JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Intelligent diagnosis of tomato leaf diseases using YOLOv8
Abstract
Tomato is one of the most widely cultivated and consumed vegetables worldwide. However, its production is hindered by various pests and pathogens. Rapid and accurate detection of these diseases is crucial for timely intervention and effective management. This study presents a model that employs YOLOv8n, an advanced object detection algorithm, for the identification of tomato leaf diseases. Experimental evaluations were conducted using 2,000 images selected from the Tomato subset of the CCMT dataset, which comprises 5,435 images categorized into five disease classes. The subset was constructed using a stratified sampling strategy to ensure balanced class representation, while low-quality or ambiguous images were excluded to improve annotation reliability. The results demonstrate that YOLOv8n achieved a mean Average Precision of 0.704, a precision of 0.655, and a recall of 0.721 across all classes. The best performance was observed in the healthy class, with an mAP50 of 0.942, a precision of 0.835, and a recall of 0.944. Overall, the findings indicate that AI-based rapid diagnostic systems can serve as an effective solution for early detection of tomato diseases and the prevention of yield losses in agricultural production.

Keywords : Plant leaf disease, disease detection, tomato leaf disease, YOLOv8, object detection

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Article available in :
Volume 4, Issue 1, 2026
Page : 27-33
https://doi.org/10.51271/JCEEES-0034
Article Information
Received : 11 Kas 2025
Accepted : 01 Nis 2026
Published : 25 Nis 2026
Corresponding Author :

Hatice Candan: Department of Electrical and Electronics Engineering, Kırıkkale University, Kırıkkale, Turkiye

[email protected]
Citation : Candan, H., Aydilek, H. & Erten, M. Y. (2026). Intelligent diagnosis of tomato leaf diseases using YOLOv8. J Comp Electr Electron Eng Sci, 4(1), 27-33.

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