An improved YOLOv5-based apple leaf disease detection method.

Abstract

The effective identification of fruit tree leaf disease is of great practical significance to reduce pesticide spraying, improve fruit yield and realize ecological agriculture. Computer vision technology can be effectively identifying and prevent plant diseases and insect pests. However, the lack of consideration of disease diversity and accuracy of existing detection models hinders their application and development in the field of plant pest detection. This paper proposes an efficient detection model of apple leaf disease spot through the improvement of the traditional Yolov5 detection network called A-Net. In order to significantly increase the A-Net's detection speed and accuracy, the A-Net model applies the loss function Wise-IoU, which includes the attention mechanism and the dynamic focusing mechanism, to the Yolov5 network model. The RepVGG module is then used to replace the original model's convolution module. The experimental results show that the improved model effectively suppresses the growth of some error weights. Compared with several object detection models, the improved A-Net model has a Mean Average Precision across IoU threshold 0.5 and an accuracy of 92.7%, which fully proves that the improved A-Net model has more advantages in detecting apple leaf diseases.

Plant phenotyping relevance

リンゴ葉の病斑を画像から検出するYOLOv5改良手法を開発し、他モデルとの精度比較で検証しており、植物病害状態の表現型取得が中心である。

Code and data availability

The paper describes a custom apple leaf disease dataset (12,000 images, partly from Baidu Flying Paddle) and an improved YOLOv5 model, but provides no public deposit, URL, or availability statement for the dataset, code, or trained model. The Data availability statement only says data are included in the article itself

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