of any PT2–PT5 status. Sources: [R43] [R50] [R64] [R65] | Level | Name | Minimum meaning | Public wording | |---|---|---|---| | PT0 | Saved recipe | The source recipe and evidence package are complete. | “Recorded on the originating system.” | | PT1 | Repeated | Independent cycles on the same source system support the declared direction and variability. | “Repeated on the originating FlavoRotor.” | | PT2 | Cross-unit replicated | A second calibrated FlavoRotor meets the environmental and outcome criteria. | “Replicated on another calibrated FlavoRotor.” | | PT3 | Cross-location reproduced | PT2 is extended to another site with local water, room and handling effects addressed. | “Reproduced at another site under the stated conditions.” | | PT4 | System translated | A greenhouse or different cultivation architecture passes a bridge experiment. | “Translated and validated for greenhouse/system X.” | | PT5 | Independently verified | An independent partner executes and analyses the registered protocol. | “Independently verified within the published scope.” | The evidence burden increases as hardware, location and cultivation architecture diverge. PT-F05 · FRAMEWORK Transfer envelope Increasing system and environmental differences require stronger bridge validation. Does not show: A quantified probability of success. Sources: [R14] [R64] [R66] [R67] ## Recipe compiler architecture The compiler performs a preflight before the run button is enabled. ### Preflight gates 1. **Biological compatibility** - the correct cultivar and material identifier are available. 2. **Range compatibility** - the destination can reach every mandatory target. 3. **Measurement compatibility** - the destination can verify those targets at the required location and frequency. 4. **Calibration validity** - all mandatory sensors and actuators have valid calibration records. 5. **Method compatibility** - the required harvest, laboratory and sensory methods are available. 6. **Safety compatibility** - target and abort rules are compatible with the destination. 7. **Evidence compatibility** - the requested public claim does not exceed the available validation level. 8. **Licence compatibility** - the recipe version and permitted use are valid. The result is one of four states: - **compatible**; - **compatible with declared adaptation**; - **research-only**; - **incompatible**. PT-F12 · FRAMEWORK Transfer release decision Release status follows compatibility, exposure and outcome gates. Does not show: A current pass/fail result. Sources: [R43] [R46] [R65] [R70] ### Subsystem translation PT-F16 · FRAMEWORK Subsystem translation Recipe portability is implemented as calibrated translation for each subsystem. Does not show: That all listed subsystems are already fully validated on FlavoRotor. Sources: [I01] [I02] [R17] [R38] [R39] [R67] [R69] [R72] A recent digital-twin framework demonstrates that vendor-agnostic sensor/actuator mapping, model calibration, dynamic targets and adaptive control are technically implementable in controlled-environment agriculture. Its published case study is simulation-based, so it supports the software architecture rather than proving physical Plant Teleport performance. [R69] ## Measuring exposure fidelity For controlled quantity j : PT-2 e j (t) = y j (t) − r j (t) Purpose: compute instantaneous tracking error. yj(t) is the measured value; rj(t) is the target at the same time and location. MEASUREMENT DEFINITION PT-3 q j (t) = |e j (t)| / Δ j Purpose: normalise the error by the preregistered tolerance Δj. q ≤ 1 means the observation is inside the declared tolerance. NORMALISED COMPLIANCE METRIC - NOT A BIOLOGICAL RESULT PT-4 P j = 100 · ΣI(q j ≤ 1) / N Purpose: report the percentage of valid observations inside tolerance. N is the number of valid observations; I is an indicator equal to one when the condition is true. CONTROL-PERFORMANCE SUMMARY The acceptance value for P j , excursion duration and safety limits must be established by protocol. No universal percentage is claimed. PT-F03 · ILLUSTRATIVE - NOT FLAVOROTOR DATA Exposure fidelity over time Transfer requires time-resolved exposure compliance, not matching a single average. Does not show: Any measured FlavoRotor trajectory or tolerance. Sources: [R38] [R39] [R67] [R68] Environmental compliance is necessary but not sufficient. A destination can reproduce the target environment and still obtain a different biological result because of seed material, microbiology, developmental timing or unmeasured differences. A valid transfer therefore uses a conjunctive decision: PT-5 G transfer = G biology ∧ G exposure ∧ G method ∧ G outcome Purpose: express that a validated transfer requires every mandatory gate to pass. The logical AND symbol means that one failed critical gate prevents the full claim. DECISION LOGIC - NOT A CONTINUOUS PERFORMANCE SCORE ## Uncertainty-aware environmental compliance A measured value close to a tolerance boundary cannot be treated as an unquestioned pass when measurement uncertainty overlaps that boundary. Calibration, uncertainty and acceptance must therefore be connected. [R38] [R39] [R43] For a measured estimate y j , target r j , tolerance Δ j , standard uncertainty u j and chosen coverage factor k , a conservative guard-band rule can be written as: PT-5A |y j − r j | + k·u j ≤ Δ j Purpose: require the measured deviation plus the declared uncertainty allowance to remain inside the tolerance. The coverage factor and decision rule must be specified by the protocol; they are not universal constants. ILLUSTRATIVE GUARD-BAND RULE BASED ON METROLOGY PRINCIPLES **Illustrative example.** A target is 22.0 °C with a tolerance of ±0.5 °C. The measured estimate is 22.3 °C and the standard uncertainty is 0.1 °C. If the protocol uses k = 2, the guarded deviation is 0.3 + 2 × 0.1 = 0.5 °C, exactly at the declared limit. This example explains the rule; it is not a FlavoRotor acceptance specification. PT-F17 · ILLUSTRATIVE - NOT FLAVOROTOR DATA Uncertainty-aware environmental acceptance Measurement uncertainty should be included in the environmental acceptance rule. Does not show: A universal coverage factor, tolerance or measured FlavoRotor uncertainty. Sources: [R38] [R39] [R43] The environmental report must distinguish: - target uncertainty or allowable biological tolerance; - instrument calibration uncertainty; - spatial sampling uncertainty; - temporal interpolation uncertainty; - missing data; - actuator-delivery uncertainty. A recipe should not declare a tolerance narrower than the destination can credibly measure and control. ## Translation by physical domain ### Light The recipe stores spectrum, intensity, distribution and timing at plant level. For a constant PPFD interval: PT-6 DLI = PPFD · τ · 10 −6 Purpose: convert PPFD in µmol·m⁻²·s⁻¹ over duration τ in seconds into DLI in mol·m⁻². For variable light, integrate the measured PPFD time series. PHOTON-EXPOSURE DEFINITION Matching DLI alone is insufficient when spectrum, spatial distribution or within-day trajectory differs. Artificial and natural lighting can produce different metabolic responses even when important lighting characteristics are approximated. [R66] ### Nutrient composition EC is an aggregate conductivity measurement, not an ion-specific recipe. Different ion mixtures can produce similar EC, and recirculating systems can change individual nutrient concentrations. [R17] The portable recipe therefore stores: - source-water analysis; - named nutrient species; - stock formulation; - intended elemental concentrations; - pH and EC trajectories; - reservoir volume and replacement rules; - mixing verification; - analytical checkpoints where necessary. ### Root-zone exposure A rotating hydroponic system and a greenhouse substrate system do not expose roots identically. Translation must consider: - contact or irrigation duration; - drainage; - oxygen availability; - root-zone temperature; - solution movement; - biofilm and sanitation; - water-holding properties of the destination medium. Cultivation-system comparisons show that system architecture can materially alter crop performance. [R14] ### Air and leaf environment Canopy air temperature is not always leaf temperature. Room relative humidity is not necessarily canopy humidity. The transfer protocol therefore defines sensor placement and records temperature, humidity, VPD, airflow and carbon dioxide when relevant. ### Mechanical and rotation exposure Rotation can simultaneously affect watering, orientation and mechanical stimulation. If rotation is part of a validated treatment, the destination must separate: - root wetting caused by rotation; - light-exposure geometry; - mechanical stimulus; - air movement; - position-dependent effects. A greenhouse child recipe may reproduce the relevant physical exposure through different hardware, but this requires a bridge experiment rather than semantic relabelling. ### Camera and plant-state alignment A camera can support transfer by detecting: - germination and establishment; - leaf area; - canopy coverage; - colour changes; - growth rate; - visible stress; - developmental transitions; - positional non-uniformity. The camera does not directly measure taste or aroma. It can align recipe stages and detect deviations that would otherwise make two runs biologically incomparable. The imaging algorithm, model version, confidence threshold and manual-audit rule become part of the recipe record. ## Cross-unit replication protocol The first demonstration should use two FlavoRotor systems of the same supported hardware generation. ### Required design - frozen recipe and analysis plan; - one cultivar and traceable material lot; - independent biological units; - randomised plant positions; - multiple independent cultivation cycles; - calibrated light, dosing, environmental and rotation subsystems; - predefined primary endpoints; - matched harvest and post-harvest methods; - full deviation reporting. The number of plants and cycles cannot be selected by a universal rule. It must be calculated using pilot variability, the justified equivalence margin, desired power and the actual experimental-unit structure. Pseudoreplication must be avoided. Multiple leaves from one plant or several plants sharing one unreplicated environment do not automatically represent independent treatment units. [R65] PT-F14 · ILLUSTRATIVE - NOT FLAVOROTOR DATA Independent replication and blocking Reference and destination units should be independently replicated and spatially blocked. Does not show: A required sample size or actual FlavoRotor layout. Sources: [R65] ### Minimum reports - calibration report for both units; - empty-system spatial and temporal map; - source and destination exposure comparison; - biological baseline; - chemical and physical results; - sensory report when sensory equivalence is claimed; - statistical analysis; - raw dataset and analysis code; - failed or excluded units with reasons. ## Cross-location reproduction Another country or room introduces additional variables: - local water chemistry; - ambient temperature and humidity; - electrical and maintenance differences; - operator handling; - sample transport; - laboratory method and instrument; - sensory language and panel context. A cross-location transfer should use either the same analysis laboratory or a documented inter-laboratory method comparison. Measurement-method reproducibility and biological reproducibility must remain distinct. [R50] ## Greenhouse scale translation A greenhouse is not a physically enlarged FlavoRotor. It is a new cultivation architecture exposed to sunlight, weather disturbances, spatial gradients and different root-zone dynamics. The correct process is: 1. map the greenhouse; 2. define controllable zones; 3. measure plant-level conditions; 4. compile supplemental controls; 5. run a bridge experiment; 6. compare outcomes; 7. release a greenhouse-specific child recipe. PT-F06 · FRAMEWORK Greenhouse zone translation Greenhouse transfer must be measured and compiled per zone. Does not show: The number, dimensions or performance of a real greenhouse. Sources: [R66] [R67] [R68] [R69] [R72] ### Spatial mapping and sensor placement A greenhouse average is not a plant-level exposure record. Light, temperature, humidity and airflow can vary across positions, and controlled chambers can also exhibit spatial or chamber effects. [R78] Greenhouse mapping methods likewise begin from spatial measurements rather than assuming homogeneity. [R79] The recommended workflow is: 1. deploy a temporary dense sensor grid during representative operating periods; 2. quantify spatial and temporal gradients at canopy and root-zone locations; 3. identify zones that can be controlled as coherent units; 4. select representative permanent sensors; 5. validate the reduced sensor arrangement against the dense map; 6. repeat mapping after material changes in crop height, layout, season, glazing, airflow or equipment. PT-F18 · FRAMEWORK Greenhouse mapping strategy Greenhouse translation should begin with measured spatial mapping and validated operational zones. Does not show: A universal sensor count, zone layout or mapping accuracy. Sources: [R67] [R79] The number of sensors is an experimental design decision. Plant Teleport does not prescribe a universal sensor density. ### Supplemental light balance PT-7 DLI supp = max(0, DLI target − DLI sun,usable ) Purpose: estimate the daily supplemental photon amount after measured usable sunlight is credited. Spectrum, timing, distribution and plant state remain separate matching requirements. FIRST-ORDER BALANCE - NOT A COMPLETE GREENHOUSE TRANSLATION If the target DLI is 16 mol·m⁻²·d⁻¹ and measured usable sunlight contributes 10 mol·m⁻²·d⁻¹, the first-order supplement is 6 mol·m⁻²·d⁻¹. These numbers are an explanatory example, not a FlavoRotor crop recommendation. ### Zone control Each zone converts physical targets into its own: - supplemental-light schedule; - fertigation events; - nutrient-stock commands; - heating, cooling and ventilation; - humidification or