Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms.

ABSTRACT: Pests are a major cause of crop loss globally, and accurate pest identification is crucial for effective prevention and control strategies. This paper proposes a novel deep-learning architecture for crop pest classification, addressing the limitations of existing methods that struggle with distinguishing the fine details of pests and background interference. The proposed model is designed to balance fine-grained feature extraction with deep semantic understanding, utilizing a parallel structure composed of two main components: the Feature Fusion Module (FFM) and the Mixed Attention Module (MAM). FFM focuses on extracting key fine-grained features and fusing them across multiple scales, while MAM leverages an attention mechanism to model long-range dependencies within the channel domain, fu

SUBMITTER: Qian Y 

PROVIDER: S-EPMC12003288 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature