Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases.

Abstract

Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.

Plant phenotyping relevance

リンゴ葉の病害状態を画像から検出するYOLOv5改良モデルを開発し、複数モデルとの性能比較で検証しているため、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 apple leaf disease detection dataset (12,500 labeled images of five apple leaf diseases) is explicitly stated to be publicly available online at a Baidu AI Studio dataset URL in the Data Availability Statement. No author analysis code or trained model checkpoints are reported as publicly available.