dehumidification; - airflow; - carbon-dioxide control where used; - alarms and recovery rules. Sensor-controlled fertigation can be evaluated in greenhouse hydroponic production using quality, yield and resource-use outcomes. [R72] This supports the practical feasibility of local sensor-to-actuator control; it does not prove transfer of a FlavoRotor aroma profile. ### Bridge experiment The greenhouse bridge includes: - validated source condition; - translated greenhouse condition; - spatial blocks and independent units; - repeated production periods; - matched cultivar and biological material; - same primary analytical endpoints; - matched harvest and post-harvest handling; - blinded sensory method when sensory equivalence is claimed; - a declared acceptable loss if strict equivalence is not the intended objective. The greenhouse child recipe receives a new identifier. PT-F08 · FRAMEWORK Recipe lineage Every transfer creates a traceable child record and report. Does not show: Actual released FlavoRotor recipes. Sources: [R23] [R24] ## Chemical and sensory confirmation A chemical difference can exist without a perceptible sensory difference. A sensory difference can also reflect compounds not included in a narrow targeted analysis. Plant Teleport therefore treats chemical and sensory evidence as complementary. ### Three property-claim tiers “Same aroma” is ambiguous unless the evidence domain is named. PT-F19 · FRAMEWORK Property claim tiers A precise transfer claim must identify whether it concerns chemistry, descriptive sensory attributes or consumers. Does not show: That success at one tier guarantees success at another. Sources: [R01] [R41] [R73] [R74] [R75] [R76] [R77] - **Tier A - chemical and physical profile:** selected volatile compounds, non-volatile chemistry, colour or texture meet predefined analytical criteria. - **Tier B - descriptive sensory equivalence:** trained assessors produce equivalent intensities for predefined attributes under a controlled protocol. - **Tier C - consumer response:** a stated consumer population does not detect a relevant difference or shows comparable liking under the chosen design. A recipe may pass one tier and fail another. Chemical similarity does not prove perceptual equivalence, and comparable liking does not prove chemical identity. ### Chemical layer Depending on the claim: - targeted volatile compounds; - broader volatile fingerprint; - sugars and organic acids; - pigments; - minerals; - dry matter; - compounds linked to pungency, bitterness or aroma; - method uncertainty and quality-control samples. ### Physical layer - colour coordinates; - firmness; - fracture or compression response; - water content; - structural measurements. ### Descriptive sensory layer A trained panel may profile intensity of defined attributes. Panel recruitment and training, test-room conditions, vocabulary, sample preparation and analysis must be documented. [R41] [R73] [R74] [R75] [R76] ### Consumer layer Consumer liking is not proof of descriptive equivalence. It answers whether a defined consumer population prefers or accepts the samples. [R77] ## How equivalence is decided A result is not equivalent merely because a difference was not statistically significant. Insufficient sample size can produce a non-significant result even when important differences remain. For one continuous primary endpoint: PT-8 CI 90% (μ D − μ R ) ⊂ [−Δ, +Δ] Purpose: declare equivalence only when the complete confidence interval for the destination-minus-reference effect lies inside the predeclared equivalence interval. Δ must be justified before data review. CLASSICAL TWO ONE-SIDED TESTS FRAMEWORK PT-F07 · ILLUSTRATIVE - NOT FLAVOROTOR DATA Equivalence decision examples A complete confidence interval must lie inside a justified equivalence margin. Does not show: A FlavoRotor margin, sample size or measured effect. Sources: [R46] [R70] For multiple endpoints: - primary endpoints are declared in advance; - every essential endpoint must satisfy its criterion; - multiplicity is handled in the analysis plan; - yield and plant-health guardrails cannot be ignored; - chemical and sensory evidence are not substituted for one another; - exploratory endpoints remain labelled exploratory; - missing-data and outlier rules are preregistered. There is no universal ±10% aroma margin. Each margin must be justified using measurement capability, baseline biological variation and the intended product claim. ## Exact research programme ### Experiment PT-E01 - same-machine repeatability **Question:** Can one frozen recipe be executed across independent cycles on one FlavoRotor with stable exposure and outcome variability? **Outputs:** PT1 recipe, calibration bundle, baseline variance, endpoint shortlist. ### Experiment PT-E02 - second-unit replication **Question:** Can a second calibrated FlavoRotor independently reproduce the required exposure trajectory and primary outcome profile? **Outputs:** PT2 transfer report, command-translation comparison, failed-variable analysis. ### Experiment PT-E03 - cross-location reproduction **Question:** Does the recipe remain equivalent when executed at another site after water, room, operator and analytical differences are controlled? **Outputs:** PT3 report, site adaptation record, inter-laboratory method check where necessary. ### Experiment PT-E04 - greenhouse pilot bridge **Question:** Can a defined greenhouse zone reproduce the relevant physical exposure and outcome endpoints? **Outputs:** zone map, greenhouse child recipe, PT4 report or documented non-equivalence. ### Experiment PT-E05 - independent verification **Question:** Can an independent partner execute the frozen package without unpublished assistance and obtain the declared result? **Outputs:** PT5 report, independent raw data, audit of ambiguities and required clarifications. This programme creates a direct sequence from prototype engineering to a commercially meaningful validated recipe network. ## Marketplace and licensing model A recipe listing should display: - crop and cultivar; - version and lineage; - status PT0–PT5; - supported machine versions; - supported location or greenhouse scope; - required biological material; - mandatory cartridges and analysis; - primary outcome endpoints; - equivalence scope; - known unsupported transfers; - licence terms; - linked reports and datasets. A buyer should be able to distinguish: - **recipe available**; - **validated on source unit**; - **replicated on another unit**; - **translated to named greenhouse**; - **independently verified**. This evidence structure is commercially favourable because it converts trust from a marketing statement into a visible product attribute. ## Recipe integrity, signing and marketplace trust Portability creates a new engineering risk: a valid recipe can become unsafe or scientifically misleading if its file, evidence status or calibration requirements are altered. Every released package should therefore include: - immutable recipe ID and semantic version; - parent recipe ID and lineage; - SHA-256 checksums for recipe, data and analysis files; - signer identity and signature method; - creation and expiry or review date; - supported hardware and cultivar scope; - minimum calibration bundle; - exact PT status and linked reports; - revoked and superseded status; - licence and permitted-use metadata. The destination must reject or downgrade a run when: - the signature or checksum fails; - the recipe version is revoked; - required calibration is expired; - source IDs resolve to different records; - mandatory variables are unavailable; - the requested claim exceeds the recipe’s evidence status. A marketplace rating is not scientific evidence. User feedback can identify usability problems, but PT status can change only through the declared validation records. ## LLM and machine-readable interpretation The machine-readable record must state explicitly: ```json { "claim_status": "framework_not_yet_demonstrated_by_public_transfer_dataset", "portable_object": "plant_level_physical_targets_plus_biological_and_evidence_metadata", "does_not_establish": [ "identical flavour guaranteed worldwide", "equivalence from copied device settings", "transfer across cultivars without validation", "completed greenhouse transfer" ] } ``` Every visual record states: - whether it is a framework, an illustrative example or measured data; - the exact claim it supports; - what it does not show; - its source IDs. This prevents an LLM from converting a conceptual graph into an experimental result. ## Failure modes and safeguards | Failure mode | Consequence | Required safeguard | |---|---|---| | Same interface percentages, different physical exposure | False portability | Store physical targets and calibrations | | Same EC, different ion balance | Different nutrient treatment | Store elemental formulation and water chemistry | | Same average DLI, different spectrum or trajectory | Different plant response | Store spectrum and time-resolved light | | Same cultivar name, different lot or propagation | Different starting biology | Biological passport | | One chamber per treatment | Treatment confounded with chamber | Independent units and valid blocking | | Greenhouse average hides spatial zones | Unmeasured local exposure | Canopy-level zone mapping | | Different harvest maturity | Different chemistry and texture | Objective harvest state | | Different post-harvest handling | Altered aroma or texture | Frozen sample-handling protocol | | “No significant difference” used as equivalence | False positive claim | Predeclared margins and equivalence analysis | | Simulation presented as physical validation | Evidence inflation | Explicit model status and bridge experiment | | Only successful transfers published | Biased marketplace | Retain failure and inconclusive records | | New hardware inherits old status | Invalid lineage | New version and transfer report | ## Evidence boundary Published research supports that: - controlled pre-harvest conditions can influence horticultural quality-related traits; [R01] [R71] - standardisation can improve cross-laboratory reproducibility, while residual site effects remain; [R64] - actual environmental measurement is essential for interpretation and repeatability; [R67] - dynamic environmental trajectories can be reproduced in controlled infrastructure; [R68] - digital-twin and adaptive-control architectures can integrate heterogeneous sensors and actuators; [R69] - sensor-coupled greenhouse fertigation is technically practical; [R72] - independent replication and equivalence methods are established scientific tools. [R65] [R70] The public FlavoRotor evidence does not yet establish: - equivalent aroma on two physical FlavoRotor units; - cross-country reproduction of a measured property profile; - a validated greenhouse child recipe; - universal compatibility or equivalence margins; - identical sensory results from a downloaded file. ## Questions the completed research must answer 1. Which measured properties are stable enough to become primary transfer endpoints? 2. What fraction of variability comes from machine delivery, biological material, site and analysis? 3. Which variables must be matched exactly and which can be compensated? 4. How far can hardware versions differ before a bridge experiment is required? 5. Can camera-derived plant state improve recipe-stage alignment and outcome fidelity? 6. Which crops are best suited to initial cross-unit validation? 7. What equivalence margins are scientifically and commercially meaningful? 8. How much greenhouse zoning is necessary for a stable child recipe? 9. Can failed transfers be used to improve the recipe compiler? 10. What evidence badge is understandable to consumers without overstating certainty? ## Definition of done for the first credible Plant Teleport demonstration The first public PT2 claim is complete only when all of the following are available: - two independently calibrated physical FlavoRotor units; - a frozen recipe and biological material identifier; - a preregistered design with a correctly identified experimental unit; - independent cycles and positional randomisation; - empty-system light, temperature, solution and rotation maps; - source and destination exposure logs; - a declared primary chemical or physical endpoint; - a defined sensory method if sensory equivalence is claimed; - justified equivalence margins; - raw and processed datasets; - versioned analysis code; - calibration, replication and deviation reports; - a public result classified as passed, failed or inconclusive. A failed or inconclusive first transfer remains valuable. It reveals which part of the portable specification or local compiler requires improvement and prevents premature commercial claims. ## Final definition > **Plant Teleport is FlavoRotor’s evidence-gated system for moving a validated cultivation specification between calibrated machines and into larger controlled environments. It transfers time-indexed plant exposure targets, biological material definitions, harvest rules and an outcome fingerprint; compiles those targets into local commands; verifies the delivered environment; and releases a replication or scale-transfer claim only after independent measurements meet preregistered criteria.** The transferable asset is a validated specification and evidence chain. The ambition is clear and technically credible: > **Create a plant property profile once, validate it rigorously, reproduce it on another calibrated FlavoRotor, and translate it to commercial cultivation with measured and published fidelity.** Article bibliography Sources used on this page R69 Frontzek, Julius; Wagner, Zühal; Streif, Stefan (2026). Dynamic, adaptive and modular Digital Twin framework for resource-efficient Controlled Environment Agriculture. Frontiers in Plant Science, 17, 1864757 . 