{"@context":"https://schema.org","@type":"TechArticle","id":"FLV-STR-001","slug":"salinity-water-stress","canonical_url":"https://flavorotor.com/research/salinity-water-stress","machine_readable_url":"https://flavorotor.com/research/data/chapters/salinity-water-stress.json","markdown_url":"https://flavorotor.com/research/markdown/salinity-water-stress","title":"Salinity, water stress and multimodal detection","description":"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.","chapter":"Flavour control","version":"2.0","updated":"2026-07-29","table_of_contents":[{"id":"plain","label":"Explanation"},{"id":"model","label":"Response model"},{"id":"water-stress-sequence","label":"Measured water-stress sequence"},{"id":"separating-water-and-nutrient-signals","label":"Separating water, nutrient and disease signals"},{"id":"stress-timing","label":"Time to detection"},{"id":"protocol","label":"FlavoRotor rule"}],"html":"
In brief

How concentration effects, osmotic stress, biomass penalties and metabolite responses are separated before any flavour claim.

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Explanation

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A treatment can increase the concentration of a compound per gram while reducing total plant growth. Both outcomes must be reported.

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Response model

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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.
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Mint species show species-dependent essential-oil and antioxidant responses under salinity, accompanied by growth effects.

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Arugula EC trials report simultaneous changes in growth, nutritional quality and flavour-related phytochemicals, demonstrating why yield and chemistry must be analysed together.

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Measured water-stress sequence

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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).

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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.

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Separating water, nutrient and disease signals

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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.

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Time to detection

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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

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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.

\n","text":"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.","source_ids":["R05","R15","R60"],"visuals":[{"id":"lettuce-water-stress-signals","type":"interactive aligned sensor and image-derived time-series chart","title":"Water availability and image-derived stress signals change on different scales","article":"salinity-water-stress","source_ids":["R60"],"data":"/research/datasets/lettuce-water-stress/chart-data.json","generated_by":"scripts/analyze-plant-monitoring-evidence.py"},{"id":"lettuce-water-stress-images","type":"interactive published pseudo-colour image gallery","title":"Published pseudo-colour outputs for irrigated and non-irrigated lettuce","article":"salinity-water-stress","source_ids":["R60"],"data":"/research/datasets/lettuce-water-stress/summary.json","generated_by":"scripts/analyze-plant-monitoring-evidence.py"}],"sources":[{"id":"R05","authors":"Yang, Teng; Samarakoon, Uttara C.; Altland, James; Ling, Peter","year":2021,"title":"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","publication":"Agronomy","doi":"10.3390/agronomy11071340","source_type":"peer-reviewed research","relevance":"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.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.3390/agronomy11071340","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R15","authors":"Hosseini, Seyyed Jaber; Tahmasebi-Sarvestani, Zeinolabedin; Mokhtassi-Bidgoli, Ali; Keshavarz, Hamed; Kazemi, Shahryar; Khalvandi, Masoumeh; Pirdashti, Hematollah; Hashemi-Petroudi, Seyyed Hamidreza; Nicola, Silvana","year":2023,"title":"Functional Quality, Antioxidant Capacity and Essential Oil Percentage in Different Mint Species Affected by Salinity Stress","publication":"Chemistry & Biodiversity","doi":"10.1002/cbdv.202200247","source_type":"peer-reviewed research","relevance":"Supports species-specific mint screening for salinity, essential-oil percentage and biomass trade-offs.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.1002/cbdv.202200247","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R60","authors":"Fevgas, Georgios; Lagkas, Thomas; Papadopoulos, Petros; Sarigiannidis, Panagiotis; Argyriou, Vasileios","year":2025,"title":"Integrating thermal infrared and RGB imaging for early detection of water stress in lettuces with comparative analysis of IoT sensors","publication":"Smart Agricultural Technology, 10, 100881","doi":"10.1016/j.atech.2025.100881","url":"https://doi.org/10.1016/j.atech.2025.100881","source_type":"peer-reviewed research and multimodal dataset","relevance":"Aligns hourly soil-moisture measurements with RGB, thermal and pseudo-colour lettuce images from irrigated and non-irrigated conditions.","verification":"Publisher paper, Mendeley dataset 10.17632/294zk6k5wf.2, sensor CSV and published image outputs checked 2026-07-29","verified_on":"2026-07-29","verification_status":"DOI, PRIMARY PAPER AND DATASET CHECKED","verified_against":"Elsevier Smart Agricultural Technology and Mendeley Data"}]}