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Image Recognition of Cucumber Diseases Based on a New Attention Module

Deep convolutional neural network is an important breakthrough in the computer field, but deep convolutional neural network requires a large amount of labeled data for model training. It is difficult to build a large-scale crop disease image dataset in the agricultural field. In this paper, we propose a solution to this problem that uses a channel and position block (called a CPB module). Given an intermediate feature map, the CPB module can infer attention maps in parallel with the channel and position. The attention maps can then be multiplied to form input feature maps for adaptive feature refinement. We undertook extensive experiments to validate the effectiveness of the CPB module using our own crop disease image dataset. The experiments included using class activation map (CAM) display characteristics. The results showed that the CPB module can effectively classify the crop disease image.