10.3389/fpls.2026.1864757 Used for: Supports vendor-agnostic sensor and actuator integration, dynamic target trajectories, model calibration and adaptive control as an architecture for translating targets to local equipment. Evidence boundary: Its case study is simulation-based and uses synthetic data; physical, sensory and cross-facility recipe transfer remain unvalidated. R01 Hammock, Hunter A.; Sams, Carl E. (2023). Variation in supplemental lighting quality influences key aroma volatiles in hydroponically grown &#x27;Italian Large Leaf&#x27; basil. Frontiers in Plant Science . 10.3389/fpls.2023.1184664 Used for: Shows that a defined spectral treatment can alter selected aroma-related volatile measurements in one named hydroponic basil cultivar. Evidence boundary: It does not demonstrate that the result transfers to another cultivar, machine, location or greenhouse. R71 Zhao, Xinyi; Peng, Jie; Zhang, Li; et al. (2024). Optimizing the quality of horticultural crop: insights into pre-harvest practices in controlled environment agriculture. Frontiers in Plant Science, 15, 1427471 . 10.3389/fpls.2024.1427471 Used for: Reviews how pre-harvest manipulation of nutrients, light and other controlled-environment factors can affect horticultural quality, including colour, aroma and taste-related outcomes. Evidence boundary: A broad review establishes scientific plausibility and candidate mechanisms; it does not validate any FlavoRotor recipe, universal optimum or cross-system transfer. I01 FlavoRotor project team (2026). FlavoRotor prototype implementation record. Internal engineering report . /research/platform Used for: Documents the built rotating prototype, sensing electronics, dashboard and current validation limitations. Evidence boundary: An internal engineering record does not demonstrate cross-machine recipe replication or biological property transfer. I02 FlavoRotor project team (2026). FlavoRotor v2.0 system architecture. Internal engineering design report . /research/platform Used for: Documents the proposed magnetic drive, axial lighting, four-channel peristaltic dosing and imaging architecture. Evidence boundary: Architecture and design targets are not a measured demonstration of Plant Teleport. R64 Massonnet, Catherine; Vile, Denis; Fabre, Justine; et al. (2010). Probing the Reproducibility of Leaf Growth and Molecular Phenotypes: A Comparison of Three Arabidopsis Accessions Cultivated in Ten Laboratories. Plant Physiology, 152(4), 2142–2157 . 10.1104/pp.109.148338 Used for: Shows that detailed standardisation can produce similar growth in a core group of laboratories, while small laboratory-environment differences can still alter growth and metabolite phenotypes. Evidence boundary: The study used Arabidopsis and did not test hydroponic flavour, FlavoRotor hardware or one-click recipe transfer. R67 Vincent, Christopher; Leisner, Courtney P.; Locke, Anna M.; Teshome, Demissew Tesfaye; et al. (2025). Importance of measuring and reporting environmental conditions across plant science subdisciplines. Plant Physiology, 199(2), kiaf405 . 10.1093/plphys/kiaf405 Used for: Supports measuring actual environmental conditions—rather than reporting equipment settings alone—to improve replicability and cross-scale interpretation. Evidence boundary: The paper proposes reporting practices and does not demonstrate Plant Teleport or define crop-specific tolerances. R70 Schuirmann, Donald J. (1987). A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680 . 10.1007/BF01068419 Used for: Provides the classical two one-sided tests framework for evaluating whether an effect lies within predeclared equivalence margins. Evidence boundary: The method originated in bioequivalence; FlavoRotor must justify crop- and endpoint-specific margins, models and multiplicity handling before using it. R65 Rogers, Alistair; Dietz, Karl-Josef; Gifford, Miriam L.; Lunn, John E. (2021). The importance of independent replication of treatments in plant science. Journal of Experimental Botany, 72(15), 5270–5274 . 10.1093/jxb/erab268 Used for: Explains independent experimental units, randomisation and why pseudoreplication can invalidate treatment claims. Evidence boundary: It provides experimental-design requirements, not evidence that a FlavoRotor recipe has been replicated. R09 Senizza, Biancamaria; Zhang, Leilei; Miras-Moreno, Begoña; et al. (2020). The Strength of the Nutrient Solution Modulates the Functional Profile of Hydroponically Grown Lettuce in a Genotype-Dependent Manner. Foods . 10.3390/foods9091156 Used for: Demonstrates that nutrient-strength responses may depend on genotype. Evidence boundary: A recipe cannot be assumed portable across cultivars or genetic material. R11 Thakulla, Dharti; Dunn, Bruce; Hu, Bizhen; Goad, Carla; Maness, Niels (2021). Nutrient Solution Temperature Affects Growth and °Brix Parameters of Seventeen Lettuce Cultivars Grown in an NFT Hydroponic System. Horticulturae . 10.3390/horticulturae7090321 Used for: Shows cultivar-dependent responses to root-zone temperature. Evidence boundary: The tested NFT conditions and °Brix response do not define a universal transfer recipe. R14 Hutchinson, George Kerrigan; Nguyen, Lan Xuan; Ames, Zilfina Rubio; Nemali, Krishna; Ferrarezi, Rhuanito Soranz (2025). Substrate system outperforms water-culture systems for hydroponic strawberry production. Frontiers in Plant Science . 10.3389/fpls.2025.1469430 Used for: Shows that cultivation-system architecture can materially change crop performance. Evidence boundary: A result from one root-zone architecture cannot be copied to another without a bridge experiment. R78 Porter, Amanda S.; Evans-Fitz.Gerald, Christiana; McElwain, Jennifer C.; Yiotis, Charilaos; Elliott-Kingston, Caroline (2015). How well do you know your growth chambers? Testing for chamber effect using plant traits. Plant Methods, 11, 44 . 10.1186/s13007-015-0088-0 Used for: Demonstrates that nominally identical controlled-environment chambers can produce chamber effects and supports independent sensing, pilot testing, randomisation and replicated experimental units. Evidence boundary: The study used Vicia faba in walk-in growth chambers; it does not quantify FlavoRotor unit-to-unit variability or property-transfer fidelity. R23 Papoutsoglou, E. A. et al. (2020). Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytologist . 10.1111/nph.16544 Used for: Provides reusable metadata structures for investigations, studies, biological material and observed variables. Evidence boundary: Metadata interoperability does not prove biological equivalence. R24 Wilkinson, Mark D. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data . 10.1038/sdata.2016.18 Used for: Defines findable, accessible, interoperable and reusable data principles. Evidence boundary: FAIR data can make a recipe reusable, but does not make its biological result automatically reproducible. R38 Joint Committee for Guides in Metrology (2008). Evaluation of measurement data — Guide to the expression of uncertainty in measurement. JCGM 100:2008 . 10.59161/JCGM100-2008E Used for: Defines measurement uncertainty and uncertainty propagation. Evidence boundary: It provides a measurement framework, not crop-specific tolerances. R39 Joint Committee for Guides in Metrology (2012). International vocabulary of metrology — Basic and general concepts and associated terms. JCGM 200:2012 . 10.59161/JCGM200-2012 Used for: Defines calibration, accuracy, precision, repeatability and related terms. Evidence boundary: Metrology vocabulary does not define a transferable flavour profile. R43 International Organization for Standardization (2017). ISO/IEC 17025:2017 General requirements for the competence of testing and calibration laboratories. ISO/IEC . https://www.iso.org/standard/66912.html Used for: Supports traceable testing, calibration, method control and records. Evidence boundary: Use of an external laboratory must not imply accreditation outside its verified scope. R66 Annunziata, Maria Grazia; Apelt, Federico; Carillo, Petronia; et al. (2017). Getting back to nature: a reality check for experiments in controlled environments. Journal of Experimental Botany, 68(16), 4463–4477 . 10.1093/jxb/erx220 Used for: Demonstrates that natural sunlight and artificial controlled-light regimes can produce different metabolic profiles even when important lighting features are approximated. Evidence boundary: The study used Arabidopsis and does not quantify FlavoRotor-to-greenhouse flavour transfer. R68 Heuermann, Marc C.; Knoch, Dominic; Junker, Astrid; Altmann, Thomas (2023). Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation. Nature Communications, 14, 5783 . 10.1038/s41467-023-41332-4 Used for: Shows the value of reproducing time-varying environmental trajectories rather than only static averages when bridging controlled and natural conditions. Evidence boundary: The work concerns maize development in the IPK PhenoSphere; it does not demonstrate hydroponic aroma equivalence or FlavoRotor greenhouse transfer. R41 International Organization for Standardization (2016). ISO 13299:2016 Sensory analysis — Methodology — General guidance for establishing a sensory profile. International Standard . https://www.iso.org/standard/58042.html Used for: Supports structured sensory-attribute and intensity profiling. Evidence boundary: A sensory profile must still be defined for each crop, product and claim. R73 International Organization for Standardization (2023). ISO 8586:2023 Sensory analysis — Selection and training of sensory assessors. International Standard . https://www.iso.org/standard/76667.html Used for: Defines criteria and procedures for selecting and training trained and expert sensory assessors. Evidence boundary: Training assessors improves method control; it does not guarantee that two plant samples are sensorially equivalent. R75 International Organization for Standardization (2017). ISO 6658:2017 Sensory analysis — Methodology — General guidance. International Standard . https://www.iso.org/standard/65519.html Used for: Provides general guidance on sensory tests and the statistical treatment of sensory-analysis results. Evidence boundary: It does not prescribe a universal Plant Teleport experiment or guarantee outcome equivalence. R76 International Organization for Standardization (2008). ISO 5492:2008 Sensory analysis — Vocabulary. International Standard, with Amendment 1:2016 . https://www.iso.org/standard/38051.html Used for: Defines sensory-analysis terminology relating to senses, organoleptic attributes and methods. Evidence boundary: Vocabulary alignment improves semantic precision but does not create experimental evidence. R77 International Organization for Standardization (2014). ISO 11136:2014 Sensory analysis — Methodology — General guidance for conducting hedonic tests with consumers in a controlled area. International Standard, with Amendment 1:2020 . https://www.iso.org/standard/50125.html Used for: Provides guidance for measuring consumer liking and preference under controlled conditions. Evidence boundary: Consumer liking is distinct from descriptive sensory equivalence and must not replace chemical or trained-panel evidence. R74 International Organization for Standardization (2007). ISO 8589:2007 Sensory analysis — General guidance for the design of test rooms. International Standard, with Amendment 1:2014 . https://www.iso.org/standard/36385.html Used for: Provides guidance for sensory test-room design and separation of testing, preparation and support areas. Evidence boundary: The standard is under revision and does not define crop-specific attributes, assessors or equivalence margins. R50 International Organization for Standardization (2025). ISO 5725-2:2025 Accuracy (trueness and precision) of measurement methods and results — Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method. International Standard . https://www.iso.org/standard/90054.html Used for: Supports estimation of repeatability and reproducibility of measurement methods. Evidence boundary: Measurement-method reproducibility is distinct from biological recipe reproducibility. R46 Heckert, N. Alan; Filliben, James J.; Croarkin, C. M.; et al. (2002). NIST/SEMATECH e-Handbook of Statistical Methods. NIST Handbook 151 . https://www.nist.gov/publications/handbook-151-nistsematech-e-handbook-statistical-methods Used for: Supports experimental design, calibration regression, residual analysis and statistical process control. Evidence boundary: The handbook does not define biologically meaningful equivalence margins for FlavoRotor. R17 Vought, Kelsey; Bayabil, Haimanote K.; Pompeo, Jean; Crawford, Daniel; Zhang, Ying; Correll, Melanie; Martin-Ryals, Ana (2024). Dynamics of micro and macronutrients in a hydroponic nutrient film technique system under lettuce cultivation. Heliyon . 10.1016/j.heliyon.2024.e32316 Used for: Supports the statement that bulk EC does not uniquely specify individual ion concentrations. Evidence boundary: NFT lettuce nutrient dynamics are not numerically transferable to every reservoir and crop. R72 Hutchinson, George Kerrigan; Nguyen, Lan Xuan; Rubio Ames, Zilfina; Nemali, Krishna; Ferrarezi, Rhuanito Soranz (2025). Sensor-controlled fertigation management for higher yield and quality in greenhouse hydroponic strawberries. Frontiers in Plant Science, 15, 1469434 . 10.3389/fpls.2024.1469434 Used for: Demonstrates that sensor-coupled fertigation strategies can be evaluated for yield, quality and resource use in greenhouse hydroponic strawberry production. Evidence boundary: The study concerns specified cultivars, substrate, sensors and management strategies; it does not prove FlavoRotor-to-greenhouse aroma transfer. R79 Brentarolli, Elia; Locatelli, Silvia; Nicoletto, Carlo; Sambo, Paolo; Quaglia, Davide; Muradore, Riccardo (2024). A spatio-temporal methodology for greenhouse microclimatic mapping. PLOS ONE, 19(9), e0310454 . 10.1371/journal.pone.0310454 Used for: Supports temporary dense sensing, spatial modelling and greenhouse microclimate mapping when a single environmental average is insufficient. Evidence boundary: The paper presents a greenhouse mapping methodology; it does not define FlavoRotor sensor placement, aroma equivalence or a universal number of greenhouse zones.

