Comparative Analysis of the Performance of YOLO11 and YOLOv8 Models in Identification of Diseases in Tomato Leaves

Abstract

Diseases on tomato leaves can reduce the quality and quantity of agricultural yields, as well as affect market prices. This study compares the effectiveness of the YOLO11 and YOLOv8 models in detecting diseases on tomato leaves with traditional CNN-based models such as VGG-16 and Inception-V3. The results show that the YOLO11 model provides the best accuracy of 99.4%, followed by YOLOv8 with 98.5%, both excelling in real-time detection. CNN-based models like VGG-16 and Inception-V3 have high accuracy (99% and 93.8%), but are slower in computation. The ensemble model of VGG-16 and NASNet Mobile achieves an accuracy of 98.7%, but is slightly lower than YOLO11. The YOLO model is more efficient in detection speed, making it a better choice for field applications. This study shows that YOLO11 offers the best combination of accuracy and detection speed for a real-time plant disease detection system.