A comprehensive analysis of YOLO architectures for tomato leaf disease identification.

Abstract

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.

Plant phenotyping relevance

トマト葉の病害状態を画像から推定するYOLO手法を複数比較し、精度・推論速度・学習時間で技術評価しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper benchmarks YOLO models on the public Tomato-Village dataset, which the authors state is freely available on GitHub, but no explicit URL or repository identifier for the dataset or any authors' analysis code/trained models is provided in the supplied blocks, and no allowed URL corresponds to such an asset. The

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