Visual records:
- Plant Teleport transfer architecture (SVG framework diagram). Sources: I01, I02, R23, R24, R38, R39, R67, R69.
- Machine command decoupling (SVG illustrative diagram). Sources: R38, R39.
- Exposure fidelity over time (SVG illustrative diagram). Sources: R38, R39, R67, R68.
- Plant Teleport evidence ladder (SVG framework diagram). Sources: R43, R50, R64, R65.
- Transfer envelope (SVG framework diagram). Sources: R14, R64, R66, R67.
- Greenhouse zone translation (SVG framework diagram). Sources: R66, R67, R68, R69, R72.
- Equivalence decision examples (SVG illustrative diagram). Sources: R46, R70.
- Recipe lineage (SVG framework diagram). Sources: R23, R24.
- Causal stack for property fidelity (SVG framework diagram). Sources: R09, R11, R14, R64, R66, R67.
- Data provenance (SVG framework diagram). Sources: R23, R24, R38, R39, R43.
- One-click workflow (SVG framework diagram). Sources: R38, R39, R43, R65.
- Transfer release decision (SVG framework diagram). Sources: R43, R46, R65, R70.
- Outcome fingerprint (SVG framework diagram). Sources: R01, R41, R71, R73, R75, R76, R77.
- Independent replication and blocking (SVG illustrative diagram). Sources: R65.
- Evidence boundary (SVG framework diagram). Sources: I01, I02, R64, R67, R69, R70.
- Subsystem translation (SVG framework diagram). Sources: I01, I02, R17, R38, R39, R67, R69, R72.
- Uncertainty-aware environmental acceptance (SVG illustrative diagram). Sources: R38, R39, R43.
- Greenhouse mapping strategy (SVG framework diagram). Sources: R67, R79.
- Property claim tiers (SVG framework diagram). Sources: R01, R41, R73, R74, R75, R76, R77.
- Compatibility matrix (SVG framework diagram). Sources: R09, R11, R14, R64, R78.

