Residual Derivative-Guided Spectral Fusion Module for Few-Shot Classification of Soybean Seed Varieties Using...

Soybean seed variety identification is essential for seed quality control, germplasm management, and variety authentication. However, few-shot classification remains challenging because different varieties often exhibit highly similar one-dimensional hyperspectral signatures, and labeled samples are limited in practical seed-testing scenarios. This study proposes a Residual Derivative-Guided Spectral Fusion (RDSF) module to improve spectral representation under limited-sample conditions. RDSF uses the raw spectrum and its first- and second-order derivatives to characterize global reflectance patterns, local slope variations, and spectral curvature, respectively. The three representations are processed by separate branches and combined through bounded learnable residual fusion, with the raw spectrum serving as the primary representation and the derivatives providing complementary corrections. As a plug-and-play component, RDSF was integrated into Prototypical Network (ProtoNet), Relation Network (RelationNet), and Model-Agnostic Meta-Learning (MAML). The module was evaluated using spectra from 11,000 individual soybean seeds representing 11 varieties under known-class and strict class-disjoint unseen-class protocols. Under the representative known-class 3-way 10-shot setting with 15 query samples per class, RDSF increased the meta-test accuracy of RelationNet from 0.8898 ± 0.0201 to 0.9184 ± 0.0060. Under the unseen-class protocol, RDSF consistently improved ProtoNet and RelationNet across all evaluated shot settings; the largest gain was observed for ProtoNet in the 5-shot setting, with the meta-test accuracy increasing from 0.8848 ± 0.0253 to 0.9094 ± 0.0080. In contrast, RDSF did not consistently improve MAML under this protocol, indicating that its effectiveness depended partly on the underlying meta-learning mechanism. Ablation experiments and architecture comparisons further showed the complementary contributions of the derivative branches and the advantages of bounded residual fusion over a three-channel architecture and direct feature concatenation. Overall, RDSF provides an effective spectral representation module for metric-based few-shot classification of soybean seed varieties under the evaluated known-class and unseen-class conditions.