Advances in International Applied Mathematics

Smart agriculture is the development direction of future agriculture. The most crucial issue in managing crop diseases is to accurately determine the type of disease. In order to further improve the accuracy of crop disease recognition, an improved ConvNeXt method for crop disease recognition is proposed. To enhance the feature extraction ability of the network, a parameter free attention module SimAM is added to the network structure of ConvNeXt; In order to enhance the characteristics of channels, an ECA attention module is added to the basic module of the model; Conduct experiments on the publicly available dataset PlantVillage. The results show that compared with the original ConvNeXt model, the improved method achieves a recognition accuracy of 99.36% without increasing the number of parameters, which is 2.02 percentage points higher than the original model, providing a reference for automated crop recognition.

Smart agriculture; Disease identification; ConvNeXt; SimAM; Attention mechanism