FewMedical-XJAU: A Challenging Benchmark for Fine-Grained Medicinal Plant Classification

Fine-grained plant image classification (FPIC) aims to distinguish plant species with subtle visual differences, but existing datasets often suffer from limited category diversity, homogeneous backgrounds, and insufficient environmental variation, limiting their effectiveness in complex real-world scenarios. To address these challenges, a novel dataset, FewMedical-XJAU, is presented, focusing on rare medicinal plants native to Xinjiang, China. This dataset offers higher intra-class variability, more complex and diverse natural backgrounds, varied shooting angles and lighting conditions, and more rigorous expert annotations, providing a realistic testbed for FPIC tasks. Building on this, an improved method called BDCC (Bilinear Deep Cross-modal Composition) is proposed, which incorporates textual priors into a deep metric learning framework to enhance semantic discrimination. A Class-Aware Structured Text Prompt Construction strategy is introduced to improve the model's semantic understanding, along with a dynamic fusion mechanism to address high inter-class similarity and intra-class variability. In few-shot classification experiments, the method demonstrates superior accuracy and robustness under complex environmental conditions, offering strong support for practical applications of fine-grained classification.