BGM-YOLO: An accurate and efficient detector for detecting plant disease.

Abstract

Given the complexity of crop growth environments in nature, where leaf backgrounds often include soil, weeds, and other plants, along with variable lighting conditions, and considering the small size of leaf spots and the wide variety of crop diseases with significant scale differences, this paper proposes a new BGM-YOLO model structure aimed at improving accuracy and inference speed. First, the GSBottleneck module is utilized to enhance the C2f module of the YOLOv8n model, leading to the introduction of the GSC2f module, which reduces computational costs and increases inference efficiency. Next, the model incorporates a multiscale bitemporal fusion module (BFM) to increase the effectiveness and robustness of feature fusion across different levels. Finally, we developed a median-enhanced spatial and channel attention block (MECS) that combines both channel and spatial attention mechanisms, effectively improving the capture and fusion of small-scale features. The experimental results demonstrate that the BGM-YOLO model achieves a 3.9% improvement in the mean average precision (mAP) over the original model. In crop disease detection tasks, the BGM-YOLO model has higher detection accuracy and a lower false negative rate, confirming its practical value in complex application scenarios.

Plant phenotyping relevance

植物の病斑・病害状態を画像から検出するYOLOベース手法を開発し、精度と推論速度を評価しており、植物病害フェノタイピング手法が中心です。

Code and data availability

The paper's Data Availability statement deposits the quantitative data files underlying the plant disease detection experiments in Figshare, providing a direct public URL (DOI). No separate author analysis code or trained model checkpoint is explicitly deposited in the supplied blocks.