Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model

Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model Mohamed S. Abd El-baki, Mohamed M Ibrahim, Salah Elsayed, Nadia G. Abd El-Fattah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4379462/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Water stress is a global challenge that severely impacts crop production by hindering essential processes such as nutrient uptake, photosynthesis, and respiration. To address this issue, proximal sensing has emerged as a promising technique for detecting stress in plants. By utilizing remote sensing and non-destructive methods, early and spatial identification of stress in vegetable crops becomes possible, enabling timely management interventions and optimizing yield in precision farming. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels, growth stages, and growing seasons. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to decreased FB, DB, CWC, SMC, and tomato yield. The study also revealed significant differences in RGB image indices between different irrigation levels, with lower values observed under severe stress treatment. The majority of RGB image indices incorporating the green component demonstrated strong positive relationships, with R 2 ranging between 0.52 and 0.94 for FB, 0.49 and 0.92 for DB, 0.44 and 0.85 for CWC, 0.29 and 0.82 for SPAD, 0.27 and 0.74 for SMC, and 0.42 and 0.89 for tomato yield. Notably, we did not observe a significant correlation between any of the RGB image indices and SPAD during the combined data of both stages. However, the red-blue simple ratio (RB) index, which does not consider the green component (G), did not significantly correlate with any of the plant traits. The ANN models utilizing RGB image indices achieved high prediction accuracy, as indicated by R 2 values ranging from 0.84 to 0.99 for FB, 0.88 to 0.98 for DB, 0.81 to 0.97 for CWC, 0.67 to 0.98 for SPAD, 0.55 to 0.81 for SMC, and 0.83 to 0.96 for tomato yield. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under conditions of limited water availability for irrigation. Deficit irrigation Image-based indices RGB sensor RGB digital camera Remote sensing techniques Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4379462","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":302335288,"identity":"855cf2e1-5a2f-451d-8194-52bdaf0785c6","order_by":0,"name":"Mohamed S. Abd El-baki","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"S. Abd","lastName":"El-baki","suffix":""},{"id":302335289,"identity":"830d0e7e-aa97-4730-9dbe-db900a0d0275","order_by":1,"name":"Mohamed M Ibrahim","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"M","lastName":"Ibrahim","suffix":""},{"id":302335290,"identity":"d2f43b93-9202-4d24-9739-a6200ab24b67","order_by":2,"name":"Salah Elsayed","email":"data:image/png;base64,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","orcid":"","institution":"University of Sadat City","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Salah","middleName":"","lastName":"Elsayed","suffix":""},{"id":302335291,"identity":"547f44e3-ebb5-43c6-8f06-55864a7b685c","order_by":3,"name":"Nadia G. Abd El-Fattah","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"G. Abd","lastName":"El-Fattah","suffix":""}],"badges":[],"createdAt":"2024-05-07 01:24:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4379462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4379462/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56673979,"identity":"cc6994aa-3165-4ace-b5e6-12d8f5173f17","added_by":"auto","created_at":"2024-05-17 15:32:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1536202,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4379462/v1_covered_322071a5-73b6-4861-8fbe-201a68722381.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deficit irrigation, Image-based indices, RGB sensor, RGB digital camera, Remote sensing techniques, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4379462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4379462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater stress is a global challenge that severely impacts crop production by hindering essential processes such as nutrient uptake, photosynthesis, and respiration. To address this issue, proximal sensing has emerged as a promising technique for detecting stress in plants. By utilizing remote sensing and non-destructive methods, early and spatial identification of stress in vegetable crops becomes possible, enabling timely management interventions and optimizing yield in precision farming. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels, growth stages, and growing seasons. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to decreased FB, DB, CWC, SMC, and tomato yield. The study also revealed significant differences in RGB image indices between different irrigation levels, with lower values observed under severe stress treatment. The majority of RGB image indices incorporating the green component demonstrated strong positive relationships, with R\u003csup\u003e2\u003c/sup\u003e ranging between 0.52 and 0.94 for FB, 0.49 and 0.92 for DB, 0.44 and 0.85 for CWC, 0.29 and 0.82 for SPAD, 0.27 and 0.74 for SMC, and 0.42 and 0.89 for tomato yield. Notably, we did not observe a significant correlation between any of the RGB image indices and SPAD during the combined data of both stages. However, the red-blue simple ratio (RB) index, which does not consider the green component (G), did not significantly correlate with any of the plant traits. The ANN models utilizing RGB image indices achieved high prediction accuracy, as indicated by R\u003csup\u003e2\u003c/sup\u003e values ranging from 0.84 to 0.99 for FB, 0.88 to 0.98 for DB, 0.81 to 0.97 for CWC, 0.67 to 0.98 for SPAD, 0.55 to 0.81 for SMC, and 0.83 to 0.96 for tomato yield. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under conditions of limited water availability for irrigation.\u003c/p\u003e","manuscriptTitle":"Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 18:27:37","doi":"10.21203/rs.3.rs-4379462/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3987bb40-b451-4700-ae5d-75ecb19b45f9","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-17T15:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 18:27:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4379462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4379462","identity":"rs-4379462","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}