[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117827-en":3,"doc-seo-117827-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117827,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning Based Automatic Leaf Diseases Detection","Machine learning is applied to automatically detect leaf diseases by training a convolutional neural network on datasets containing both healthy and diseased leaf images. The workflow includes dataset compilation and preprocessing, followed by evaluation using accuracy, precision, and recall on the labeled training set. Experimental results indicate strong performance for detecting leaf illnesses, supporting practical deployment. The approach supports early identification and prevention of bacterial spot and early blight, aiming to improve crop productivity while reducing reliance on toxic pesticides.","MACHINE LEARNING BASED AUTOMATIC LEAF DISEASES DETECTION  \nPrasad J1,*, RubanKumar J2, Thirumoorthi G3, UdayaKeerthi4 V S and Santhosh S M5  \n1Assistant Professor, Department of ECE, KPRIET, Coimbatore.  \n2 ,3,4,5 Student, Department of ECE, KPRIET, Coimbatore.  \nAbstract. The method for applying machine learning to automatically detect leaf diseases is presented in this paper. A convolutional neural network was used to extract pertinent features from leaf image datasets that included healthy and diseased leaves. The dataset was compiled and preprocessed. Accuracy, precision, and recall measures were used to assess the machine learning algorithm after it had been trained on the labeled dataset. According to the findings, the algorithm was very precise andrecallable in its ability to detect leaf illnesses, making it a potential method for practical use. This strategy may help with early leaf disease identification and prevention, increasing crop productivity and lowering the demand for toxic pesticides. Here we are identifying the Bacterial spot,  \nEarly blight.  \n1. INTRODUCTION  \nFarming is difficult because leaf diseases can negatively affect crop yield and quality. Early diagnosis and prevention are essential for these illnesses to be effectively managed, but manually identifying and monitoring crop infections can be challenging and timeconsuming for farmers. Machine learning methods have been a potential strategy for automating the diagnosis of leaf diseases in recent years. Machine learning algorithms can help effectively identify diseases and give farmers an early warning by utilizing vast datasets of tagged leaf photos. [1]Farmers with little experience could misidentify cattle and misuse medications. Environmental pollution will result from poor quality, insufficient productivity, and unnecessary financial losses. The use of image processing methods for the detection of plant diseases has emerged as a hot area of research to address these issues.  \nThis study presents an approach for automatic leaf disease detection using machine learning. We collected a dataset of leaf images with both healthy and diseased leaves, which were pre-processed and used to train a convolutional neural network to extract relevant features. The resulting machine-learning algorithm was evaluated using standard metrics and showed high accuracy, precision, and recall in detecting leaf diseases.  \n* [Corresponding author: ](Corresponding author: prasad7research@gmail.com)[prasad7research@gmail.com](Corresponding author: prasad7research@gmail.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nThe early diagnosis and control of leaf diseases, increasing crop output, and lowering the use of toxic pesticides are all problems that our strategy may help with. We think that this research can be used as a starting point for future work on creating automated agricultural systems that will increase farming productivity and sustainability.  \n2. OVERVIEW EXISTING SYSTEM  \nA new field, automatic leaf disease detection using machine learning, uses image processing methods and machine learning algorithms to detect and diagnose diseases in plant leaves. Rule-based and machine- learning-based methods [12] can be used to categorize the automatic leaf disease detection systems now in use. Rule- based systems rely on a preset set of guidelines based on the visual traits of the condition. These guidelines are often created by subject-matter specialists and used to assess the health of a plant leaf from a particular photograph. Contrarily, machine learning-based systems automatically learn the visual characteristics of healthy and diseased leaves using statistical models and algorithms. [2]Typically, these systems need a sizable dataset of tagged photos of healthy and ","cbCaip9UGO5eF8td","https://ap.wps.com/l/cbCaip9UGO5eF8td","pdf",669509,1,14,"English","en",105,"# Introduction\n# Overview Existing System\n# Proposed System","[{\"question\":\"How does the proposed method detect leaf diseases?\",\"answer\":\"It compiles and preprocesses a dataset of healthy and diseased leaf images, then trains a convolutional neural network to extract relevant features for disease detection.\"},{\"question\":\"Which evaluation metrics are used to assess the algorithm?\",\"answer\":\"Accuracy, precision, and recall are used to evaluate performance on the labeled dataset after training.\"},{\"question\":\"Why is early leaf disease detection important in this study?\",\"answer\":\"Early diagnosis and control can improve crop output and reduce the use of toxic pesticides, helping farmers manage infections more effectively.\"}]","Machine Learning Based Automatic Leaf Diseases Detection | PDF",1785679857,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-automatic-leaf-diseases-detection","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-automatic-leaf-diseases-detection/117827/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the proposed method detect leaf diseases?","Question",{"text":76,"@type":77},"It compiles and preprocesses a dataset of healthy and diseased leaf images, then trains a convolutional neural network to extract relevant features for disease detection.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which evaluation metrics are used to assess the algorithm?",{"text":81,"@type":77},"Accuracy, precision, and recall are used to evaluate performance on the labeled dataset after training.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is early leaf disease detection important in this study?",{"text":85,"@type":77},"Early diagnosis and control can improve crop output and reduce the use of toxic pesticides, helping farmers manage infections more effectively.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]