[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123092-en":3,"doc-seo-123092-105":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},123092,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Disease Detection in Tropical Tomato Leaves via Machine Learning Models - Research","The study tackles the serious threat of tomato diseases in Ghana, where outbreaks reduce both yield and fruit quality and thereby weaken farmers’ livelihoods and the availability of tomatoes as a staple diet. Manual visual identification is slow and subjective, limiting timely intervention. A machine learning image-processing approach is proposed using the YOLOv5 architecture, trained on healthy and diseased leaf images. Validation reaches mAP@0.5 = 0.715, with strong on-site performance, and the model is deployed on a mobile platform for rapid, automated detection.","Vol. 8, No. 2, December 2024, page. 179-191 ISSN 2598-3245 (Print), ISSN 2598-3288 (Online) DOI: [http://doi.org/10.31961/eltikom.v8i2.1340](http://doi.org/10.31961/eltikom.v8i2.1340)  \nAvailable online at [http://eltikom.poliban.ac.id](http://eltikom.poliban.ac.id)  \nDISEASE DETECTION IN TROPICAL TOMATO LEAVES VIA MACHINE LEARNING MODELS  \nBenjamin Kommey1*, Elvis Tamakloe1, Daniel Opoku2, Tibilla Crispin1, Jeffrey Dan  \nquah1  \n1) Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana  \n2) Department of Electrical Engineering, Kwame Nkrumah University of Science and Technology, Kumasi,  \nGhana  \n[E-mail: bkommey.coe@knust.edu.gh](E-mail: bkommey.coe@knust.edu.gh), [tamakloe.elvis@gmail.com](tamakloe.elvis@gmail.com), [dopoku.coe@knust.edu.gh](dopoku.coe@knust.edu.gh), crispintibil  \n[la@gmail.com](la@gmail.com), [danquahjeffrey1@gmail.com](danquahjeffrey1@gmail.com)  \nReceived: 16 October 2024 – Revised: 31 October 2024 – Accepted: 1 November 2024  \nABSTRACT  \nThis study addresses the significant threat of tomato diseases to production in Ghana, which has led to substantial yield and quality losses, adversely affecting the livelihoods of local farmers and the availability of this essential dietary staple. Traditional disease identification methods are time-consuming and rely on subjective visual inspections, hindering early detection and control. This study develops a machine learning model capable of accurately identifying tomato plant diseases through image processing. The methodology involves processing a dataset of tomato plant images displaying healthy and diseased symptoms. The proposed model employs the YOLOv5 architecture and is deployed on a mobile platform for accessible disease identification. The model achieved a validation mAP@.5 of 0. 715, demonstrating strong performance during live, on-site testing. This system provides a swift, accurate, and automated solution for detecting tomato diseases, supporting the sustainability of tomato production in Ghana.  \nKeywords: CNN, disease detection, image processing, leaf, machine learning, tomato.  \nI. INTRODUCTION  \nTOMATOES are a staple ingredient in the daily diet of Ghanaians, with approximately 90% of the  \n300,000 metric tons produced annually consumed locally. Tomatoes account for about 38% of  \ntotal vegetable expenditure in the country [1] . However, plant diseases significantly threaten tomato production, causing severe losses in yield and quality. These diseases, caused by various fungi or water molds that infect leaves, stems, and fruits, spread rapidly under favorable environmental conditions such as warm and wet weather. Common tomato diseases include bacterial spots, leaf mosaic, leaf curls, septoria, blight, and fusarium.  \nA survey conducted in three districts within Ghana's forest and forest-savannah agro-ecological zones identified blight as the predominant fungal disease, with a mean incidence of 63.9% in the Asante Akim North District [2]. In contrast, fusarium was most prevalent in the Offinso North District. These diseases not only reduce the marketability and shelf life of tomatoes but also increase production costs due to the heavy reliance on fungicides. In some cases, tomato diseases have caused annual economic yield losses of up to 79%, underscoring their detrimental impact on Ghana's tomato production.  \nEarly detection and diagnosis of tomato diseases are crucial for effective management and prevention of further damage. Conventional methods, which rely on expert visual inspections, are time-consuming and labor-intensive. This necessitates the development of fast, accurate, and automated disease identification methods using machine learning.  \nAdvancements in image processing and machine learning have recently provided promising solutions for the early and precise detection of plant diseases, enabling timely intervention and mitigation. Among the crops affected by diseases, tomatoes hold particu","cbCaiqwpmSCHKAbk","https://ap.wps.com/l/cbCaiqwpmSCHKAbk","pdf",884244,1,13,"English","en",105,"# Abstract\n# I. Introduction\n# II. Related Works\n# III. Methodology\n# IV. Results and Discussion\n# V. Conclusion","[{\"question\":\"Why is tomato disease detection important in Ghana?\",\"answer\":\"Tomato diseases cause substantial losses in yield and quality, harming farmers’ livelihoods and reducing the availability of tomatoes as a staple food in Ghana.\"},{\"question\":\"What approach does the study propose for disease detection?\",\"answer\":\"It proposes a machine learning model using image processing with the YOLOv5 architecture to identify healthy and diseased tomato leaf symptoms.\"},{\"question\":\"How well did the model perform during validation and testing?\",\"answer\":\"The model achieved a validation mAP@0.5 of 0.715 and demonstrated strong performance during live, on-site testing.\"}]","Disease Detection in Tropical Tomato Leaves via Machine Learning Models - Research | PDF",1785814599,33,{"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},"disease-detection-in-tropical-tomato-leaves-via-machine-learning-models-research","",{"@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/disease-detection-in-tropical-tomato-leaves-via-machine-learning-models-research/123092/",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","2026-08-04",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 tomato disease detection important in Ghana?","Question",{"text":75,"@type":76},"Tomato diseases cause substantial losses in yield and quality, harming farmers’ livelihoods and reducing the availability of tomatoes as a staple food in Ghana.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What approach does the study propose for disease detection?",{"text":80,"@type":76},"It proposes a machine learning model using image processing with the YOLOv5 architecture to identify healthy and diseased tomato leaf symptoms.",{"name":82,"@type":73,"acceptedAnswer":83},"How well did the model perform during validation and testing?",{"text":84,"@type":76},"The model achieved a validation mAP@0.5 of 0.715 and demonstrated strong performance during live, on-site testing.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]