Fine-grained few-shot class-incremental identification of medicinal plants via frequency-aware contrastive learning

Developing robust algorithmic tools for accurately identifying diverse medicinal plant species is critical for advancing precision medicine. Although deep learning methods have shown considerable promise, they generally require large-scale annotated datasets, which are often difficult to acquire given the vast taxonomic diversity and limited labeled samples available for many plant species. To address this, we propose a novel Frequency-Aware Guided Domain Enhancement Contrastive Learning (FGDE) framework, designed to incrementally learn new categories from few annotated examples while alleviating catastrophic forgetting and overfitting. Our approach integrates high- and low-frequency components to refine feature representations, using multi-frequency fusion to preserve detail-enhanced information. Contrastive learning is further employed to strengthen multi-semantic aggregation and extract discriminative features across both visual and label domains. Additionally, we introduce a multi-objective loss function to enhance semantic compactness within base classes and improve separation among incremental classes. Extensive experiments demonstrate that FGDE significantly outperforms state-of-the-art methods on our collected dataset and two public benchmarks. These results underscore the potential of our model to support practical applications in intelligent plant identification and precision agriculture.