Plant Leaf Disease Detection and Classification using Image Processing Techniques

Plant Leaf Disease Detection and Classification using Image Processing Techniques | 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 Plant Leaf Disease Detection and Classification using Image Processing Techniques Sandeep Srivastava, LALAN KUMAR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8299643/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 Agriculture: the backbone of livelihood in India, where a significant portion of the economy is dependent on agriculture. And with a burgeoning population, agricultural systems come under pressure to supply enough, high-quality yields to guarantee food security and economic stability. Diseases of plants that impose highly visible constraints on growth are one of the principal villains causing loss of productivity in agriculture, resulting in major deficits, both in farm productivity and farmer income. Hence, a timely and accurate identification of these diseases is crucial. Therefore, this work offers an approach based on image processing for identifying and classifying the diseases on the leaf of a plant coupled with machine learning algorithms. To lay a foundation of proven techniques, a thorough review of current research was performed. The technique involves deploying seven machine learning classifiers, which are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Random Forest (RF), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), and Linear Discriminant Analysis (LDA). Performance was measured in terms of precision, recall, F1-score, specificity, and accuracy. Random Forest model performed best of all the classifiers with 98.12% accuracy level; this highlights that ensemble methods in general pull ahead of others in real-world disease detection applications. The findings highlight the power of AI-based tools in facilitating effective, scalable, and sustainable agricultural practices. Plant Disease Detection Image Processing Machine Learning Leaf Image Classification Random Forest Support Vector Machine (SVM) Agricultural Technology 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-8299643","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569020363,"identity":"6963c367-6750-471a-9615-da959dbb4fe0","order_by":0,"name":"Sandeep 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And with a burgeoning population, agricultural systems come under pressure to supply enough,\u0026ensp;high-quality yields to guarantee food security and economic stability. Diseases of plants that impose highly visible constraints on growth are one of the principal villains causing loss of\u0026ensp;productivity in agriculture, resulting in major deficits, both in farm productivity and farmer income. Hence,\u0026ensp;a timely and accurate identification of these diseases is crucial. Therefore, this work offers an approach based on image processing for identifying and classifying the diseases\u0026ensp;on the leaf of a plant coupled with machine learning algorithms. To lay\u0026ensp;a foundation of proven techniques, a thorough review of current research was performed. 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