DASO-RiceNet: A Sequential Dual-Attention Network for Fine-Grained Rice Disease and Damage Classification

Abstract

Rice diseases and pest-related damage severely threaten agricultural productivity and global food security. While deep learning has advanced automated crop diagnostics, distinguishing visually similar disease symptoms and damage patterns remains challenging due to subtle visual variations and complex backgrounds. To address this challenge, we introduce DASO-RiceNet (Dual-Attention Semantic Optimization Network), a deep learning framework for fine-grained rice disease and damage classification. The architecture utilizes a multi-stage residual backbone for feature extraction and a sequential dual-attention module combining channel and spatial attention to emphasize diagnostically relevant features while suppressing background information. Evaluated on a ten-class rice disease and damage dataset under a consistent experimental protocol, DASO-RiceNet achieved an accuracy of 0.965, macro precision of 0.963, macro recall of 0.966, macro F1-score of 0.964, and micro F1-score of 0.965, outperforming the evaluated CNN- and transformer-based baseline models. Furthermore, Grad-CAM and LIME provided qualitative insights into model predictions, with the examined examples showing attention to visually apparent symptom-related regions. These results demonstrate the potential of DASO-RiceNet for automated rice disease and damage classification and precision-agriculture applications.