FGBNet: A Bio-Subspecies Classification Network with Multi-Level Feature Interaction

Biodiversity is a foundation for maintaining ecosystem health and stability, while precise species identification is crucial to monitoring and protecting ecosystems. Subspecies of organisms, as carriers of genetic diversity, play key roles in ecosystem stability and adaptive evolution. Accurate identification of subspecies helps deepen our understanding of species distribution, ecological relationships, and change trends, providing a scientific basis for effective protection strategies. Therefore, this study proposes FineGrained-BioNet (FGBNet), a deep learning network model specifically constructed for fine-grained bio-subspecies image classification. The model combines a detail information supplement module, multi-level feature interaction, and a coordinate attention (CA) mechanism to improve the accuracy and efficiency of bio-subspecies classification. Through experimentation and optimization, the ConvNeXt is selected as the backbone network for FGBNet feature extraction, and the effectiveness of the multi-level feature interaction method is verified. Additionally, the optimal placement of the CA mechanism within the network is also explored. The experimental results show that, compared with ConvNeXt-Tiny, FGBNet achieved an increase of 6.204% in accuracy by increasing parameter quantity by only 5.702%, reaching an accuracy of 90.748%. This indicates that FGBNet significantly improves classification accuracy while maintaining computational efficiency. The proposed method facilitates more accurate subspecies classification, promoting the development of biodiversity monitoring and providing strong technical support for biodiversity conservation.