Weed and Chilli crop discrimination to evaluation of inter and intra row weeder

Weed and Chilli crop discrimination to evaluation of inter and intra row weeder | 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 Weed and Chilli crop discrimination to evaluation of inter and intra row weeder K. Raju Yadav, Shashikumar Shashikumar, D. Raj Kumar, P. Sambasiva rao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6743563/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 Automated crop and weed detection is a crucial advancement in precision agriculture, enabling efficient weed management while reducing herbicide use and labour costs. This study focuses on developing a machine learning model using google teachable machine to distinguish between chili crops and weeds. This study evaluates the performance of three camera systems (iphone 15, Samsung M32, and Moto g 64) for plant and weed detection using key metrics such as accuracy, F1 score, and recall. Results indicate that iphone 15 camera consistently outperforms the others, achieving an average accuracy of approximately 95% for plant detection and 90% for weed detection. Its F1 scores of around 0.94 for plants and 0.89 for weeds, coupled with high recall rates of about 96% and 92%, demonstrate a strong balance between precision and sensitivity, ensuring reliable identification of true positives. In comparison, Samsung M32 camera shows moderate performance with accuracy near 88% (plants) and 82% (weeds), F1 scores around 0.86 and 0.81, and recall rates of approximately 89% and 83%. Moto g 64 camera exhibits the lowest performance, with accuracy of about 80% (chilli plants) and 75% (weeds), F1 scores around 0.78 and 0.73, and recall rates near 81% and 77%. These findings highlight the importance of high-quality imaging and robust detection algorithms in agricultural monitoring systems. The superior performance of iphone 15 underscores its suitability for precise plant and weed detection, essential for optimizing crop management and sustainable farming practices. Improving the capabilities of Samsung M32, and Moto g 64 cameras could further enhance their effectiveness, but current results iphone 15 camera has the most reliable option for discrimination of weeds and chilli crop. Weeds Detection Recall F1 score Trained model and Algorithm 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-6743563","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470033954,"identity":"95bfcb18-f2da-4ce0-b3ab-7516e275623c","order_by":0,"name":"K. Raju Yadav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYHCCBCBmZmDgYWw4kFABYjM3EKuF+eCDB2dAbEaCWhigWtiSDR+2gTgEtJi3H3gmzVNjLW9w5oyZROK82mj+dqCWHxXbcGqROZOQJs1zLN1ww9keoJZtx3NnHGZsYOw5cxunFgkGkBa2w4wz+3lAWo7lNgC1MDO24dHC/wCo5d9he4iWOcdy5xPUIgG0hbftcGI/b1uyQWJDTe4GwloeJFvO7UtP7uc5fPBBwrEDuRuBWg7i9Qt/TuKNN9+sbdt4EhsO/qipy513Hqj3RwVuLcD4SGDiQfAOg8kDeNQDAfsBxh8IXh1+xaNgFIyCUTAiAQC5+F4xFIMN7QAAAABJRU5ErkJggg==","orcid":"","institution":"RVS Technical campus","correspondingAuthor":true,"prefix":"","firstName":"K.","middleName":"Raju","lastName":"Yadav","suffix":""},{"id":470033955,"identity":"f36c35d1-732e-4bf0-8a63-0cc6769d1715","order_by":1,"name":"Shashikumar Shashikumar","email":"","orcid":"","institution":"Reva University - Yelahanka","correspondingAuthor":false,"prefix":"","firstName":"Shashikumar","middleName":"","lastName":"Shashikumar","suffix":""},{"id":470033956,"identity":"162fa97b-e332-4d06-a24d-4f8d29beeee5","order_by":2,"name":"D. Raj Kumar","email":"","orcid":"","institution":"RVS Technical campus","correspondingAuthor":false,"prefix":"","firstName":"D.","middleName":"Raj","lastName":"Kumar","suffix":""},{"id":470033957,"identity":"29bc58d2-056a-4df5-b396-0307f53042aa","order_by":3,"name":"P. Sambasiva rao","email":"","orcid":"","institution":"RVS Technical campus","correspondingAuthor":false,"prefix":"","firstName":"P.","middleName":"Sambasiva","lastName":"rao","suffix":""},{"id":470033958,"identity":"1528ea42-d92a-4fb5-85bf-40223b50a747","order_by":4,"name":"T. Mahesh Babu","email":"","orcid":"","institution":"RVS Technical campus","correspondingAuthor":false,"prefix":"","firstName":"T.","middleName":"Mahesh","lastName":"Babu","suffix":""},{"id":470033959,"identity":"66ff9c74-8e36-428e-815e-2c8d7f848c91","order_by":5,"name":"S. Gobika","email":"","orcid":"","institution":"RVS Technical campus","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"","lastName":"Gobika","suffix":""}],"badges":[],"createdAt":"2025-05-25 12:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6743563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6743563/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89562418,"identity":"cf8692f8-7e24-4980-9ffb-14edebf32dff","added_by":"auto","created_at":"2025-08-21 10:24:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":759284,"visible":true,"origin":"","legend":"","description":"","filename":"Weedandchillicropdiscriminationusingimageanalysis1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6743563/v1_covered_005968f0-e21f-4b24-ba9b-c63968ceab33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weed and Chilli crop discrimination to evaluation of inter and intra row weeder","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":"Weeds, Detection, Recall, F1 score, Trained model and Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-6743563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6743563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Automated crop and weed detection is a crucial advancement in precision agriculture, enabling efficient weed management while reducing herbicide use and labour costs. This study focuses on developing a machine learning model using google teachable machine to distinguish between chili crops and weeds. This study evaluates the performance of three camera systems (iphone 15, Samsung M32, and Moto g 64) for plant and weed detection using key metrics such as accuracy, F1 score, and recall. Results indicate that iphone 15 camera consistently outperforms the others, achieving an average accuracy of approximately 95% for plant detection and 90% for weed detection. Its F1 scores of around 0.94 for plants and 0.89 for weeds, coupled with high recall rates of about 96% and 92%, demonstrate a strong balance between precision and sensitivity, ensuring reliable identification of true positives. In comparison, Samsung M32 camera shows moderate performance with accuracy near 88% (plants) and 82% (weeds), F1 scores around 0.86 and 0.81, and recall rates of approximately 89% and 83%. Moto g 64 camera exhibits the lowest performance, with accuracy of about 80% (chilli plants) and 75% (weeds), F1 scores around 0.78 and 0.73, and recall rates near 81% and 77%. These findings highlight the importance of high-quality imaging and robust detection algorithms in agricultural monitoring systems. The superior performance of iphone 15 underscores its suitability for precise plant and weed detection, essential for optimizing crop management and sustainable farming practices. Improving the capabilities of Samsung M32, and Moto g 64 cameras could further enhance their effectiveness, but current results iphone 15 camera has the most reliable option for discrimination of weeds and chilli crop.","manuscriptTitle":"Weed and Chilli crop discrimination to evaluation of inter and intra row weeder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 06:59:05","doi":"10.21203/rs.3.rs-6743563/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":"2ca7eb3a-5fcd-4dd3-a191-a5a95e8090f1","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-21T10:24:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 06:59:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6743563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6743563","identity":"rs-6743563","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}