关键词: 图像识别, 多尺度卷积, 深度可分离卷积：注意力模块

Abstract: To solve the problems of large number of parameters , low recognition accuracy and slow training speed on the crop disease image recognition model, a multi-scale convolutional network leaf disease image recognition model with attention module is proposed. Based on the residual network module, multi-scale convolution replaces the traditional single-scale convolution, so that the network is widened to obtain more feature information, avoid overfitting caused by network stacking too deep. In order to speed up the model training, depthwise separable convolution is used instead of tradition convolution to reduce the number of model parameters, the attention mechanism is introduced into the residual network to enhance the extraction ability of key feature information of the model, thus improving the recognition accuracy of the model. Through the comparative test of the experimental data set, the recognition accuracy of the improved network model reaches 99.48% and the parameters is only 19.06?MB, the experimental results show that the proposed method can effectively improve the recognition performance of the model and reduce the parameters, which lays a foundation for the realization of low-cost terminal operation.

Key words: image recognition, multi-scale convolution, depthwise separable convolution, attention module