关键词: 辣椒病虫害, 精准识别, 轻量化模型, 注意力机制, 深度学习

Abstract: In order to address the issues of numerous network parameters, large model memory and long training time in recognizing diseases and pests on pepper leaves in natural environments by using the GoogLeNet model, a lightweight GoogLeNet model incorporating the CBAM mechanism (CBAM—GoogLeNet) is proposed. In this model, Inception (4b) and Inception (4c) modules are replaced by the CBAM attention mechanism, while this attention mechanism is inserted into  the average pooling layer, and L2 regularization is added in the fully connected layer, so as to reduce the model size and shorten the training time, while ensuring high accuracy and validation rates of the network model. A visual pepper disease and pest identification system is also designed by using the MATLAB platform. The results show that the size of the CBAM—GoogLeNet model is reduced by 91.2%, 96.2%, 96.3%, and 15.0% compared to AlexNet, VGG16, VGG19, and GoogLeNet, respectively. The training time is reduced by 12.7%, 26.5%, 62.2%, and 8.8%, respectively. Additionally, the model achieves an identification accuracy of 99.5% and a validation accuracy of 97.3%, realizing the goals of model lightweighting and fast, accurate recognition. This provides effective technical support for timely prevention and control of pepper diseases and pests, and the reduction of losses.

Key words:  , pepper pests and diseases, accurate recognition, lightweight model, attention mechanism, deep learning