Enhanced multiscale plant disease detection with the PYOLO model innovations.

Abstract

Timely detection of plant diseases is crucial for agricultural safety, product quality, and environmental protection. However, plant disease detection faces several challenges, including the diversity of plant disease scenarios and complex backgrounds. To address these issues, we propose a plant disease detection model named PYOLO. Firstly, the model enhances feature fusion capabilities by optimizing the PAN structure, introducing a weighted bidirectional feature pyramid network (BiFPN), and repeatedly fusing top and bottom scale features. Additionally, the model's ability to focus on different parts of the image is improved by redesigning the EC2f structure and dynamically adjusting the convolutional kernel size to better capture features at various scales. Finally, the MHC2f mechanism is designed to enhance the model's ability to perceive complex backgrounds and targets at different scales by utilizing its self-attention mechanism for parallel processing. Experiments demonstrate that the model's mAP value increases by 4.1% compared to YOLOv8n, confirming its superiority in plant disease detection.

Plant phenotyping relevance

植物病害を画像から検出するPYOLOモデルを開発し、YOLOv8nとの性能比較で検証しているため、植物の病害状態を推定する画像ベースの表現型計測手法が中心である。

Code and data availability

The paper's authors publicly release the YOLOv8-based PYOLO/YOLO-ESC model code used for plant disease detection via a GitHub release, matching an allowed URL. No separate phenotype dataset or trained checkpoint is explicitly deposited in the supplied text.