A genotype-specific programme for pH, nutrient strength, root-zone temperature and short pre-harvest flavour treatments.
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.
\n\n\nThe 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.
\nThe 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.
\nEach 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.
\n\n| 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 |
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.
\nThe 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.
\nHydroGrowNet 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.
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.
\nLettuce responses are strongly cultivar-dependent. Every trial names the cultivar and does not combine cultivars as interchangeable replicates.
\n| 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 | tested pH 5.0–6.5; separates pH and alkalinity |
Use a named cultivar and treatment strengths derived from or rather than a universal 1.2–1.6 mS/cm statement. In , growth response differed between the tested lettuce and basil cultivars; showed functional-metabolite responses were genotype-dependent.
\nA confirmatory experiment can test the combined pre-harvest nitrogen limitation and controlled-light treatment reported by . Primary outcomes should include sensory sweetness/bitterness and the chemical variables used in the source study.
\nRoot-zone temperature is explicitly controlled because found cultivar-dependent effects on growth and °Brix. °Brix is reported as an instrumental endpoint, not automatically as perceived sweetness.
","text":"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]","source_ids":["R06","R07","R08","R09","R10","R11","R27","R57","R61","R62"],"visuals":[{"id":"aalto-canopy-progression","type":"interactive longitudinal source-image gallery","title":"The same controlled cultivation study observed through time","article":"lettuce","source_ids":["R57"],"data":"/research/datasets/lettuce-growth-aalto/summary.json","generated_by":"scripts/analyze-plant-monitoring-evidence.py"},{"id":"aalto-growth-trajectories","type":"interactive mean and observed-range time-series chart","title":"Fresh-biomass trajectory across 18 identified lettuce heads","article":"lettuce","source_ids":["R57"],"data":"/research/datasets/lettuce-growth-aalto/growth-chart.json","generated_by":"scripts/analyze-plant-monitoring-evidence.py"},{"id":"aalto-forecast-comparison","type":"interactive model-error and confidence-interval chart","title":"Three-day biomass forecast tested on plants withheld from fitting","article":"lettuce","source_ids":["R57"],"csv":"/research/datasets/lettuce-growth-aalto/forecast-predictions.csv","generated_by":"scripts/analyze-plant-monitoring-evidence.py"},{"id":"r11-temperature-profile","type":"interactive dual-axis line chart","title":"Lettuce response to root-zone temperature","article":"lettuce","source_ids":["R11"],"csv":"/research/data/derived/r11-lettuce-temperature-table-3.csv","exact_location":"Table 3, PDF page 6"}],"sources":[{"id":"R06","authors":"Kudirka, Gediminas; Viršilė, Akvilė; Sutulienė, Rūta; Laužikė, Kristina; Samuolienė, Giedrė","year":2023,"title":"Precise Management of Hydroponic Nutrient Solution pH: The Effects of Minor pH Changes and MES Buffer Molarity on Lettuce Physiological Properties","publication":"Horticulturae","doi":"10.3390/horticulturae9070837","source_type":"peer-reviewed research","relevance":"Provides controlled lettuce evidence across pH 5.0–6.5 and demonstrates that small root-zone pH changes affect physiology.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.3390/horticulturae9070837","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R07","authors":"Anderson, T. S.; Martini, M. R.; de Villiers, D.; Timmons, M. B.","year":2017,"title":"Growth and Tissue Elemental Composition Response of Butterhead Lettuce (Lactuca sativa, cv. Flandria) to Hydroponic Conditions at Different pH and Alkalinity","publication":"Horticulturae","doi":"10.3390/horticulturae3030041","source_type":"peer-reviewed research","relevance":"Supports separating pH from alkalinity and measuring tissue composition in lettuce.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.3390/horticulturae3030041","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R08","authors":"Hosseini, Hadis; Mozafari, Vahid; Roosta, Hamid Reza; Shirani, Hossein; van de Vlasakker, Paulien C. H.; Farhangi, Mohsen","year":2021,"title":"Nutrient Use in Vertical Farming: Optimal Electrical Conductivity of Nutrient Solution for Growth of Lettuce and Basil in Hydroponic Cultivation","publication":"Horticulturae","doi":"10.3390/horticulturae7090283","source_type":"peer-reviewed research","relevance":"Provides cultivar-specific EC response data for hydroponic lettuce and basil.