[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127446-en":3,"doc-seo-127446-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},127446,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","A Review of Machine Learning Approaches for Tomato Plant Disease Classification","Tomatoes are among the most widely cultivated and economically important crops, yet their productivity is frequently reduced by fungal, bacterial, and viral diseases. Early and reliable diagnosis is vital to protect yield and crop quality, but traditional visual identification is labor-intensive, subjective, and difficult to scale. Machine learning (ML) and deep learning (DL) enable automated disease classification by learning image and environmental patterns. This review analyzes conventional ML and advanced DL methods, discusses datasets and evaluation metrics, and addresses practical limits such as data availability, model robustness, and real-world deployment constraints.","A Review of Machine Learning Approaches for Tomato Plant Disease Classification  \nIbrahim Asim Ibrahim Eltayeb1*, Yacqub Isse Salah 1, Mohd Zaki Mohd Salikon1, Rusma Anieza Ruslan1  \n1 Faculty of Computer Science and Information Technology,  \nUniversiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, 86400, MALAYSIA  \n*Corresponding Author: [ibrasim2003@gmail.com](ibrasim2003@gmail.com)  \nDOI: [https://doi.org/10.30880/jastec.2025.02.01.002](https://doi.org/10.30880/jastec.2025.02.01.002)  \nArticle Info  \nReceived: 16 January 2025  \nAccepted: 20 May 2025  \nAvailable online: 30 June 2025  \nKeywords  \nMachine Learning, classification, plant disease, agricultural  \nAbstract  \nTomatoes represent one of the most extensively cultivated and economically significant crops globally. Nevertheless, their productivity is frequently undermined by a range of plant diseases, which, if not promptly detected or effectively managed, can result insubstantial yield reductions and economic repercussions. Traditional methods for disease identification are often labor-intensive, subjective, and reliant on expert knowledge, making them inefficient for largescale agricultural operations. In recent years, machine learning (ML) has emerged as a promising avenue for automating and improving the accuracy of disease detection and classification. This review offers a comprehensive analysis of both conventional ML techniques and advanced deep learning (DL) methodologies specifically applied to the classification of tomato plant diseases. It examines the underlying methodologies of each approach, highlights commonly utilized datasets and evaluation metrics, and discusses the practical limitations associated with their implementation. Key challenges, such as data availability, model robustness, and the deployment of these solutions in real-world agricultural settings, are also addressed.  \n1. Introduction  \nTomato crops are integral to global agriculture, making significant contributions to the economy and food supply across various regions [1]. As a versatile and nutrient-dense vegetable, tomatoes are cultivated extensively, particularly in areas characterized by favorable climatic conditions. However, these plants are highly vulnerable to a multitude of diseases caused by fungi, bacteria, and viruses. Such diseases can substantially diminish both the yield and quality of the crop, resulting in economic losses for farmers and disruptionsin food supply chains. Timely and accurate disease detection is crucial for effective management; however, traditional identification methods predominantly rely on manual visual inspections conducted by agricultural experts [2]. These approaches are often labor-intensive, susceptible to human error, and challenging to scale.  \nML has emerged as a transformative technology within agriculture, providing a data-driven methodology to automate disease detection processes. By analyzing patterns in images and environmental data, ML models can accurately classify various disease types. These systems not only enhance diagnostic precision but also facilitate real-time and large-scale monitoring, rendering them invaluable tools in the realm of precision agriculture. The purpose of this review is to deliver a comprehensive overview of the ML techniques employed in tomato plant disease classification. The paper commences with a discussion of the most prevalent tomato diseases and the fundamental principles of ML in agricultural contexts. It subsequently explores both traditional and modern  \nclassification approaches, compares their performance, and highlights challenges associated with dataset quality, model generalization, and the deployment of these models in real-world settings.  \n2. Background and Fundamentals  \nSustainable tomato production is crucial for ensuring food security and supporting global agricultural economies. Tomatoes are not only consumed fresh but also serve as key raw materials for various processed products, inclu","cbCaiiqgN7YelZY1","https://ap.wps.com/l/cbCaiiqgN7YelZY1","pdf",1138582,1,16,"English","en",105,"# Introduction\n# Background and Fundamentals\n## Tomato Plant Diseases\n## Common Tomato Diseases and Visual Features","[{\"question\":\"Why is timely tomato plant disease detection important?\",\"answer\":\"Diseases can substantially reduce yield and crop quality and cause economic losses. Timely, accurate detection supports effective management and intervention.\"},{\"question\":\"What problem do traditional tomato disease identification methods face?\",\"answer\":\"Conventional approaches rely on manual expert visual inspections, which are labor-intensive, subjective, prone to human error, and challenging to scale for large operations.\"},{\"question\":\"How does machine learning improve tomato disease classification?\",\"answer\":\"ML models learn patterns from images and environmental data, enabling more accurate classification, real-time monitoring, and large-scale precision agriculture support.\"}]","A Review of Machine Learning Approaches for Tomato Plant Disease Classification | PDF",1785938896,40,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-review-of-machine-learning-approaches-for-tomato-plant-disease-classification","",{"@graph":36,"@context":85},[37,54,68],{"@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/a-review-of-machine-learning-approaches-for-tomato-plant-disease-classification/127446/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is timely tomato plant disease detection important?","Question",{"text":75,"@type":76},"Diseases can substantially reduce yield and crop quality and cause economic losses. Timely, accurate detection supports effective management and intervention.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem do traditional tomato disease identification methods face?",{"text":80,"@type":76},"Conventional approaches rely on manual expert visual inspections, which are labor-intensive, subjective, prone to human error, and challenging to scale for large operations.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning improve tomato disease classification?",{"text":84,"@type":76},"ML models learn patterns from images and environmental data, enabling more accurate classification, real-time monitoring, and large-scale precision agriculture support.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]