References: I01, I02, R01, R09, R11, R14, R17, R23, R24, R38, R39, R41, R43, R46, R50, R64, R65, R66, R67, R68, R69, R70, R71, R72, R73, R74, R75, R76, R77, R78, R79

## Publications and technical reports

- Canonical URL: https://flavorotor.com/research/publications
- Document ID: OUT-PUB-001
- Group: Research outputs
- Version: 1.1
- Updated: 2026-07-26
- Markdown: https://flavorotor.com/research/markdown/publications
- JSON: https://flavorotor.com/research/data/chapters/publications.json

How technical reports, calibration reports, experiment reports, datasets and peer-reviewed papers are separated.

In brief How technical reports, calibration reports, experiment reports, datasets and peer-reviewed papers are separated. Document types Prefix Document Minimum content TR Technical report design, assumptions, calculations and validation plan PR Protocol frozen method before execution CR Calibration report raw measurements, model, residuals and uncertainty ER Experiment report protocol, deviations, analysis and conclusion linked to the tested conditions DS Dataset raw and processed data with metadata RR Replication report independent repeat and comparison PB Peer-reviewed publication publisher-reviewed scientific output External laboratory output External test or calibration results identify the laboratory, method and competence scope without implying accreditation that has not been verified. [R43] Publication types Prefix Document What it establishes TR technical report architecture, calculations or engineering analysis PR protocol method fixed before execution CR calibration report measured actuator or sensor performance ER experiment report result from a defined trial DS dataset machine-readable observations and metadata RR replication report repeatability or transfer evidence RC released cultivation recipe validated target profile within a declared scope

References: R43

## Scientific bibliography

- Canonical URL: https://flavorotor.com/research/bibliography
- Document ID: BIB-001
- Group: Research outputs
- Version: 1.2
- Updated: 2026-07-29
- Markdown: https://flavorotor.com/research/markdown/bibliography
- JSON: https://flavorotor.com/research/data/chapters/bibliography.json

Canonical index of the scientific literature, standards, metrology records and internal engineering records used by FlavoRotor Research.

In brief The canonical bibliography is maintained as structured source records rather than copied references inside individual articles. How the bibliography is used Every factual claim based on external literature is connected to a source identifier. Selecting the identifier opens the title, authors, publication, DOI or official record, the reason the source is used, how it supports the explanation and the metadata-verification record. Source classes Class Use Peer-reviewed research biological, chemical, sensory or engineering evidence Peer-reviewed review mechanism, scope and interpretation limits Official standard sensory, laboratory, colour and measurement methods Metrology record calibration, uncertainty, repeatability and terminology Official technical record component operation and electrical constraints Internal primary record FlavoRotor-specific design, build and media provenance Canonical source library The complete, current bibliography contains 66 records and is published at Research references . Article-level bibliographies contain only the records used on that page. Audit exports The public research archive also contains machine-readable JSON, CSV and BibTeX records, plus article, paragraph, sentence, formula and image-provenance ledgers. These files support editorial review and do not replace the public source drawer.

References: None

## Source registry

### I01 — FlavoRotor prototype implementation record

FlavoRotor project team (2026). Internal engineering report.
URL: https://flavorotor.com/research/platform
Used for: Documents the built rotating prototype, sensing electronics, dashboard and current validation limitations.

### I02 — FlavoRotor v2.0 system architecture

FlavoRotor project team (2026). Internal engineering design report.
URL: https://flavorotor.com/research/platform
Used for: Documents the proposed magnetic drive, axial lighting, four-channel peristaltic dosing and imaging architecture.

### I03 — FlavoRotor peristaltic pump technical record

FlavoRotor project team (2026). Internal engineering record.
URL: https://flavorotor.com/research/peristaltic-pump
Used for: Documents CAD geometry, components, first-order equations, four-channel integration and the proposed calibration protocol.

### R01 — Variation in supplemental lighting quality influences key aroma volatiles in hydroponically grown 'Italian Large Leaf' basil

Hammock, Hunter A.; Sams, Carl E. (2023). Frontiers in Plant Science.
URL: https://doi.org/10.3389/fpls.2023.1184664
Used for: Direct evidence that spectral treatment can modify volatile profiles in a named hydroponic basil cultivar.

### R02 — Controlled mechanical stimuli reveal novel associations between basil metabolism and sensory quality

Seeburger, P.; Herdenstam, A.; Kurtser, P.; Arunachalam, A.; Castro-Alves, V. C.; Hyötyläinen, T.; Andreasson, H. (2023). Food Chemistry.
URL: https://doi.org/10.1016/j.foodchem.2022.134545
Used for: Supports testing controlled mechanical stimulation as a contributor to basil metabolic and sensory response.

### R03 — Nutraceutical Profiles of Two Hydroponically Grown Sweet Basil Cultivars as Affected by the Composition of the Nutrient Solution and the Inoculation With Azospirillum brasilense

Kolega, Simun; Miras-Moreno, Begoña; Buffagni, Valentina; Lucini, Luigi; Valentinuzzi, Fabio; Maver, Mauro; Mimmo, Tanja; Trevisan, Marco; Pii, Youry; Cesco, Stefano (2020). Frontiers in Plant Science.
URL: https://doi.org/10.3389/fpls.2020.596000
Used for: Shows cultivar- and nutrient-composition-dependent changes in basil biomass and nutraceutical traits.

### R04 — Evaluating Species-Specific Replenishment Solution Effects on Plant Growth and Root Zone Nutrients with Hydroponic Arugula (Eruca sativa L.) and Basil (Ocimum basilicum L.)

Houston, Lauren L.; Dickson, Ryan W.; Bertucci, Matthew B.; Roberts, Trenton L. (2023). Horticulturae.
URL: https://doi.org/10.3390/horticulturae9040486
Used for: Supports species-specific nutrient replenishment and root-zone accounting for basil and arugula.

### R05 — Photosynthesis, Biomass Production, Nutritional Quality, and Flavor-Related Phytochemical Properties of Hydroponic-Grown Arugula (Eruca sativa Mill.) 'Standard' under Different Electrical Conductivities of Nutrient Solution

Yang, Teng; Samarakoon, Uttara C.; Altland, James; Ling, Peter (2021). Agronomy.
URL: https://doi.org/10.3390/agronomy11071340
Used for: Directly compares EC 1.2, 1.5, 1.8 and 2.1 mS/cm in arugula cultivar Standard and reports yield and flavour-related phytochemicals.

### R06 — Precise Management of Hydroponic Nutrient Solution pH: The Effects of Minor pH Changes and MES Buffer Molarity on Lettuce Physiological Properties

Kudirka, Gediminas; Viršilė, Akvilė; Sutulienė, Rūta; Laužikė, Kristina; Samuolienė, Giedrė (2023). Horticulturae.
URL: https://doi.org/10.3390/horticulturae9070837
Used for: Provides controlled lettuce evidence across pH 5.0–6.5 and demonstrates that small root-zone pH changes affect physiology.

### R07 — Growth and Tissue Elemental Composition Response of Butterhead Lettuce (Lactuca sativa, cv. Flandria) to Hydroponic Conditions at Different pH and Alkalinity

Anderson, T. S.; Martini, M. R.; de Villiers, D.; Timmons, M. B. (2017). Horticulturae.
URL: https://doi.org/10.3390/horticulturae3030041
Used for: Supports separating pH from alkalinity and measuring tissue composition in lettuce.

### R08 — Nutrient Use in Vertical Farming: Optimal Electrical Conductivity of Nutrient Solution for Growth of Lettuce and Basil in Hydroponic Cultivation

Hosseini, Hadis; Mozafari, Vahid; Roosta, Hamid Reza; Shirani, Hossein; van de Vlasakker, Paulien C. H.; Farhangi, Mohsen (2021). Horticulturae.
URL: https://doi.org/10.3390/horticulturae7090283
Used for: Provides cultivar-specific EC response data for hydroponic lettuce and basil.

### R09 — The Strength of the Nutrient Solution Modulates the Functional Profile of Hydroponically Grown Lettuce in a Genotype-Dependent Manner

Senizza, Biancamaria; Zhang, Leilei; Miras-Moreno, Begoña; Righetti, Laura; Zengin, Gokhan; Ak, Gunes; Bruni, Renato; Lucini, Luigi; Sifola, Maria Isabella; El-Nakhel, Christophe; Corrado, Giandomenico; Rouphael, Youssef (2020). Foods.
URL: https://doi.org/10.3390/foods9091156
Used for: Demonstrates genotype-dependent changes in lettuce functional metabolites with nutrient-solution strength.

