부산대학교 도서관

This unique method for recognising objects with YOLOv8 object detection will allow for the early identification of tomato diseases, which include blight, mould, and viral threats. And as this early identification is a key to global food security at this time, it is crucial. Automated technologies provide a solution to the cost and error-prone processes of classical diagnostics. Therefore, a tool based on them can improve diagnostic effectiveness. Based on YOLOv8’s fruit defect detection technique and the use of Deep Learning, it yields fast and precise results in the recognition and classification of common diseases of tomatoes. The idea of training the model by curated dataset form and hyperparameter tuning will further optimise the model performance. For instance, precision, recall, and F1-score, known as the metrics, help me in determining the efficacy. Interpretation of the results reveals that these algorithms are more accurate and efficient than previous ones, prompting scientists and farmers to use the algorithm to take preventive measures in their operations, such as agriculture, horticulture, and crop management.