An Integrated YOLOv7–Fuzzy Reasoning Framework for Interpretable and Robust Cantaloupe (Cucumis melo) Growth-Stage Assessment

An Integrated YOLOv7–Fuzzy Reasoning Framework for Interpretable and Robust Cantaloupe (Cucumis melo) Growth-Stage Assessment | 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 An Integrated YOLOv7–Fuzzy Reasoning Framework for Interpretable and Robust Cantaloupe (Cucumis melo) Growth-Stage Assessment Sikudhan Lucas Mpuhus, Mohamad Shukri Zainal Abidin, Mohd Shahkhirat bin Norizan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8600877/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Background Precision agriculture increasingly relies on computer vision systems to monitor crop growth; however, most existing approaches remain limited to frame-level object detection and do not support agronomic decision-making under uncertainty. To address this limitation, this study develops an interpretable and robust framework for cantaloupe ( Cucumis melo ) growth-stage assessment by integrating deep learning–based visual perception with fuzzy reasoning. Results A YOLOv7 detector was fine-tuned to identify healthy leaves, wilted leaves, flowers, and fruits from greenhouse imagery collected across eleven cultivation cycles at three production sites. The detected class counts were temporally aggregated and used as inputs to a Mamdani-type fuzzy inference system encoding expert agronomic knowledge and growth-stage expectations. Experimental evaluation showed that YOLOv7 achieved the highest [email protected] (0.771) and balanced precision–recall performance compared with other YOLO variants, while the fuzzy reasoning layer transformed noisy object-level outputs into consistent crop-condition states with associated confidence levels. Real-world deployment on an edge device further demonstrated the system’s ability to generate actionable alerts, such as “Check Flower” and “Abnormal Condition,” aligned with expected phenological trends. Conclusions The proposed framework advances beyond conventional detection pipelines by enabling decision-level crop assessment that is interpretable, temporally aware, and robust to visual uncertainty. This approach provides a practical decision-support tool for greenhouse crop monitoring and supports the broader adoption of intelligent, confidence-aware systems in precision agriculture. Deep neural network Fuzzy logic Growth detection Rock melon (cantaloupe) YOLOv7 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviews received at journal 08 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Editor invited by journal 06 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 05 Feb, 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. 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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-8600877","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590495534,"identity":"37318509-b95d-4778-8daf-f19f8bb1d04d","order_by":0,"name":"Sikudhan Lucas Mpuhus","email":"","orcid":"","institution":"University of Technology Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Sikudhan","middleName":"Lucas","lastName":"Mpuhus","suffix":""},{"id":590495536,"identity":"d1f154eb-7da9-4d7d-a405-5d1c16cefacf","order_by":1,"name":"Mohamad Shukri Zainal 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Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep neural network, Fuzzy logic, Growth detection, Rock melon (cantaloupe), YOLOv7","lastPublishedDoi":"10.21203/rs.3.rs-8600877/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8600877/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cdiv id=\"ASec1\" class=\"AbstractSection\"\u003e \u003cdiv class=\"Heading\"\u003eBackground\u003c/div\u003e \u003cp\u003ePrecision agriculture increasingly relies on computer vision systems to monitor crop growth; however, most existing approaches remain limited to frame-level object detection and do not support agronomic decision-making under uncertainty. To address this limitation, this study develops an interpretable and robust framework for cantaloupe (\u003cem\u003eCucumis melo\u003c/em\u003e) growth-stage assessment by integrating deep learning\u0026ndash;based visual perception with fuzzy reasoning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"ASec2\" class=\"AbstractSection\"\u003e \u003cdiv class=\"Heading\"\u003eResults\u003c/div\u003e \u003cp\u003eA YOLOv7 detector was fine-tuned to identify healthy leaves, wilted leaves, flowers, and fruits from greenhouse imagery collected across eleven cultivation cycles at three production sites. The detected class counts were temporally aggregated and used as inputs to a Mamdani-type fuzzy inference system encoding expert agronomic knowledge and growth-stage expectations. Experimental evaluation showed that YOLOv7 achieved the highest [email protected] (0.771) and balanced precision\u0026ndash;recall performance compared with other YOLO variants, while the fuzzy reasoning layer transformed noisy object-level outputs into consistent crop-condition states with associated confidence levels. Real-world deployment on an edge device further demonstrated the system\u0026rsquo;s ability to generate actionable alerts, such as \u0026ldquo;Check Flower\u0026rdquo; and \u0026ldquo;Abnormal Condition,\u0026rdquo; aligned with expected phenological trends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"ASec3\" class=\"AbstractSection\"\u003e \u003cdiv class=\"Heading\"\u003eConclusions\u003c/div\u003e \u003cp\u003eThe proposed framework advances beyond conventional detection pipelines by enabling decision-level crop assessment that is interpretable, temporally aware, and robust to visual uncertainty. This approach provides a practical decision-support tool for greenhouse crop monitoring and supports the broader adoption of intelligent, confidence-aware systems in precision agriculture.\u003c/p\u003e \u003c/div\u003e","manuscriptTitle":"An Integrated YOLOv7–Fuzzy Reasoning Framework for Interpretable and Robust Cantaloupe (Cucumis melo) Growth-Stage Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 09:57:32","doi":"10.21203/rs.3.rs-8600877/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-13T09:43:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T00:30:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T14:23:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10161920437903120002479354750531532050","date":"2026-05-10T10:33:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109400254935196669358318914350882017615","date":"2026-05-09T14:45:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148985442477593220237645210709651741851","date":"2026-05-09T01:33:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224549927436116877849970128291039281429","date":"2026-05-08T14:50:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T13:17:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147307786073833633174559633173643993570","date":"2026-05-08T12:58:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330870363939947349994688625146523245987","date":"2026-05-08T10:08:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T20:27:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T20:26:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-06T06:12:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T17:15:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-02-05T15:26:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e56c3d0-9aee-4530-b4fd-06e57d52aa4c","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-13T09:43:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T00:30:45+00:00","index":78,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T14:23:02+00:00","index":77,"fulltext":""},{"type":"reviewerAgreed","content":"10161920437903120002479354750531532050","date":"2026-05-10T10:33:42+00:00","index":76,"fulltext":""},{"type":"reviewerAgreed","content":"109400254935196669358318914350882017615","date":"2026-05-09T14:45:56+00:00","index":75,"fulltext":""},{"type":"reviewerAgreed","content":"148985442477593220237645210709651741851","date":"2026-05-09T01:33:17+00:00","index":74,"fulltext":""},{"type":"reviewerAgreed","content":"224549927436116877849970128291039281429","date":"2026-05-08T14:50:24+00:00","index":73,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T13:17:21+00:00","index":72,"fulltext":""},{"type":"reviewerAgreed","content":"147307786073833633174559633173643993570","date":"2026-05-08T12:58:58+00:00","index":71,"fulltext":""},{"type":"reviewerAgreed","content":"330870363939947349994688625146523245987","date":"2026-05-08T10:08:01+00:00","index":70,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T10:01:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 09:57:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8600877","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8600877","identity":"rs-8600877","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}