Autonomous Embedded-Vision System for Multistage Detection of Phytopathogenic Fungi in Potato and Tomato Crops UsingConvolutional Neural Networks

Autonomous Embedded-Vision System for Multistage Detection of Phytopathogenic Fungi in Potato and Tomato Crops UsingConvolutional Neural Networks | 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 Article Autonomous Embedded-Vision System for Multistage Detection of Phytopathogenic Fungi in Potato and Tomato Crops UsingConvolutional Neural Networks Gustavo Rafael Rodriguez Inga, Leonardo Javier Pariona Chavez, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8919524/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Current phytopathological diagnostic systems rely on manual inspections or laboratory analyses, which delay early detection and limit in-field responsiveness. Phytopathogenic fungal diseases pose a persistent threat to food security, directly affecting the productivity of essential crops such as potato ( Solanum tuberosum ) and tomato ( Solanum lycopersicum ) [ 1 ]–[ 5 ]. Among these diseases, Phytophthora infestans , the causal agent of late blight, is characterized by its high virulence and rapid spread, capable of generating significant losses in short periods when detection occurs too late [ 3 ], [ 4 ]. To address this issue, a computer vision and deep learning–based system for multistage detection of fungal infections in potato and tomato crops is proposed. The system comprises a convolutional neural network optimized for edge processing and a mobile robotic platform equipped with a manipulator arm for localized treatment application. The developed model was deployed on a Raspberry Pi 4 connected to a 12-MP Raspberry Pi Camera Module 3 NoIR, responsible for acquiring RGB images in the field. The proposed network was compared with reference architectures—ResNet-50, VGG16, MobileNetV2, and Inception-v3—within a four-stage detection pipeline: crop identification, health-state classification, infection diagnosis, and foliar severity estimation. A dataset of 18,200 images obtained from publicly accessible online sources, under diverse lighting and background conditions, was used, partitioned into 70% for training, 20% for validation, and 10% for testing. Preliminary results show an average accuracy in the range of 0.90–0.92, with inference latencies below 60 ms per image, ensuring smooth performance on the Raspberry Pi 4 without requiring cloud connectivity. Additionally, the network demonstrated higher sensitivity to visual variations compared to the baseline models. Biological sciences/Computational biology and bioinformatics Biological sciences/Plant sciences computer vision deep learning agricultural robotics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Editor invited by journal 18 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 14 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-8919524","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":623268308,"identity":"d45a0de4-0d96-4f0a-bbdb-f5f9d85f27a6","order_by":0,"name":"Gustavo Rafael Rodriguez Inga","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3PsWrDMBCA4TMGdVGc9YyoTd9ARkuhfRiJgKcMGTOEtl6UxdmVpc/QKbOLwVn8AIYuNoXOnkqGUhq7SyfVY6H6ERwIPo4DcLn+YPyYZa1cY0Ti1+L7q/iN1GXJ+/paBJDKiaRJ03Cv1+oRlnwiKZacUYJKQ/3OVhuIgkaStreSmgtKUWhvd2CmAhE28iIxNvKc8wVFvNT+7MBmD6CezlsYtZGSDg89TejbQO5H8mEjFVGZkXilKSUDkXwgtvPD3C+hL1AQJOLGVJjs626b5BYyj7vtSX3eRbHxu5fV5jYOjouqPdnW/MwHwPPw9FQwkjEynbhcLtd/6AsL5U6IuRkjLAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad Peruana de Ciencias Aplicadas (UPC)","correspondingAuthor":true,"prefix":"","firstName":"Gustavo","middleName":"Rafael Rodriguez","lastName":"Inga","suffix":""},{"id":623268309,"identity":"70a6680d-b4d3-48c9-b7e4-345cac099834","order_by":1,"name":"Leonardo Javier Pariona Chavez","email":"","orcid":"","institution":"Universidad Peruana de Ciencias Aplicadas (UPC)","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"Javier Pariona","lastName":"Chavez","suffix":""},{"id":623268310,"identity":"e38d6398-9a16-4fc0-8f38-0053d263dece","order_by":2,"name":"Joel Figueroa Vilcarromero","email":"","orcid":"","institution":"Universidad Peruana de Ciencias Aplicadas (UPC)","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"Figueroa","lastName":"Vilcarromero","suffix":""}],"badges":[],"createdAt":"2026-02-19 16:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8919524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8919524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107488337,"identity":"62d31db1-9169-41eb-af50-35fb32ac0d26","added_by":"auto","created_at":"2026-04-22 02:44:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":699828,"visible":true,"origin":"","legend":"","description":"","filename":"MultistageDetectionManuscriptRevisedScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919524/v1_covered_9f6bb269-27e6-4886-b88b-dcdbbfc21aef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autonomous Embedded-Vision System for Multistage Detection of Phytopathogenic Fungi in Potato and Tomato Crops UsingConvolutional Neural Networks","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"computer vision, deep learning, agricultural robotics","lastPublishedDoi":"10.21203/rs.3.rs-8919524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8919524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent phytopathological diagnostic systems rely on manual inspections or laboratory analyses, which delay early detection and limit in-field responsiveness. Phytopathogenic fungal diseases pose a persistent threat to food security, directly affecting the productivity of essential crops such as potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e) and tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e) [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among these diseases, \u003cem\u003ePhytophthora infestans\u003c/em\u003e, the causal agent of late blight, is characterized by its high virulence and rapid spread, capable of generating significant losses in short periods when detection occurs too late [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address this issue, a computer vision and deep learning\u0026ndash;based system for multistage detection of fungal infections in potato and tomato crops is proposed. The system comprises a convolutional neural network optimized for edge processing and a mobile robotic platform equipped with a manipulator arm for localized treatment application. The developed model was deployed on a Raspberry Pi 4 connected to a 12-MP Raspberry Pi Camera Module 3 NoIR, responsible for acquiring RGB images in the field.\u003c/p\u003e \u003cp\u003eThe proposed network was compared with reference architectures\u0026mdash;ResNet-50, VGG16, MobileNetV2, and Inception-v3\u0026mdash;within a four-stage detection pipeline: crop identification, health-state classification, infection diagnosis, and foliar severity estimation. A dataset of 18,200 images obtained from publicly accessible online sources, under diverse lighting and background conditions, was used, partitioned into 70% for training, 20% for validation, and 10% for testing.\u003c/p\u003e \u003cp\u003ePreliminary results show an average accuracy in the range of 0.90\u0026ndash;0.92, with inference latencies below 60 ms per image, ensuring smooth performance on the Raspberry Pi 4 without requiring cloud connectivity. Additionally, the network demonstrated higher sensitivity to visual variations compared to the baseline models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Autonomous Embedded-Vision System for Multistage Detection of Phytopathogenic Fungi in Potato and Tomato Crops UsingConvolutional Neural Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 04:00:30","doi":"10.21203/rs.3.rs-8919524/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T07:02:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"351796898697791978491607380386136760","date":"2026-05-12T00:05:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T11:49:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T11:46:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T17:18:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T06:16:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-14T06:11:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"02f1b3ea-f139-4a2b-bcba-0a618ccfffc3","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T07:02:47+00:00","index":119,"fulltext":""},{"type":"reviewerAgreed","content":"351796898697791978491607380386136760","date":"2026-05-12T00:05:53+00:00","index":118,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66328823,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":66328824,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-21T04:00:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 04:00:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8919524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8919524","identity":"rs-8919524","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}