[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120328-en":3,"doc-seo-120328-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120328,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Automated Plant Disease Detection with Machine Learning","Automated Plant Disease Detection with Machine Learning focuses on early, accurate identification of plant diseases to reduce crop damage and strengthen agricultural productivity. It addresses limitations of manual visual inspection, expert dependency, and time-consuming laboratory workflows by introducing an image-based automated system. Convolutional Neural Networks using pretrained architectures such as ResNet and SqueezeNet classify leaf images as healthy or diseased. The workflow includes dataset collection and preprocessing, augmentation (rotation, scaling, flipping), transfer learning fine-tuning, and evaluation with accuracy, precision, recall, and F1-score. A deployable interface supports farmers by enabling rapid diagnosis and potential treatment recommendations, with future extensibility to more crops and diseases.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \n|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|  \nVolume 14, Issue 4, April 2025  \n|DOI: 10.15680/IJIRSET.2025.1404464|  \nAutomated Plant Disease Detection with  \nMachine Learning  \nS Tarun Kumar, LUday Sai, T Manoj Guptha, P Ganesh, T.Poovizhi  \nB. Tech Students, Department ofCSE, Bharath Institute of Higher Education and Research, Selaiyur, Chennai,  \nTamil Nadu, India  \nAssistant Professor, Department ofCSE, Bharath Institute of Higher Education and Research, Selaiyur, Chennai,  \nTamil Nadu, India  \nABSTRACT: The early and accurate detection of plant diseases plays a vital role in minimizing crop damage and enhancing agricultural productivity. Traditional methods for identifying plant diseases—such as manual observation and expert consultations—are often time-consuming, costly, and reliant on the availability of skilled personnel. To overcome these limitations, this study presents an automated system for plant disease detection using advanced machine learning techniques. The proposed framework utilizes convolutional neural networks (CNNs), specifically pretrained models like ResNet and SqueezeNet, to analyze images of plant leaves and classify them as healthy or diseased. The system begins with the collection and preprocessing of a diverse dataset comprising both healthy and infected leaf samples. Image augmentation techniques such as rotation, scaling, and flipping are applied to increase dataset variability and enhance model robustness. Subsequently, feature extraction is conducted through CNN-based models that capture color, shape, and texture-based characteristics from the leaf images. Transfer learning is employed to finetune the models on the specific dataset, leveraging previously learned features for improved accuracy.  \nThe performance of the trained models is evaluated using key metrics including accuracy, precision, recall, and F1-score. Experimental results indicate a high degree of classification accuracy and generalization capability on unseen data. The final model is deployed through a user-friendly interface that can be integrated into mobile or web-based applications, allowing farmers to upload leaf images and receive instant disease diagnosis and possible treatment recommendations.  \nThe proposed system holds significant promise for real-world applications, particularly in regions with limited access to agricultural experts. Moreover, its scalability and adaptability pave the way for future extensions to other crops and disease types. This research underscores the transformative potential of machine learning in precision agriculture, offering a cost-effective, scalable, and efficient solution for early plant disease detection.  \nI. INTRODUCTION  \nAgriculture plays a critical role in the economy of many countries, particularly those with agrarian-based industries. However, plant diseases pose a significant threat to crop yield and quality, often resulting in considerable financial loss and food scarcity. Traditionally, the identification and management of plant diseases have relied on manual methods such as visual inspection by experts, laboratory analysis, or field surveys. These approaches are not only timeconsuming and labor-intensive but also require domain-specific knowledge, which may not always be accessible to farmers, especially in rural or under-resourced areas.  \nWith the rise of smart farming technologies, there is an increasing need for automated, efficient, and scalable solutions to detect plant diseases early and accurately. Early detection is crucial as it allows for timely intervention, reducing crop damage and improving overall agricultural productivit","cbCaiswcEC2vCCtK","https://ap.wps.com/l/cbCaiswcEC2vCCtK","pdf",1278447,1,"English","en",105,"# Abstract\n# I. Introduction\n# II. Existing System","[{\"question\":\"Why is early plant disease detection important in the proposed work?\",\"answer\":\"Early detection reduces crop damage and improves overall agricultural productivity by enabling timely intervention.\"},{\"question\":\"Which machine learning approach does the system use to classify leaf images?\",\"answer\":\"The system uses convolutional neural networks (CNNs), leveraging pretrained models such as ResNet and SqueezeNet for classification into healthy or diseased categories.\"},{\"question\":\"How is model performance evaluated and improved?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and F1-score. Improvement comes from preprocessing, image augmentation, and transfer learning fine-tuning on the specific dataset.\"}]","Automated Plant Disease Detection with Machine Learning | PDF",1785729492,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"automated-plant-disease-detection-with-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/automated-plant-disease-detection-with-machine-learning/120328/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is early plant disease detection important in the proposed work?","Question",{"text":74,"@type":75},"Early detection reduces crop damage and improves overall agricultural productivity by enabling timely intervention.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning approach does the system use to classify leaf images?",{"text":79,"@type":75},"The system uses convolutional neural networks (CNNs), leveraging pretrained models such as ResNet and SqueezeNet for classification into healthy or diseased categories.",{"name":81,"@type":72,"acceptedAnswer":82},"How is model performance evaluated and improved?",{"text":83,"@type":75},"Performance is assessed using accuracy, precision, recall, and F1-score. Improvement comes from preprocessing, image augmentation, and transfer learning fine-tuning on the specific dataset.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]