Early detection of Chickpea Ascochyta Blight using Hyperspectral imaging Coupled with Machine learning

Early detection of Chickpea Ascochyta Blight using Hyperspectral imaging Coupled with Machine learning | 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 Early detection of Chickpea Ascochyta Blight using Hyperspectral imaging Coupled with Machine learning Mohamed ARAME, Issam Meftah Kadmiri, Oussama Elbaraghi, Francois Bourzeix, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9186587/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Fungal diseases such as Ascochyta pose major threats to chickpea production, causing significant losses if not detected early. Conventional diagnostic methods, including visual inspection and molecular assays, are often time-consuming, subjective, and ineffective for early detection of infection. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning for early, non-destructive detection of Ascochyta blight in chickpea leaves, an application that remains underexplored in previous research. Hyperspectral data in the 400–1000 nm range were acquired under controlled laboratory conditions from artificially infected chickpea plants. In this study, we developed a new comprehensive processing pipeline to address critical challenges associated with hyperspectral data, including noise, artifacts, and illumination variations. Subsequently, unsupervised learning approaches, such as K-means clustering, were employed to construct a clean, well-labeled database of mean leaf spectra. Using this refined dataset, we evaluated a classification framework based on supervised learning models, leveraging selected vegetation indices, visible and infrared spectral bands, along with features derived from statistical analyses. The proposed approach achieved an overall classification accuracy exceeding 95% in distinguishing healthy chickpea plants from those infected with Ascochyta blight. Results demonstrate that HSI can capture subtle physiological changes in leaves before visible symptoms appear, offering a reliable and scalable tool for precision agriculture. This study contributes a promising step toward AI-powered early disease detection in chickpea farming, enabling timely interventions, reducing fungicide use, and supporting sustainable crop protection strategies. Future work will focus on real-world deployment and cost-effective integration into existing monitoring systems. Chickpea Ascochyta blight Hyperspectral imaging Early disease detection Machine learning Precision agriculture Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 26 Mar, 2026 Editor invited by journal 24 Mar, 2026 Editor assigned by journal 24 Mar, 2026 First submitted to journal 21 Mar, 2026 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-9186587","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612437073,"identity":"1c5e492a-c16d-45b2-8406-2be807365655","order_by":0,"name":"Mohamed ARAME","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFACHhDBDOVUMDDwk6jlDAODZANJWhjbGBgMCGnRbe89+OlGhTUD/+zDTzd8nHc42kC6gXXDDzxazM6cS5bOOZPOIHEuzezmzG2Hc7fLHGC72YNPy40cA+nctsMgb5jd5gVq2Tkjge0GD34txr9z/x1mkD/D/u323zmHczfcSGC7+Qe/FjPp3IbDDAZneMxuMzZAtNzGa8uZM2bWOcfSeQzP8JTd7DmWnjtzzsG22zL4tBzvMb6dU2MtJ3eGfduNHzXWuf3SzcduvsGjBQaQXCLB2ECEBhQgQaqGUTAKRsEoGO4AAAidVp5q4+0iAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0004-0785-9309","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"ARAME","suffix":""},{"id":612437074,"identity":"3c1ea485-aadc-4c71-859c-18cfdab80ac9","order_by":1,"name":"Issam Meftah Kadmiri","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Issam","middleName":"Meftah","lastName":"Kadmiri","suffix":""},{"id":612437075,"identity":"93222183-b413-4976-9fdd-015e28b9adfd","order_by":2,"name":"Oussama Elbaraghi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Oussama","middleName":"","lastName":"Elbaraghi","suffix":""},{"id":612437076,"identity":"61c884f0-54eb-4b42-a997-f0b2a91ce4d7","order_by":3,"name":"Francois Bourzeix","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Francois","middleName":"","lastName":"Bourzeix","suffix":""},{"id":612437077,"identity":"ee6edbda-3ea2-474c-a4c4-7533d8d1d8e2","order_by":4,"name":"Yahya Zennayi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yahya","middleName":"","lastName":"Zennayi","suffix":""},{"id":612437078,"identity":"1f52f67e-7c2a-4ea9-b330-f9ae25d0c594","order_by":5,"name":"Ilyass Maafa","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ilyass","middleName":"","lastName":"Maafa","suffix":""},{"id":612437079,"identity":"adc3d4c9-563f-422d-9539-cda5e8cdf29e","order_by":6,"name":"Abdelghani Chehbouni","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Abdelghani","middleName":"","lastName":"Chehbouni","suffix":""}],"badges":[],"createdAt":"2026-03-21 