### R10 — Pre-harvest Nitrogen Limitation and Continuous Lighting Improve the Quality and Flavor of Lettuce (Lactuca sativa L.) under Hydroponic Conditions in Greenhouse

Yang, Xiao; Hu, Jiangtao; Wang, Zheng; Huang, Tao; Xiang, Yuting; Zhang, Li; Peng, Jie; Tomas-Barberan, Francisco A.; Yang, Qichang (2023). Journal of Agricultural and Food Chemistry.
URL: https://doi.org/10.1021/acs.jafc.2c07420
Used for: Supports a confirmatory lettuce trial combining a defined pre-harvest nitrogen treatment with controlled lighting and sensory/chemical endpoints.

### R11 — Nutrient Solution Temperature Affects Growth and °Brix Parameters of Seventeen Lettuce Cultivars Grown in an NFT Hydroponic System

Thakulla, Dharti; Dunn, Bruce; Hu, Bizhen; Goad, Carla; Maness, Niels (2021). Horticulturae.
URL: https://doi.org/10.3390/horticulturae7090321
Used for: Supports measuring root-zone temperature and cultivar interaction rather than treating temperature as a background variable.

### R12 — Interactive Effects of the Potassium and Nitrogen Relationship on Yield and Quality of Strawberry Grown Under Soilless Conditions

Preciado-Rangel, Pablo; Troyo-Diéguez, Enrique; Valdez-Aguilar, Luis Alonso; García-Hernández, José Luis; Luna-Ortega, José Guadalupe (2020). Plants.
URL: https://doi.org/10.3390/plants9040441
Used for: Supports factorial strawberry experiments for potassium and nitrogen, with fruit quality and yield measured together.

### R13 — Nutrient solution strength affects growth, physiology, biochemistry and fruit quality of Korean strawberry 'Kuemsil' in a recycling hydroponic system

Zebro, Mewuleddeg; Baek, Jeong-Hyeon; Kim, Minkyung; Jeong, Youngae; Rabbani, M. G.; Choi, Ki-Young (2025). Frontiers in Plant Science.
URL: https://doi.org/10.3389/fpls.2025.1685755
Used for: Provides exact nutrient-strength, pH and EC treatment combinations for cultivar Kuemsil and a measured fruit-quality response.

### R14 — Substrate system outperforms water-culture systems for hydroponic strawberry production

Hutchinson, George Kerrigan; Nguyen, Lan Xuan; Ames, Zilfina Rubio; Nemali, Krishna; Ferrarezi, Rhuanito Soranz (2025). Frontiers in Plant Science.
URL: https://doi.org/10.3389/fpls.2025.1469430
Used for: Supports treating root-zone architecture and oxygenation as critical strawberry factors and records an operating pH/EC range used in the comparison.

### R15 — Functional Quality, Antioxidant Capacity and Essential Oil Percentage in Different Mint Species Affected by Salinity Stress

Hosseini, Seyyed Jaber; Tahmasebi-Sarvestani, Zeinolabedin; Mokhtassi-Bidgoli, Ali; Keshavarz, Hamed; Kazemi, Shahryar; Khalvandi, Masoumeh; Pirdashti, Hematollah; Hashemi-Petroudi, Seyyed Hamidreza; Nicola, Silvana (2023). Chemistry & Biodiversity.
URL: https://doi.org/10.1002/cbdv.202200247
Used for: Supports species-specific mint screening for salinity, essential-oil percentage and biomass trade-offs.

### R16 — Conceptual design of LED-based hydroponic photobioreactor for high-density plant cultivation

Shotipruk, A.; Kaufman, P. B.; Wang, H. Y. (1999). Biotechnology Progress.
URL: https://doi.org/10.1021/bp990114i
Used for: Historical engineering precedent for combining high-density hydroponics and controlled lighting.

### R17 — Dynamics of micro and macronutrients in a hydroponic nutrient film technique system under lettuce cultivation

Vought, Kelsey; Bayabil, Haimanote K.; Pompeo, Jean; Crawford, Daniel; Zhang, Ying; Correll, Melanie; Martin-Ryals, Ana (2024). Heliyon.
URL: https://doi.org/10.1016/j.heliyon.2024.e32316
Used for: Directly supports the statement that maintaining bulk EC does not guarantee stable individual-ion concentrations.

### R18 — Fluid–Structure Interaction Modeling Applied to Peristaltic Pump Flow Simulations

Formato, Gaetano; Romano, Raffaele; Formato, Andrea; Sorvari, Joonas; Koiranen, Tuomas; Pellegrino, Arcangelo; Villecco, Francesco (2019). Machines.
URL: https://doi.org/10.3390/machines7030050
Used for: Supports modelling tubing deformation and the limits of a rigid geometric displacement estimate.

### R19 — An open-source peristaltic pump with multiple independent channels for laboratory automation

Buchhorn, Michael; Akkoc, Gun Deniz; Dworschak, Dominik (2025). Digital Discovery.
URL: https://doi.org/10.1039/D5DD00157A
Used for: Provides a relevant multi-channel open-source pump architecture and a measured calibration methodology.

### R20 — "Do-It-Yourself" reliable pH-stat device by using open-source software, inexpensive hardware and available laboratory equipment

Milanovic, Jovana Z.; Milanovic, Predrag; Kragic, Rastislav; Kostic, Mirjana (2018). PLOS ONE.
URL: https://doi.org/10.1371/journal.pone.0193744
Used for: Supports incremental pH control, calibration, logging and mixing-aware feedback with accessible hardware.

### R21 — Thigmomorphogenesis: a complex plant response to mechano-stimulation

Chehab, E. Wassim; Eich, Elizabeth; Braam, Janet (2009). Journal of Experimental Botany.
URL: https://doi.org/10.1093/jxb/ern315
Used for: Establishes the biological basis for measuring plant responses to repeated mechanical stimulation.

### R22 — Comparison of Microgravity Analogs to Spaceflight in Studies of Plant Growth and Development

Kiss, John Z.; Wolverton, Chris; Wyatt, Sarah E.; Hasenstein, Karl H.; van Loon, Jack J. W. A. (2019). Frontiers in Plant Science.
URL: https://doi.org/10.3389/fpls.2019.01577
Used for: Defines limitations of clinostats and related microgravity analogues and supports conservative gravity claims.

### R23 — Enabling reusability of plant phenomic datasets with MIAPPE 1.1

Papoutsoglou, E. A. et al. (2020). New Phytologist.
URL: https://doi.org/10.1111/nph.16544
Used for: Provides a practical metadata structure for investigation, study, biological material and observed variables.

### R24 — The FAIR Guiding Principles for scientific data management and stewardship

Wilkinson, Mark D. et al. (2016). Scientific Data.
URL: https://doi.org/10.1038/sdata.2016.18
Used for: Defines findable, accessible, interoperable and reusable research-data principles.

### R25 — ISO 4120:2021 Sensory analysis — Methodology — Triangle test

International Organization for Standardization (2021). ISO.
URL: https://www.iso.org/standard/76666.html
Used for: Defines the triangle test for determining whether a perceptible sensory difference exists.

### R26 — Influence of nutrient solutions in an open-field soilless system on the quality characteristics and shelf life of fresh-cut red and green lettuces (Lactuca sativa L.) in different seasons

Luna, María C.; Martínez-Sánchez, Ascensión; Selma, María V.; Tudela, Juan A.; Baixauli, Carlos; Gil, María I. (2013). Journal of the Science of Food and Agriculture.
URL: https://doi.org/10.1002/jsfa.5777
Used for: Supports controlling post-harvest handling and season when interpreting lettuce quality.

### R27 — The bioactive profile of lettuce produced in a closed soilless system as configured by combinatorial effects of genotype and macrocation supply composition

El-Nakhel, C. et al. (2019). Food Chemistry.
URL: https://doi.org/10.1016/j.foodchem.2019.125713
Used for: Supports factorial macrocation-by-genotype experiments in closed soilless lettuce production.

### R28 — Strawberry Production in Soilless Culture Systems: A Comparative Analysis of Volatile Metabolites, Quality, and Sensory Traits in Three Cultivars

Malorni, Livia; Di Renzo, Tiziana; Matarazzo, Cristina; Petriccione, Milena; Ferrara, Elvira; Capriolo, Giuseppe; Baruzzi, Gianluca; Sbrighi, Paolo; Cozzolino, Rosaria (2026). Foods.
URL: https://doi.org/10.3390/foods15061072
Used for: Supports combining volatile analysis, instrumental fruit-quality measurements and sensory analysis across strawberry cultivars.

### R29 — ISO 6658:2017 Sensory analysis — Methodology — General guidance

International Organization for Standardization (2017). ISO.
URL: https://www.iso.org/standard/65519.html
Used for: Provides general sensory-analysis planning, sample presentation and interpretation guidance.

### R30 — Using Deep Learning for Image-Based Plant Disease Detection

Mohanty, Sharada P.; Hughes, David P.; Salathé, Marcel (2016). Frontiers in Plant Science.
URL: https://doi.org/10.3389/fpls.2016.01419
Used for: Provides a canonical controlled-image plant-disease classification benchmark and documents generalisation limitations.

### R31 — MobileNetV2: Inverted Residuals and Linear Bottlenecks

Sandler, Mark; Howard, Andrew; Zhu, Menglong; Zhmoginov, Andrey; Chen, Liang-Chieh (2018). 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
URL: https://doi.org/10.1109/CVPR.2018.00474
Used for: Provides the architecture basis for an efficient edge-image classifier.

### R32 — Microgravity research in plants: A range of platforms and options allow research on plants in zero or low gravity that can yield important insights into plant physiology

Böhmer, Maik; Schleiff, Enrico (2019). EMBO Reports, 20, e48541.
URL: https://doi.org/10.15252/embr.201948541
Used for: Explains real, simulated and partial-gravity platforms and the limits of ground-based rotation systems.

