关键词: 叶片病害识别, 通道信息注意力网络, 模型融合, 卷积神经网络

Abstract:

In this paper, an attention network model with channel information is proposed to identify various plant leaf diseases. Firstly, a residual structure based basic network is built for feature extraction, and then the feature is re-calibrated by integrating multiple channel information through the attention network. Then, the constraint information is added to the cross entropy function to accelerate the convergence of the model. Finally, the model is tested on the data sets of 16 diseases of four different plants. The results show that the recognition accuracy of the basic network model is 83.13%, while the accuracy increases by 4.64 percentage points after the channel information network is fused. Compared with other models, the fusion model improves the recognition accuracy by 9.72 percentage points and the model complexity is about 1/2 of that of the optimal model in the comparison experiment.

Key words: identification of leaf disease, channel information attention network, model fusion, Convolutional Neural Network（CNN）