A hierarchical prototype-graph with optimal-transport matching for few-shot rice disease recognition.
TL;DR
A domain-specific prototype-based few-shot framework that avoids pretrained visual backbones and treats rice disease recognition as structured matching over a pathogen-aware class graph is formulated, making it well-suited for real-world agricultural deployment under limited supervision.
Abstract
Accurate identification of rice diseases from field images is critical for crop health monitoring and sustainable agriculture, particularly in low-resource environments. However, most deep learning approaches depend on large-scale labeled datasets and pretrained backbones, limiting their applicability to rare or emerging diseases. In this work, we formulate a domain-specific prototype-based few-shot framework that avoids pretrained visual backbones and treats rice disease recognition as structured matching over a pathogen-aware class graph. The individual components, including wavelet-scattering features, optimal transport, semantic prototype fusion, and transductive refinement, are established techniques; the contribution lies in their coupled use within a disease-taxonomy-guided few-shot matching process. This design combines fixed visual descriptors, root-to-leaf prototype matching, class symptom descriptors, and confidence-gated refinement to support rice disease recognition under limited labeled data. We evaluate the model on two publicly available rice disease datasets-the Philippines Rice Diseases and Roboflow Rice-under 1-shot and 5-shot classification settings. In in-domain experiments, our approach achieves up to 95.8% accuracy and 94.9% macro-F1 on the Philippines dataset, consistently outperforming a diverse set of baselines including CNN-from-scratch, ResNet-18-from-scratch, Matching Networks, MAML, ProtoNet, RelationNet, SimpleShot, FEAT, and a flat optimal-transport variant. In cross-domain evaluation, the model demonstrates strong generalization capability, attaining up to 91.7% accuracy and 90.6% macro-F1 when transferring across datasets. An ablation study further confirms the consistent contribution of hierarchical structure, semantic fusion, and transductive refinement to performance gains. These results demonstrate that the proposed framework delivers highly accurate, robust, and data-efficient disease recognition, making it well-suited for real-world agricultural deployment under limited supervision.