Ramos Leo Thomas, Sappa Angel D
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
ESPOL Polytechnic University, 090112, Guayaquil, Ecuador.
Sci Rep. 2025 Jul 24;15(1):26890. doi: 10.1038/s41598-025-11064-0.
Tomato leaf disease detection is critical in precision agriculture for safeguarding crop health and optimizing yields. This study compares the latest YOLO architectures, including YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, using the Tomato-Village dataset, which contains 14,368 images across six disease classes. All models are trained under identical settings to ensure a fair evaluation based on precision, recall, mean Average Precision, training time, and inference speed. Results show that YOLOv11 consistently outperforms the other architectures, achieving the highest accuracy with competitive training times and acceptable latency. YOLOv10, YOLOv8, and YOLOv12 also deliver strong results, with YOLOv12n emerging as the most effective lightweight model for resource-constrained environments. In contrast, YOLOv9 demonstrates the weakest performance, requiring more training time and exhibiting higher latency. Overall, YOLOv11 is positioned as the most effective solution for tomato leaf disease detection, providing a strong benchmark for future advancements in agricultural technology.
番茄叶部病害检测对于精准农业中保障作物健康和优化产量至关重要。本研究使用了包含六个病害类别的14368张图像的番茄村数据集，比较了最新的YOLO架构，包括YOLOv8、YOLOv9、YOLOv10、YOLOv11和YOLOv12。所有模型均在相同设置下进行训练，以确保基于精度、召回率、平均精度均值、训练时间和推理速度进行公平评估。结果表明，YOLOv11始终优于其他架构，在具有竞争力的训练时间和可接受的延迟下实现了最高精度。YOLOv10、YOLOv8和YOLOv12也取得了不错的结果，其中YOLOv12n成为资源受限环境中最有效的轻量级模型。相比之下，YOLOv9表现最弱，需要更多训练时间且延迟更高。总体而言，YOLOv11被定位为番茄叶部病害检测最有效的解决方案，为未来农业技术的进步提供了有力的基准。
