AI-Based Early Disease Detection in Peach Crops: A Computer Vision Approach for Monilinia spp. and Taphrina deformans

AI-Based Early Disease Detection in Peach Crops: A Computer Vision Approach for Monilinia spp. and Taphrina deformans | 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 AI-Based Early Disease Detection in Peach Crops: A Computer Vision Approach for Monilinia spp. and Taphrina deformans Luisa Paola Zúñiga Castro, Leyiber Orlando Villa Pinto, Daniel Felipe Silva Gaona, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7895853/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 This study introduces a robust early disease detection system for peach crops, leveraging computer vision and artificial intelligence to address significant economic losses caused by Brown Rot (Monilinia spp.) and Leaf Curl (Taphrina deformans). The methodology comprises a structured approach, starting with the collection of a high-quality dataset of 640 images captured under real-world field conditions. These images, representing healthy and diseased fruits and leaves, underwent a rigorous preprocessing pipeline that included background removal, color space conversion, resizing, and contour detection to optimize them for model training. A Convolutional Neural Network (CNN) was developed and validated using k-fold cross-validation, achieving an outstanding accuracy of 90.28% for fruit disease detection and 96.43% for leaf disease detection during the validation phase. The model's final performance, evaluated with a confusion matrix, demonstrated a remarkable 100% precision for Brown Rot in fruits and 96.4% precision for Leaf Curl in leaves. These results confirm the system's reliability and its potential for practical application in precision agriculture. The project culminates in a functional web application, showcasing the viability of deploying deep learning solutions as accessible tools for farmers to facilitate timely and proactive crop management. Disease detection Computer vision Convolutional Neural Networks Peach 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. 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The methodology comprises a structured approach, starting with the collection of a high-quality dataset of 640 images captured under real-world field conditions. These images, representing healthy and diseased fruits and leaves, underwent a rigorous preprocessing pipeline that included background removal, color space conversion, resizing, and contour detection to optimize them for model training. A Convolutional Neural Network (CNN) was developed and validated using k-fold cross-validation, achieving an outstanding accuracy of 90.28\\% for fruit disease detection and 96.43\\% for leaf disease detection during the validation phase. The model's final performance, evaluated with a confusion matrix, demonstrated a remarkable 100\\% precision for Brown Rot in fruits and 96.4\\% precision for Leaf Curl in leaves. These results confirm the system's reliability and its potential for practical application in precision agriculture. 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