14:55:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9186587/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9186587/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105567377,"identity":"07bbf488-4ae9-433d-b791-b2fd8e4bfeb0","added_by":"auto","created_at":"2026-03-27 12:59:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1126602,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleJournalofPlantDiseasesandProtection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9186587/v1_covered_1f50791d-ec8a-4254-87a2-2b6fd91ea0e3.pdf"}],"financialInterests":"","formattedTitle":"Early detection of Chickpea Ascochyta Blight using Hyperspectral imaging Coupled with Machine learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-plant-diseases-and-protection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpdp","sideBox":"Learn more about [Journal of Plant Diseases and Protection](https://www.springer.com/journal/41348)","snPcode":"41348","submissionUrl":"https://www.editorialmanager.com/jpdp","title":"Journal of Plant Diseases and Protection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Chickpea, Ascochyta blight, Hyperspectral imaging, Early disease detection, Machine learning, Precision agriculture","lastPublishedDoi":"10.21203/rs.3.rs-9186587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9186587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Fungal diseases such as Ascochyta pose major threats to chickpea production, causing significant losses if not detected early. Conventional diagnostic methods, including visual inspection and molecular assays, are often time-consuming, subjective, and ineffective for early detection of infection. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning for early, non-destructive detection of Ascochyta blight in chickpea leaves, an application that remains underexplored in previous research. Hyperspectral data in the 400–1000 nm range were acquired under controlled laboratory conditions from artificially infected chickpea plants. In this study, we developed a new comprehensive processing pipeline to address critical challenges associated with hyperspectral data, including noise, artifacts, and illumination variations. Subsequently, unsupervised learning approaches, such as K-means clustering, were employed to construct a clean, well-labeled database of mean leaf spectra. Using this refined dataset, we evaluated a classification framework based on supervised learning models, leveraging selected vegetation indices, visible and infrared spectral bands, along with features derived from statistical analyses. The proposed approach achieved an overall classification accuracy exceeding 95% in distinguishing healthy chickpea plants from those infected with Ascochyta blight. Results demonstrate that HSI can capture subtle physiological changes in leaves before visible symptoms appear, offering a reliable and scalable tool for precision agriculture. This study contributes a promising step toward AI-powered early disease detection in chickpea farming, enabling timely interventions, reducing fungicide use, and supporting sustainable crop protection strategies. Future work will focus on real-world deployment and cost-effective integration into existing monitoring systems.","manuscriptTitle":"Early detection of Chickpea Ascochyta Blight using Hyperspectral imaging Coupled with Machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 10:04:32","doi":"10.21203/rs.3.rs-9186587/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-07T15:32:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T06:39:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Journal of Plant Diseases and Protection","date":"2026-03-24T16:38:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T16:14:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Plant Diseases and Protection","date":"2026-03-21T10:53:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-plant-diseases-and-protection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpdp","sideBox":"Learn more about [Journal of Plant Diseases and Protection](https://www.springer.com/journal/41348)","snPcode":"41348","submissionUrl":"https://www.editorialmanager.com/jpdp","title":"Journal of Plant Diseases and Protection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6b8db0aa-df8b-4b57-b592-6406bc4e828d","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T10:04:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 10:04:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9186587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9186587","identity":"rs-9186587","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}