Tackling Metamorphosis and Complex Backgrounds: A Coarse-to-Fine Network for Fine-Grained Agricultural Pest Recognition

Timely and accurate identification of agricultural pests is imperative for precision crop protection. However, real-world pest recognition faces two critical challenges: the interference of complex field backgrounds, which introduces significant noise, and the severe large intra-class variance caused by pest metamorphosis, which confuses standard classifiers. To address these issues, this paper proposes a coarse-to-fine cascade framework that integrates object localization with fine-grained multi-modal classification. First, we deploy a YOLOv8-based detector to precisely localize and crop pest regions from cluttered environments, effectively eliminating background redundancy. Second, for the cropped targets, we design a fine-grained classification network based on ResNeXt50 integrated with the Convolutional Block Attention Module (CBAM) to extract discriminative features. Crucially, to tackle the challenge of multi-state pest morphologies, we propose a novel Adaptive Multi-Center Classification Head (AMC-Head). Unlike traditional methods that enforce a single feature center for each class, our approach dynamically allocates multiple latent sub-centers for each category, allowing the model to automatically disentangle and cluster distinct morphological representations within a single label. Extensive experiments on the large-scale benchmark dataset IP102 demonstrate that our method achieves an end-to-end accuracy of 91.4%, significantly outperforming single-stage baselines. The proposed framework effectively mitigates the impact of complex backgrounds and metamorphic variation, providing a robust solution for automated pest monitoring.