Environmental microorganism recognition from microscopic images is crucial for environmental monitoring and ecological analysis. In practical scenarios, microorganism categories often evolve over time, and newly emerging classes usually have only a few labeled samples due to high annotation costs. This combination naturally gives rise to the few-shot class-incremental learning (FSCIL) problem. FSCIL requires models to incrementally learn new classes under severe data scarcity while effectively retaining knowledge of previously learned ones. In this work, we propose a unified FSCIL framework for environmental microorganism recognition. The proposed method is composed of three complementary components. First, a contrastive-inspired fine-grained representation learning strategy is introduced in the base session. This strategy enhances intra-class compactness by mining prediction-consistent augmented samples, without introducing explicit contrastive losses. Second, a prototype rectification mechanism is designed to stabilize the representations of incremental classes by leveraging semantic structures learned from base classes. Third, a dual-graph knowledge distillation framework is proposed to preserve both instance-level and class-level relational knowledge during incremental learning. This process is guided by a teacher model updated via exponential moving average. Experiments conducted on the EMDS-7 dataset demonstrate the effectiveness of the proposed approach. Compared with state-of-the-art FSCIL methods, our method achieves the highest average accuracy of 78.19% and maintains the best final-session accuracy of 65.36%. Meanwhile, strong base-session performance is consistently preserved. These results indicate that the proposed framework effectively mitigates catastrophic forgetting and enables robust adaptation to new microorganism categories in real-world incremental recognition scenarios.
Xu et al. (Mon,) studied this question.