DC-SE-DSCNet

Existing pest and disease identification models based on neural network image classification technology have high computational demands and prolonged processing times, which limit their widespread application. Therefore, further research on lightweight models is essential to improve recognition performance and enhance real-time processing capabilities. This study introduced a Dilated Convolution_Squeeze and Excitation_Depthwise Separable Convolution Network (DC-SE-DSCNet), which was a lightweight Convolutional Neural Network (CNN) designed for mobile devices and embedded systems in Mobile Edge Computing (MEC) environments. The model employed depthwise separable convolution (DSC) to replace traditional convolution, significantly reducing the parameter count and computational complexity. Additionally, Dilated Convolution (DC) expanded the receptive field, reduced the computational demands, and enhanced the feature extraction capabilities. The Squeeze-and-Excitation (SE) module was introduced as a channel attention mechanism to enhance the feature selection capabilities. This model integrated the max-pooling layer with the SE module to effectively preserve the critical feature information. The simulation results demonstrated that the DC-SE-DSCNet model achieved the accuracy of 94.14%, with a parameter count of only 0.17 M and the computational complexity of 0.48 GFLOPs. Overall, the DC-SE-DSCNet model effectively satisfied the requirements for low-cost, low-power, and high-performance devices.