A Comparative Investigation of Disease Detection in Plant Pathology: A Study on the YOLOv3 and Gaussian YOLOv3Models

A Comparative Investigation of Disease Detection in Plant Pathology: A Study on the YOLOv3 and Gaussian YOLOv3Models | 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 A Comparative Investigation of Disease Detection in Plant Pathology: A Study on the YOLOv3 and Gaussian YOLOv3Models M. Haripriya, A. Radhika, J. Jeslin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4235954/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 Leaf disease detection is a critical task in precision agriculture, aiming to monitor and control the spread of plant diseases for sustainable crop management. Object detection models have shown promise in accurately identifying and localizing diseases on plant leaves in recent years. This paper explores the effectiveness of YOLOv3 (You Only Look Once) and a variant known as Gaussian YOLOv3 in the context of leaf disease detection. YOLOv3 is known for its real-time object detection capabilities and high accuracy. However, it may face challenges in accurately localizing subtle disease patterns and handling uncertainties in complex leaf images. To address these challenges, Gaussian YOLOv3 incorporates Gaussian components to model uncertainty and improves localization accuracy. The comparative analysis involves evaluating the performance of YOLOv3 and Gaussian YOLOv3 in terms of localization accuracy, speed, adaptability to diverse conditions, and training requirements. Experiments are conducted using a dataset comprising various leaf diseases under different environmental conditions. They enable timely interventions and agricultural decision-making, reducing crop losses and ensuring effective disease management. Leaf disease detection YOLOv3 multi-scale prediction K-means cluster Gaussian YOLOv3 Uncertainty 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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