A Study on Maize Leaf Pest and Disease Detection Model Based on Attention and Multi-Scale Features

Abstract

The detection and accurate positioning of agricultural pests and diseases can significantly improve the effectiveness of disease and pest control and reduce the cost of prevention and control, which has become an urgent need for crop production. Aiming at the low precision of maize leaf pest and disease detection, a new model of maize leaf pest and disease detection using attention mechanism and multi-scale features was proposed. Our model combines a convolutional block attention module (CBAM) with the ResNet50 backbone network to suppress complex background interference and enhance feature expression in specific regions of the maize leaf images. We also design a multi-scale feature fusion module that aggregates local and global information at different scales, improving the detection performance for objects of varying sizes. This module reduces the number of parameters and enhances efficiency by using a lightweight module and replacing the deconvolutional layer. Experimental results on a natural environment dataset demonstrate that our proposed model achieves an average detection accuracy of 85.13%, which is 9.59% higher than the original CenterNet model. The model has 24.296 M parameters and a detection speed of 23.69 f/s. Compared with other popular models such as SSD-VGG, YOLOv5, Faster-RCNN, and Efficientdet-D0, our proposed model demonstrates superior performance in the fast and accurate detection of maize leaf pests and diseases. This model has practical applications in the identification and treatment of maize pests and diseases in the field, and it can provide technical support for precision pesticide application. The trained model can be deployed to a web client for user convenience.