Tomato Leaf Disease Detection and Management System Based on YOLOv5

his study presents a Tomato Leaf Disease Detection and Management System utilizing the YOLOv5 deep learning algorithm. The system is designed to accurately identify various tomato leaf diseases, including Yellow Leaf Curl Virus and early blight, using a comprehensive dataset of labeled images. Through extensive training and validation, the model demonstrated high accuracy and efficiency, with a mean average precision (mAP) of 0.919 at a 0.5 IoU threshold. The findings highlight the potential of deep learning techniques in enhancing agricultural disease management by providing timely and precise disease detection. Practical applications include automated disease monitoring in agricultural fields, integration with precision agriculture technologies, and scalability to other crops. Despite promising results, the study acknowledges limitations related to data diversity and model generalizability, suggesting future research directions such as expanding the dataset and refining the model architecture. The implications of this research are significant for advancing sustainable agricultural practices and improving crop health and productivity. Through continued development and field trials, this system can become an essential tool for farmers and agronomists, contributing to the broader adoption of smart agriculture technologies.