DFN-PSAN: Multi-level deep information feature fusion extraction network for interpretable plant disease classification

Guowei Dai, Zhimin Tian, Jingchao Fan, C. K. Sunil, Christine Dewi

Abstract: Highlights•A novel DFN-PSAN model is proposed to identify crop diseases in natural farmland environments accurately.•The multi-level Deep Information Feature Fusion Network (DFN), which can effectively extract and fuse relevant features from different network layers, improves the localization of infected plant disease areas.•Pyramid Squeeze Attention (PSA) fuses contextual information at different scales and produces better pixel-level attention.•The construction of the PSA attention classification network (PSAN) can effectively utilize the important feature information of DFN to achieve competitive performance on complex disease symptom datasets in the field.•The t-SNE and SHAP methods enhance the transparency of the model in terms of feature clustering and discrimination of multi-category disease attention, respectively.