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.3390/horticulturae7090283","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R09","authors":"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","year":2020,"title":"The Strength of the Nutrient Solution Modulates the Functional Profile of Hydroponically Grown Lettuce in a Genotype-Dependent Manner","publication":"Foods","doi":"10.3390/foods9091156","source_type":"peer-reviewed research","relevance":"Demonstrates genotype-dependent changes in lettuce functional metabolites with nutrient-solution strength.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.3390/foods9091156","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R10","authors":"Yang, Xiao; Hu, Jiangtao; Wang, Zheng; Huang, Tao; Xiang, Yuting; Zhang, Li; Peng, Jie; Tomas-Barberan, Francisco A.; Yang, Qichang","year":2023,"title":"Pre-harvest Nitrogen Limitation and Continuous Lighting Improve the Quality and Flavor of Lettuce (Lactuca sativa L.) under Hydroponic Conditions in Greenhouse","publication":"Journal of Agricultural and Food Chemistry","doi":"10.1021/acs.jafc.2c07420","source_type":"peer-reviewed research","relevance":"Supports a confirmatory lettuce trial combining a defined pre-harvest nitrogen treatment with controlled lighting and sensory/chemical endpoints.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.1021/acs.jafc.2c07420","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R11","authors":"Thakulla, Dharti; Dunn, Bruce; Hu, Bizhen; Goad, Carla; Maness, Niels","year":2021,"title":"Nutrient Solution Temperature Affects Growth and °Brix Parameters of Seventeen Lettuce Cultivars Grown in an NFT Hydroponic System","publication":"Horticulturae","doi":"10.3390/horticulturae7090321","source_type":"peer-reviewed research","relevance":"Supports measuring root-zone temperature and cultivar interaction rather than treating temperature as a background variable.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.3390/horticulturae7090321","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R27","authors":"El-Nakhel, C. et al.","year":2019,"title":"The bioactive profile of lettuce produced in a closed soilless system as configured by combinatorial effects of genotype and macrocation supply composition","publication":"Food Chemistry","doi":"10.1016/j.foodchem.2019.125713","source_type":"peer-reviewed research","relevance":"Supports factorial macrocation-by-genotype experiments in closed soilless lettuce production.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.1016/j.foodchem.2019.125713","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R57","authors":"Karimzadeh, Sara; Ahamed, M. Shamim","year":2025,"title":"Lettuce Dataset with RGB Canopy Images, Biomass, Nutrient Solution, and Environmental Variables for Machine Learning Model Development","publication":"Zenodo, Version 1","doi":"10.5281/zenodo.16912088","url":"https://doi.org/10.5281/zenodo.16912088","source_type":"longitudinal experimental dataset","relevance":"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.","verification":"Zenodo archive, Aalto University metadata record, workbook structure, file hashes and licence checked 2026-07-29","verified_on":"2026-07-29","verification_status":"PRIMARY DATASET AND FILES CHECKED","verified_against":"Zenodo and Aalto University Research Portal"},{"id":"R61","authors":"Shalash, Omar; Hassan, Nayira; Métwalli, Ahmed; Elhefny, Alia","year":2025,"title":"HydroGrowNet of Batavia Dataset","publication":"Mendeley Data, Version 5","doi":"10.17632/g6cm3v3wdp.5","url":"https://doi.org/10.17632/g6cm3v3wdp.5","source_type":"multimodal longitudinal dataset","relevance":"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.","verification":"Mendeley Data version record, data-composition file and licence checked 2026-07-29","verified_on":"2026-07-29","verification_status":"PRIMARY DATASET METADATA CHECKED","verified_against":"Mendeley Data"},{"id":"R62","authors":"Rodrigues, Leandro; Terra, Francisco; Rodrigues, Pedro; Moreira, Germano; Oliveira, Francisco; Moura, Pedro; Pinheiro, Isabel; Santos, Filipe; Cunha, Mário","year":2026,"title":"Multi-Sensor High-Throughput Phenotyping Dataset of Hydroponic Lettuce under Variable Fertigation Conditions","publication":"Zenodo","doi":"10.5281/zenodo.20759414","url":"https://doi.org/10.5281/zenodo.20759414","source_type":"multimodal longitudinal phenotyping dataset","relevance":"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.","verification":"Zenodo metadata, file inventory, experimental factors and licence record checked 2026-07-29","verified_on":"2026-07-29","verification_status":"PRIMARY DATASET METADATA CHECKED","verified_against":"Zenodo"}]}