A deep-learning-based model for detecting diseased tomato leaves is introduced, utilizing the YOLOV8s framework. The model was trained on the Plant Village dataset, which includes both healthy and diseased tomato leaf images. The images were enhanced and processed using the Ultralytics Hub, which provides an optimal setting for training YOLOV8 and YOLOV5 models. The YAML file was configured to identify sick leaves. The results show that YOLOV8s outperformed YOLOV5 and Faster R-CNN in terms of mean average precision (mAP), with YOLOV8s achieving 92.5% mAP, compared to 89.1% for YOLOV5 and 77.5% for Faster R-CNN. Additionally, YOLOV8s demonstrated a significantly faster inference speed, reaching 121.5 FPS, compared to 102.7 FPS for YOLOV5 and 11 FPS for Faster R-CNN. The model's lightweight design and high efficiency make it suitable for real-time detection. The study also compared the performance of YOLOV8s with other models, including YOLOV5 and Faster R-CNN, and found that YOLOV8s provided superior detection accuracy and speed. The results highlight the effectiveness of YOLOV8s in detecting diseased tomato leaves and its potential for application in agricultural monitoring and disease detection. The model's performance was evaluated using metrics such as precision, recall, and mAP, and the results demonstrated the model's high accuracy and efficiency in identifying and categorizing diseased tomato leaves. The study concludes that YOLOV8s is a highly effective model for detecting diseased tomato leaves, with significant improvements in detection accuracy and speed compared to previous versions.A deep-learning-based model for detecting diseased tomato leaves is introduced, utilizing the YOLOV8s framework. The model was trained on the Plant Village dataset, which includes both healthy and diseased tomato leaf images. The images were enhanced and processed using the Ultralytics Hub, which provides an optimal setting for training YOLOV8 and YOLOV5 models. The YAML file was configured to identify sick leaves. The results show that YOLOV8s outperformed YOLOV5 and Faster R-CNN in terms of mean average precision (mAP), with YOLOV8s achieving 92.5% mAP, compared to 89.1% for YOLOV5 and 77.5% for Faster R-CNN. Additionally, YOLOV8s demonstrated a significantly faster inference speed, reaching 121.5 FPS, compared to 102.7 FPS for YOLOV5 and 11 FPS for Faster R-CNN. The model's lightweight design and high efficiency make it suitable for real-time detection. The study also compared the performance of YOLOV8s with other models, including YOLOV5 and Faster R-CNN, and found that YOLOV8s provided superior detection accuracy and speed. The results highlight the effectiveness of YOLOV8s in detecting diseased tomato leaves and its potential for application in agricultural monitoring and disease detection. The model's performance was evaluated using metrics such as precision, recall, and mAP, and the results demonstrated the model's high accuracy and efficiency in identifying and categorizing diseased tomato leaves. The study concludes that YOLOV8s is a highly effective model for detecting diseased tomato leaves, with significant improvements in detection accuracy and speed compared to previous versions.