Algorithm for Crop Disease Detection Based on Channel Attention Mechanism and Lightweight Up-Sampling Operator

Abstract

Crop diseases and pests cause significant economic losses to agriculture every year, making accurate identification crucial. Traditional pest and disease detection relies on farm experts, which is often time-consuming. Computer vision technology and artificial intelligence can provide automated disease detection, enabling real-time precise control of crop diseases and timely prevention measures. To accurately identify plant diseases under complex natural conditions, we developed an improved crop pest and disease recognition model based on the original YOLOv5 network. First, we integrated the Squeeze-and-Excitation (SE) module into YOLOv5, allowing our proposed model to better distinguish leaf features of different crops and accurately identify disease types. Second, to enhance the model’s feature extraction capability for diseased areas and reduce the loss of disease feature information, we replaced the original Up-sample module in YOLOv5 with a lightweight up-sampling operator, the CARAFE module. Third, we improved the original loss function using the EIoU loss function to increase the model’s detection accuracy. Lastly, to reduce model complexity and meet real-time detection requirements, we introduced the Ghost Convolution module into the backbone network. During the experimental phase, to validate the model’s effectiveness, we randomly divided sample images from the constructed crop pest and disease database into training, validation, and test sets. Experimental results showed that the improved YOLOv5 model achieved an accuracy of 90.0%, a recall rate of 91.4%, [email protected] of 92.1%, and [email protected]:.95 of 64%. The parameter count and computational load were reduced by 23.9% and 31.2%, respectively, outperforming popular methods including YOLOv5, YOLOv7, and YOLOv8. The improved model can accurately identify crop pests and diseases under natural conditions and is suitable for deployment in real-world applications, providing a technical reference for crop pest and disease management.

Keywords

• Crop diseases and insect pests
• YOLOV5
• CARAFE
• SE
• EIoU loss function
• ghost convolution