Image recognition for crop diseases using a novel multi-attention module

Deep convolution neural networks constitute a breakthrough in computer vision. Based on this, the Convolutional Neural Network (CNN) models offer enormous potential for crop disease classification. However, significant training data are required to realize their potential. In the case of crop disease image recognition, especially with complex backgrounds, it is sometimes difficult to acquire adequately labeled large datasets. This research proposed a solution to this problem that integrates multi-attention modules, i.e., channel and position block (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. This provides a simple yet effective intermediate attention structure for CNNs. The module is also lightweight and produces little overhead. Some experiments on cucumber and rice image datasets with complex backgrounds were conducted to validate the effectiveness of the CPB module. The experiments included different module locations and class activation map display characteristics. The classification accuracy reached 96.67% on the cucumber disease image dataset and 95.29% on the rice disease image dataset. The results show that the CPB module can effectively classify crop disease images with complex backgrounds, even on small-scale datasets, which providing a reference for crop disease image recognition method under complex background conditions in the field.