# Salinity, water stress and multimodal detection

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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 m c o m p o u n d , p l a n t = c c o m p o u n d , d r y   m a s s   m p l a n t , d r y m_{\mathrm{compound,plant}}=c_{\mathrm{compound,dry\ mass}}\,m_{\mathrm{plant,dry}} m compound , plant ​ = c compound , dry   mass ​ m plant , dry ​ Prevents a concentration increase caused only by reduced biomass from being reported as higher total production.

**Explanation**Concentration alone can rise when a plant becomes smaller. Multiplying by dry mass reveals the total compound produced per plant.

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]

Note. Fevgas et al. (2025), CC BY 4.0. Sensor values and pseudo-colour images are reproduced from the published dataset. The two-plant sequence illustrates measurement alignment and is not used as a population estimate. [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.

Note. Hybrid-framework pseudo-colour images from Fevgas et al. (2025), CC BY 4.0. The fixed yellow-pixel calculation is documented in summary.json. [R60]

### 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.

## References

- [R05] Yang, Teng; Samarakoon, Uttara C.; Altland, James; Ling, Peter (2021). 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. *Agronomy*. https://doi.org/10.3390/agronomy11071340
- [R15] Hosseini, Seyyed Jaber; Tahmasebi-Sarvestani, Zeinolabedin; Mokhtassi-Bidgoli, Ali; Keshavarz, Hamed; Kazemi, Shahryar; Khalvandi, Masoumeh; Pirdashti, Hematollah; Hashemi-Petroudi, Seyyed Hamidreza; Nicola, Silvana (2023). Functional Quality, Antioxidant Capacity and Essential Oil Percentage in Different Mint Species Affected by Salinity Stress. *Chemistry & Biodiversity*. https://doi.org/10.1002/cbdv.202200247
- [R60] Fevgas, Georgios; Lagkas, Thomas; Papadopoulos, Petros; Sarigiannidis, Panagiotis; Argyriou, Vasileios (2025). Integrating thermal infrared and RGB imaging for early detection of water stress in lettuces with comparative analysis of IoT sensors. *Smart Agricultural Technology, 10, 100881*. https://doi.org/10.1016/j.atech.2025.100881