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

摘要： 针对农作物病害图像识别模型存在参数量较大内存占用较多、识别准确率不高及训练速度慢等问题，提出了融合注意力模块的多尺度卷积网络叶片病害图像识别模型。该网络模型基于残差网络模块，利用多尺度卷积取代了传统的单尺度卷积，使得网络加宽以获取更多的特征信息，避免网络堆叠过深引起的过拟合现象；同时为了加快模型训练速度，采用深度可分离卷积代替传统卷积减少模型参数量；将注意力机制引入到残差网络中，增强了模型的关键特征信息的提取能力，从而提高了模型的识别精度。通过对试验数据集进行对比试验，改进网络模型的识别准确率达到99.48%并且模型参数量仅有19.06?MB，试验结果表明所提出的方法能有效地提高模型的识别性能并降低模型参数量，为实现低成本终端运行奠定基础。

关键词: 图像识别, 多尺度卷积, 深度可分离卷积：注意力模块