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, further enhancing feature representation. Additionally, a Transformer block is integrated to overcome the limitations of traditional convolutional approaches in capturing global contextual information. The proposed architecture is evaluated on three benchmark datasets—IP102, D0, and Li—demonstrating its superior performance over state-of-the-art methods. The model achieves accuracies of 75.74% on IP102, 99.82% on D0, and 98.77% on Li, highlighting its robustness and effectiveness in complex crop pest recognition tasks. These results indicate that the proposed method excels in multi-scale feature fusion and long-range dependency modeling, offering a new competitive approach to pest classification in agricultural settings.