A Study on Intelligent Insect Image Recognition Based on Overlap Patch Relevance Extraction and Classification

Insect recognition plays a crucial role in agricultural production and maintaining ecological balance. However, the vast variety and differing forms of insects make traditional image recognition methods, which rely on manual identification, low in accuracy and efficiency. This study aims to explore the application of deep learning technology in insect image recognition by proposing a deep learning-based method to enhance recognition accuracy and efficiency through automatic extraction of image features using a deep learning model. We utilized a combination of web-collected insect images and existing datasets to provide comprehensive training data, and developed an advanced deep learning model named Advanced Overlap Patch Relevance Extraction and Classification (AOPREC). The model’s design includes overlapping patch operations to minimize redundancy in background information, enhancing the model’s ability to manage image details and local features. Additionally, we replaced the multilayer perceptron (MLP) in the vision transformer (ViT) with a more efficient classifier that combines convolutional neural networks (CNN) and long short-term memory (LSTM) structures, significantly improving classification performance. Furthermore, Gaussian error linear unit (GELU) was employed as the activation function to optimize training efficiency and generalization, ensuring consistent performance across various images. Experimental results demonstrate that the AOPREC model excels in insect image classification and object detection tasks, significantly improving recognition accuracy.