A dual-branch neural network for crop disease recognition by integrating frequency domain and spatial domain information

Crop diseases severely affect crop yield and quality, and accurate identification of crop diseases is crucial for disease management. Although deep neural networks have made progress in the task of crop disease identification, the complex environment of crop diseases, including background interference, morphological differences, and scale variations, has led to limited accuracy in disease recognition. To address these issues, this study proposes a dual-branch deep neural network for crop disease identification, integrating both frequency domain and spatial domain information. The frequency branch takes frequency domain information as input to extract rich crop disease frequency component features, while the deformable attention Transformer branch excels in representing global features and selectively focusing on local features of crop diseases. A new fusion method, Multi-Spectral Channel Attention Fusion (MSAF), is adopted to better integrate crop disease frequency and spatial features. Additionally, an improved bias loss function (cv_bias) is proposed to optimize the dual-branch network model, achieving an accuracy of 96.7 % on the test dataset, which is 2.0 % higher than the existing state-of-the-art deformable attention Transformer model. With only 14 M model parameters, this study's model provides an effective method for future applications in complex environment.