Towards Precision Agriculture for Sustainable Chili Pepper Production: A Deep Learning Approach to Crop Disease Detection in Benin

Towards Precision Agriculture for Sustainable Chili Pepper Production: A Deep Learning Approach to Crop Disease Detection in Benin | 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 Towards Precision Agriculture for Sustainable Chili Pepper Production: A Deep Learning Approach to Crop Disease Detection in Benin Mireille Gloria Founmilayo Odounfa, Castro Gbêmêmali Hounmenou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6994531/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted 10 You are reading this latest preprint version Abstract Ensuring food security is a crucial priority for nations worldwide, but plant diseases significantly hinder this objective through their impacts on agricultural productivity. Chili pepper (Capsicum spp.) is a major crop in West Africa, including in Benin. However, its production is challenged by diseases such as anthracnose and Tomato Yellow Leaf Curl Virus (TYLCV), which severely impact yields and farmer livelihoods. While traditional methods for disease detection and management have been commonly used, they are no longer sufficient to combat rising pest infestations and declining agricultural productivity. To tackle this issue, we built a comprehensive dataset of 213 images of anthracnose-affected leaves, 202 images of TYLCV-infected leaves, and 119 images of healthy leaves, collected under diverse environmental conditions in Benin. The study compared the performance of thirteen (13) deep learning models, including YOLOv8, MobileNetV2, and DenseNet121, for the classification of chili diseases using transfer learning techniques. Performance was evaluated using metrics such as Accuracy, Precision, Recall, and F1-Score. Results show that YOLOv8 outperformed other models in real-time detection and localization of leaf diseases, achieving a mean Average Precision ( [email protected] ) of 0.995 and [email protected] of 0.941, with precision and recall exceeding 99%. Among CNN models, MobileNetV2 and DenseNet121 achieved 96.25% accuracy. These findings demonstrate that deep learning models, particularly YOLOv8, hold immense potential for real-time, automated detection of chili pepper diseases. Future research should focus on expanding datasets, integrating climatic variations, and improving disease severity assessment for enhanced agricultural sustainability. Deep Learning Real-Time Detection Pepper Diseases Capsicum Agricultural Sustainability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 01 Aug, 2025 Reviews received at journal 31 Jul, 2025 Reviews received at journal 30 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers invited by journal 11 Jul, 2025 Editor assigned by journal 11 Jul, 2025 Editor invited by journal 08 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 08 Jul, 2025 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. 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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-6994531","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484542639,"identity":"6c90ff00-039d-41ac-b9e8-7cc1ad7ec24e","order_by":0,"name":"Mireille Gloria Founmilayo Odounfa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYFCCBCC2YWBgY2Y+wMDYQLSWNKAWdrYEErUw8PMYEKeFnz35mHRBgk00HzPPN4mfO2zkGNgPH92AT4tkz7M06RkJabltzLzbJHvPpBkz8KSl3cCnxeBGjpk074/DYC0SvG2HExskeMwIa+FJ+A/UwvNM8i8JWg6AtLBJE2UL0C/J1jwJyUAtbMbWsm1pxmyE/AIMsYO3eRLscuf3H354822bjRw/++FjeLUgAxYJEMlGrHIQYP5AiupRMApGwSgYOQAAfwJDnJbluigAAAAASUVORK5CYII=","orcid":"","institution":"University of Abomey-Calavi","correspondingAuthor":true,"prefix":"","firstName":"Mireille","middleName":"Gloria Founmilayo","lastName":"Odounfa","suffix":""},{"id":484542640,"identity":"64bafdb4-0a1a-4d78-a617-65dd0585625c","order_by":1,"name":"Castro Gbêmêmali Hounmenou","email":"","orcid":"","institution":"University of Abomey-Calavi","correspondingAuthor":false,"prefix":"","firstName":"Castro","middleName":"Gbêmêmali","lastName":"Hounmenou","suffix":""},{"id":484542642,"identity":"00001867-b515-4eff-9afb-bbd9b6e49260","order_by":2,"name":"Valère Kolawolé Salako","email":"","orcid":"","institution":"University of Abomey-Calavi","correspondingAuthor":false,"prefix":"","firstName":"Valère","middleName":"Kolawolé","lastName":"Salako","suffix":""},{"id":484542643,"identity":"a2d538ef-9d3f-424b-8f33-551f1b31ffa6","order_by":3,"name":"Antoine Affokpon","email":"","orcid":"","institution":"University of Abomey-Calavi","correspondingAuthor":false,"prefix":"","firstName":"Antoine","middleName":"","lastName":"Affokpon","suffix":""},{"id":484542645,"identity":"cf59f46e-f414-481e-adbd-b934c5e52f05","order_by":4,"name":"Romain L. 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Chili pepper (Capsicum spp.) is a major crop in West Africa, including in Benin. However, its production is challenged by diseases such as anthracnose and Tomato Yellow Leaf Curl Virus (TYLCV), which severely impact yields and farmer livelihoods. While traditional methods for disease detection and management have been commonly used, they are no longer sufficient to combat rising pest infestations and declining agricultural productivity. To tackle this issue, we built a comprehensive dataset of 213 images of anthracnose-affected leaves, 202 images of TYLCV-infected leaves, and 119 images of healthy leaves, collected under diverse environmental conditions in Benin. The study compared the performance of thirteen (13) deep learning models, including YOLOv8, MobileNetV2, and DenseNet121, for the classification of chili diseases using transfer learning techniques. Performance was evaluated using metrics such as Accuracy, Precision, Recall, and F1-Score. Results show that YOLOv8 outperformed other models in real-time detection and localization of leaf diseases, achieving a mean Average Precision ([email protected]) of 0.995 and [email protected] of 0.941, with precision and recall exceeding 99%. Among CNN models, MobileNetV2 and DenseNet121 achieved 96.25% accuracy. These findings demonstrate that deep learning models, particularly YOLOv8, hold immense potential for real-time, automated detection of chili pepper diseases. 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