<![CDATA[This research develops a disease detection system for tomato plant leaves using Computer Vision with the YOLOv8 algorithm. The main focus of the research is to detect three common diseases in tomato leaves: early blight, gray mold, and target spot. Using the Research and Development (R&D) method with a prototype model, this study collected a total of 405 tomato leaf images consisting of 330 images for training and 75 images for testing. The YOLOv8n model trained for 50 epochs showed promising performance with an mAP@0.5 value of 0.60 on the test dataset. Evaluation results showed the best performance in detecting gray mold disease with a precision of 0.840, while target spot disease showed the lowest performance with a precision of 0.556. Real-time testing verified the system's ability to detect tomato leaf diseases in agricultural environments under various lighting conditions and image capture distances.]]>
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