### R33 — Far-red photons have equivalent efficiency to traditional photosynthetic photons: Implications for redefining photosynthetically active radiation

Zhen, Shuyang; Bugbee, Bruce (2020). Plant, Cell & Environment, 43, 1259–1272.
URL: https://doi.org/10.1111/pce.13730
Used for: Supports measuring far-red as part of the photon environment and evaluating it in combination with shorter wavelengths.

### R34 — Decision-tree-based ion-specific dosing algorithm for enhancing closed hydroponic efficiency and reducing carbon emissions

Cho, Woo-Jae; Gang, Min-Seok; Kim, Dong-Wook; Kim, JooShin; Jung, Dae-Hyun; Kim, Hak-Jin (2023). Frontiers in Plant Science, 14, 1301490.
URL: https://doi.org/10.3389/fpls.2023.1301490
Used for: Demonstrates ion-specific monitoring and multi-stock dosing while accounting for coupled ions in fertilizer salts.

### R35 — Ion-Specific Nutrient Management in Closed Systems: The Necessity for Ion-Selective Sensors in Terrestrial and Space-Based Agriculture and Water Management Systems

Bamsey, Matthew; Graham, Thomas; Thompson, Cody; Berinstain, Alain; Scott, Alan; Dixon, Michael (2012). Sensors, 12, 13349–13392.
URL: https://doi.org/10.3390/s121013349
Used for: Explains why closed nutrient systems require ion-specific information when precise ionic balance is the objective.

### R36 — Recycling Nutrient Solution Can Reduce Growth Due to Nutrient Deficiencies in Hydroponic Production

Miller, Alexander; Adhikari, Ranjeeta; Nemali, Krishna (2020). Frontiers in Plant Science, 11, 607643.
URL: https://doi.org/10.3389/fpls.2020.607643
Used for: Shows that maintaining target EC in recycled hydroponics can mask individual nutrient deficiencies and unwanted-ion accumulation.

### R37 — NIST Calibration Services for Liquid Volume

Bean, Vern E.; Espina, Pedro I.; Wright, John D.; Sheckels, Sherry D.; Johnson, Aaron N. (2006). NIST Special Publication 250-72.
URL: https://doi.org/10.6028/NIST.SP.250-72
Used for: Provides traceable liquid-volume calibration concepts relevant to gravimetric pump calibration and uncertainty.

### R38 — Evaluation of measurement data — Guide to the expression of uncertainty in measurement

Joint Committee for Guides in Metrology (2008). JCGM 100:2008.
URL: https://doi.org/10.59161/JCGM100-2008E
Used for: Defines the framework for measurement models, standard uncertainty, combined uncertainty and expanded uncertainty.

### R39 — International vocabulary of metrology — Basic and general concepts and associated terms

Joint Committee for Guides in Metrology (2012). JCGM 200:2012.
URL: https://doi.org/10.59161/JCGM200-2012
Used for: Defines measurement, calibration, accuracy, precision, repeatability, resolution and related terms used throughout the documentation.

### R40 — ISO 8589:2007 Sensory analysis — General guidance for the design of test rooms

International Organization for Standardization (2007). ISO.
URL: https://www.iso.org/standard/36385.html
Used for: Supports controlled sensory-test spaces and separation of sample preparation from evaluation.

### R41 — ISO 13299:2016 Sensory analysis — Methodology — General guidance for establishing a sensory profile

International Organization for Standardization (2016). ISO.
URL: https://www.iso.org/standard/58042.html
Used for: Supports systematic development of sensory attributes and intensity profiles.

### R42 — ISO 11136:2014 Sensory analysis — Methodology — General guidance for conducting hedonic tests with consumers in a controlled area

International Organization for Standardization (2014). ISO.
URL: https://www.iso.org/standard/50125.html
Used for: Supports consumer liking and preference testing after analytical difference testing.

### R43 — ISO/IEC 17025:2017 General requirements for the competence of testing and calibration laboratories

International Organization for Standardization (2017). ISO/IEC.
URL: https://www.iso.org/standard/66912.html
Used for: Supports traceability, method validation, equipment control, records and competence for external laboratory measurements.

### R44 — A4988 DMOS Microstepping Driver with Translator and Overcurrent Protection

Allegro MicroSystems (2010). Official component documentation.
URL: https://www.allegromicro.com/en/products/motor-drivers/brush-dc-motor-drivers/a4988
Used for: Confirms supported microstep command modes and electrical protection features of the proposed driver.

### R45 — ISO/CIE 11664-4:2019 Colorimetry — Part 4: CIE 1976 L*a*b* colour space

International Organization for Standardization; International Commission on Illumination (2019). International Standard.
URL: https://www.iso.org/standard/74166.html
Used for: Defines calculation of CIE L*a*b* coordinates and colour differences for controlled instrumental colour reporting.

### R46 — NIST/SEMATECH e-Handbook of Statistical Methods

Heckert, N. Alan; Filliben, James J.; Croarkin, C. M.; Hembree, B.; Guthrie, William F.; Tobias, P.; Prinz, J. (2002). NIST Handbook 151.
URL: https://www.nist.gov/publications/handbook-151-nistsematech-e-handbook-statistical-methods
Used for: Supports calibration regression, residual analysis, experimental design and statistical process control.

### R47 — Extensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments

Privitera, Davide; Mecocci, Alessandro; Bartolini, Sandro (2025). Scientific Data, 12, 1618.
URL: https://doi.org/10.1038/s41597-025-05902-z
Used for: Supports repeated gravimetric characterisation, drift monitoring and data-driven compensation for peristaltic dosing.

### R48 — ISO 5492:2008 Sensory analysis — Vocabulary

International Organization for Standardization (2008). International Standard; Amendment 1 published 2016.
URL: https://www.iso.org/standard/38051.html
Used for: Provides controlled sensory-analysis terminology for taste, odour, texture, attributes and methods.

### R49 — ISO 8586:2023 Sensory analysis — Selection and training of sensory assessors

International Organization for Standardization (2023). International Standard.
URL: https://www.iso.org/standard/76667.html
Used for: Defines criteria and procedures for selecting and training trained and expert sensory assessors.

### R50 — ISO 5725-2:2025 Accuracy (trueness and precision) of measurement methods and results — Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method

International Organization for Standardization (2025). ISO.
URL: https://www.iso.org/standard/90054.html
Used for: Supports estimation of repeatability and reproducibility of measurement methods.

### R51 — PlantDoc: A Dataset for Visual Plant Disease Detection

Singh, Davinder; Jain, Naman; Jain, Pranjali; Kayal, Pratik; Kumawat, Sudhakar; Batra, Nipun (2020). Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, 249–253.
URL: https://doi.org/10.1145/3371158.3371196
Used for: Provides a 2,598-image, 13-species, 27-class plant-disease dataset with real backgrounds and documents the gap between controlled and field-like imagery.

### R52 — Quantitative monitoring of Arabidopsis thaliana growth and development using high-throughput plant phenotyping

Arend, Daniel; Lange, Matthias; Pape, Jean-Michel; Weigelt-Fischer, Kathleen; Arana-Ceballos, Fernando; Mücke, Ingo; Klukas, Christian; Altmann, Thomas; Scholz, Uwe; Junker, Astrid (2016). Scientific Data, 3, 160055.
URL: https://doi.org/10.1038/sdata.2016.55
Used for: Documents repeatable top and side RGB acquisition, image-linked metadata, reference measurements and quality validation for longitudinal plant phenotyping.

### R53 — ImageNet: A Large-Scale Hierarchical Image Database

Deng, Jia; Dong, Wei; Socher, Richard; Li, Li-Jia; Li, Kai; Fei-Fei, Li (2009). 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255.
URL: https://doi.org/10.1109/CVPR.2009.5206848
Used for: Defines the large-scale natural-image dataset used to initialise the generic visual encoder in the documented transfer-learning baseline.

### R54 — Albumentations: Fast and Flexible Image Augmentations

Buslaev, Alexander; Iglovikov, Vladimir I.; Khvedchenya, Eugene; Parinov, Alex; Druzhinin, Mikhail; Kalinin, Alexandr A. (2020). Information, 11(2), 125.
URL: https://doi.org/10.3390/info11020125
Used for: Provides the reproducible image-transform framework used to define and log bounded training-only augmentations.

### R55 — On Calibration of Modern Neural Networks

Guo, Chuan; Pleiss, Geoff; Sun, Yu; Weinberger, Kilian Q. (2017). Proceedings of the 34th International Conference on Machine Learning, PMLR 70, 1321–1330.
URL: https://proceedings.mlr.press/v70/guo17a.html
Used for: Defines confidence calibration analysis and supports temperature scaling as a simple post-hoc calibration method.

### R56 — Quantifying the reliability gap in cross-domain plant disease classification: benchmarking the limited efficacy of standard mitigation techniques under controlled-to-field shift

Xiang, Kun; Shi, Danxi; Zhu, Xiangbo (2026). Frontiers in Plant Science, 17, 1826962.
URL: https://doi.org/10.3389/fpls.2026.1826962
Used for: Quantifies controlled-to-field domain shift using PlantVillage and PlantDoc and supports explicit target-domain evaluation and calibrated abstention.

### R57 — Lettuce Dataset with RGB Canopy Images, Biomass, Nutrient Solution, and Environmental Variables for Machine Learning Model Development

Karimzadeh, Sara; Ahamed, M. Shamim (2025). Zenodo, Version 1.
URL: https://doi.org/10.5281/zenodo.16912088
Used for: Links 731 daily RGB canopy images, 540 non-destructive biomass observations from 18 identified lettuce heads, and 1,443 environmental records for longitudinal growth analysis.

