Perbandingan Deep Learning YOLOv5 dan YOLOv8 Untuk Deteksi Penyakit Daun Tanaman Tomat
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Abstract
Agriculture is one of the mainstays of the country's economy, especially the horticulture sub-sector such as fruits and vegetables. Tomato plants are one of the leading commodities. However, the failure of tomato cultivation due to the many types of diseases that exist is still an obstacle and interferes with plant growth, reduces yields, and even causes tomato plant death. This study aims to detect tomato leaf diseases by comparing the performance of the two YOLOv5 and YOLOv8 models. The purpose of comparing models is to determine the level of accuracy and to conclude which version of YOLO provides a better level of accuracy in the hope of helping to determine which method is most appropriate and appropriate to needs. The results showed that both YOLOv5m and YOLOv8m models performed very well in detection. Both models showed high precision, recall, and mAP values. YOLOv8m is better able to detect all objects in the image where the precision value is superior to YOLOv5m. YOLOv8m is superior in precision with a value of 0.95%, a difference of 0.02% with YOLOv5m and mAP50:95 which is 0.92%, a difference of 0.02% with YOLOv5m which means that YOLOv8m is better at identifying objects very precisely and objects of various sizes, but YOLOv8m requires a slightly longer training time than YOLOv5m. YOLOv8m is better able to detect all objects in the image where the precision value is 0.02% superior to YOLOv5m
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