Enhancing Feature Extraction based on Hierarchical Vision Transformer for Natural Enemy Recognition

Recognizing natural enemies is crucial in ecological management and agricultural pest control, but existing methods face challenges in feature extraction and classification accuracy. This paper proposes a novel approach to enhance feature extraction based on Transformer models, leveraging their powerful global attention mechanism, representation learning capabilities. The proposed method integrates the strengths of convolutional neural networks (CNN) and Transformers to improve the robustness of recognition in diverse environments. A significant contribution of this work is the development of a standardized benchmark dataset, carefully curated to represent a wide range of natural enemy species and environmental conditions. The dataset includes high-quality annotated images, ensuring its applicability in training and evaluating deep learning models. Experimental results demonstrate that the Transformer-based feature extraction model outperforms state-of-the-art methods not only in terms of computational efficiency but also in terms of accuracy and robustness. This research provides a comprehensive approach to the recognition of natural enemies, with potential applications in sustainable agriculture and monitoring biodiversity.