### R58 — Hydroponic Cultivation of Bibb Lettuce in Nitrogen Phosphorus Potassium (NPK)-Limited Conditions

Sharkey, Andrew; Chen, Yongsheng; Altman, Asher (2025). USDA Ag Data Commons, Version 2.
URL: https://doi.org/10.15482/USDA.ADC/28801286.v2
Used for: Provides time-resolved fresh-mass responses and published confidence intervals for separate nitrogen, phosphorus and potassium limitation treatments in hydroponic Bibb lettuce.

### R59 — Dataset on the organic acids, sulphate, total nitrogen and total chlorophyll contents of two lettuce cultivars grown hydroponically using nutrient solutions of variable macrocation ratios

El-Nakhel, Christophe; Pannico, Antonio; Kyriacou, Marios C.; Petropoulos, Spyridon A.; Giordano, Maria; Colla, Giuseppe; Troise, Antonio Dario; Vitaglione, Paola; De Pascale, Stefania; Rouphael, Youssef (2020). Data in Brief, 29, 105135.
URL: https://doi.org/10.1016/j.dib.2020.105135
Used for: Reports all 18 observations in a balanced two-cultivar by three-solution experiment, including nitrogen, sulphate, organic acids and total chlorophyll.

### R60 — Integrating thermal infrared and RGB imaging for early detection of water stress in lettuces with comparative analysis of IoT sensors

Fevgas, Georgios; Lagkas, Thomas; Papadopoulos, Petros; Sarigiannidis, Panagiotis; Argyriou, Vasileios (2025). Smart Agricultural Technology, 10, 100881.
URL: https://doi.org/10.1016/j.atech.2025.100881
Used for: Aligns hourly soil-moisture measurements with RGB, thermal and pseudo-colour lettuce images from irrigated and non-irrigated conditions.

### R61 — HydroGrowNet of Batavia Dataset

Shalash, Omar; Hassan, Nayira; Métwalli, Ahmed; Elhefny, Alia (2025). Mendeley Data, Version 5.
URL: https://doi.org/10.17632/g6cm3v3wdp.5
Used for: Contains more than 390,000 segmented lettuce images from three 30-day cycles aligned with water temperature, EC and pH measurements; useful for external growth and anomaly-model evaluation.

### R62 — Multi-Sensor High-Throughput Phenotyping Dataset of Hydroponic Lettuce under Variable Fertigation Conditions

Rodrigues, Leandro; Terra, Francisco; Rodrigues, Pedro; Moreira, Germano; Oliveira, Francisco; Moura, Pedro; Pinheiro, Isabel; Santos, Filipe; Cunha, Mário (2026). Zenodo.
URL: https://doi.org/10.5281/zenodo.20759414
Used for: Follows 45 lettuce plants from two cultivars through a 42-day crop cycle under three nitrogen concentrations and two irrigation rates, linking RGB, 3D, multispectral, SPAD, fluorescence and morphology records.

### R63 — Gravity sensing and signal conversion in plant gravitropism

Nakamura, Moritaka; Nishimura, Takeshi; Morita, Miyo Terao (2019). Journal of Experimental Botany, 70(14), 3495–3506.
URL: https://doi.org/10.1093/jxb/erz158
Used for: Explains amyloplast sedimentation, gravity signalling, directional auxin transport and differential growth in roots and shoots after reorientation.

### R64 — Probing the Reproducibility of Leaf Growth and Molecular Phenotypes: A Comparison of Three Arabidopsis Accessions Cultivated in Ten Laboratories

Massonnet, Catherine; Vile, Denis; Fabre, Justine; et al. (2010). Plant Physiology, 152(4), 2142–2157.
URL: https://doi.org/10.1104/pp.109.148338
Used for: Shows that detailed standardisation can produce similar growth in a core group of laboratories, while small laboratory-environment differences can still alter growth and metabolite phenotypes.

### R65 — The importance of independent replication of treatments in plant science

Rogers, Alistair; Dietz, Karl-Josef; Gifford, Miriam L.; Lunn, John E. (2021). Journal of Experimental Botany, 72(15), 5270–5274.
URL: https://doi.org/10.1093/jxb/erab268
Used for: Explains independent experimental units, randomisation and why pseudoreplication can invalidate treatment claims.

### R66 — Getting back to nature: a reality check for experiments in controlled environments

Annunziata, Maria Grazia; Apelt, Federico; Carillo, Petronia; et al. (2017). Journal of Experimental Botany, 68(16), 4463–4477.
URL: https://doi.org/10.1093/jxb/erx220
Used for: Demonstrates that natural sunlight and artificial controlled-light regimes can produce different metabolic profiles even when important lighting features are approximated.

### R67 — Importance of measuring and reporting environmental conditions across plant science subdisciplines

Vincent, Christopher; Leisner, Courtney P.; Locke, Anna M.; Teshome, Demissew Tesfaye; et al. (2025). Plant Physiology, 199(2), kiaf405.
URL: https://doi.org/10.1093/plphys/kiaf405
Used for: Supports measuring actual environmental conditions—rather than reporting equipment settings alone—to improve replicability and cross-scale interpretation.

### R68 — Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation

Heuermann, Marc C.; Knoch, Dominic; Junker, Astrid; Altmann, Thomas (2023). Nature Communications, 14, 5783.
URL: https://doi.org/10.1038/s41467-023-41332-4
Used for: Shows the value of reproducing time-varying environmental trajectories rather than only static averages when bridging controlled and natural conditions.

### R69 — Dynamic, adaptive and modular Digital Twin framework for resource-efficient Controlled Environment Agriculture

Frontzek, Julius; Wagner, Zühal; Streif, Stefan (2026). Frontiers in Plant Science, 17, 1864757.
URL: https://doi.org/10.3389/fpls.2026.1864757
Used for: Supports vendor-agnostic sensor and actuator integration, dynamic target trajectories, model calibration and adaptive control as an architecture for translating targets to local equipment.

### R70 — A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability

Schuirmann, Donald J. (1987). Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680.
URL: https://doi.org/10.1007/BF01068419
Used for: Provides the classical two one-sided tests framework for evaluating whether an effect lies within predeclared equivalence margins.

### R71 — Optimizing the quality of horticultural crop: insights into pre-harvest practices in controlled environment agriculture

Zhao, Xinyi; Peng, Jie; Zhang, Li; et al. (2024). Frontiers in Plant Science, 15, 1427471.
URL: https://doi.org/10.3389/fpls.2024.1427471
Used for: Reviews how pre-harvest manipulation of nutrients, light and other controlled-environment factors can affect horticultural quality, including colour, aroma and taste-related outcomes.

### R72 — Sensor-controlled fertigation management for higher yield and quality in greenhouse hydroponic strawberries

Hutchinson, George Kerrigan; Nguyen, Lan Xuan; Rubio Ames, Zilfina; Nemali, Krishna; Ferrarezi, Rhuanito Soranz (2025). Frontiers in Plant Science, 15, 1469434.
URL: https://doi.org/10.3389/fpls.2024.1469434
Used for: Demonstrates that sensor-coupled fertigation strategies can be evaluated for yield, quality and resource use in greenhouse hydroponic strawberry production.

### R73 — ISO 8586:2023 Sensory analysis — Selection and training of sensory assessors

International Organization for Standardization (2023). International Standard.
URL: https://www.iso.org/standard/76667.html
Used for: Defines criteria and procedures for selecting and training trained and expert sensory assessors.

### R74 — ISO 8589:2007 Sensory analysis — General guidance for the design of test rooms

International Organization for Standardization (2007). International Standard, with Amendment 1:2014.
URL: https://www.iso.org/standard/36385.html
Used for: Provides guidance for sensory test-room design and separation of testing, preparation and support areas.

### R75 — ISO 6658:2017 Sensory analysis — Methodology — General guidance

International Organization for Standardization (2017). International Standard.
URL: https://www.iso.org/standard/65519.html
Used for: Provides general guidance on sensory tests and the statistical treatment of sensory-analysis results.

### R76 — ISO 5492:2008 Sensory analysis — Vocabulary

International Organization for Standardization (2008). International Standard, with Amendment 1:2016.
URL: https://www.iso.org/standard/38051.html
Used for: Defines sensory-analysis terminology relating to senses, organoleptic attributes and methods.

### R77 — ISO 11136:2014 Sensory analysis — Methodology — General guidance for conducting hedonic tests with consumers in a controlled area

International Organization for Standardization (2014). International Standard, with Amendment 1:2020.
URL: https://www.iso.org/standard/50125.html
Used for: Provides guidance for measuring consumer liking and preference under controlled conditions.

### R78 — How well do you know your growth chambers? Testing for chamber effect using plant traits

Porter, Amanda S.; Evans-Fitz.Gerald, Christiana; McElwain, Jennifer C.; Yiotis, Charilaos; Elliott-Kingston, Caroline (2015). Plant Methods, 11, 44.
URL: https://doi.org/10.1186/s13007-015-0088-0
Used for: Demonstrates that nominally identical controlled-environment chambers can produce chamber effects and supports independent sensing, pilot testing, randomisation and replicated experimental units.

### R79 — A spatio-temporal methodology for greenhouse microclimatic mapping

Brentarolli, Elia; Locatelli, Silvia; Nicoletto, Carlo; Sambo, Paolo; Quaglia, Davide; Muradore, Riccardo (2024). PLOS ONE, 19(9), e0310454.
URL: https://doi.org/10.1371/journal.pone.0310454
Used for: Supports temporary dense sensing, spatial modelling and greenhouse microclimate mapping when a single environmental average is insufficient.