Multi-Granularity Feature Aggregation with Self-Attention and Spatial Reasoning for Fine-Grained Crop Disease Classification

Combining disease categories and crop species leads to complex intra-class and inter-class differences. Significant intra-class difference and subtle inter-class difference pose a great challenge to high-precision crop disease classification tasks. To this end, we propose a multi-granularity feature aggregation method for accurately identifying disease types and crop species as well as better understanding the disease-affected regions implicitly. Specifically, in order to capture fine-grained discriminating clues to disease categories, we first explored the pixel-level spatial self-attention to model the pair-wise semantic relations. Second, we utilized the block-level channel self-attention to enhance the feature-discriminative ability of different crop species. Finally, we used a spatial reasoning module to model the spatial geometric relationship of the image patches sequentially, such that the feature-discriminative ability of characterizing both diseases and species is further improved. The proposed model was verified on the PDR2018 dataset, the FGVC8 dataset, and the non-lab dataset PlantDoc. Experimental results demonstrated our method reported respective classification accuracies of 88.32%, 89.95%, and 89.75% along with F1-scores of 88.20%, 89.24%, and 89.13% on three datasets. More importantly, the proposed architecture not only improved the classification accuracy but also promised model efficiency with low complexity, which is beneficial for precision agricultural applications.

Automated summary

This study proposes a multi-granularity feature aggregation method for accurately identifying disease types and crop species, as well as better understanding the disease-affected regions. The method achieves high classification accuracies and F1 scores on multiple datasets, while maintaining low complexity, making it suitable for precision agricultural applications.