Insect-YOLO: A new method of crop insect detection

The utilization of the pest monitoring and reporting system has gained widespread adoption for automating the surveillance of field-dwelling pests, serving as a viable alternative to the labor-intensive and time-consuming manual inspection methods. Nevertheless, the heterogeneous spectrum and variable sizes of crop pests, coupled with the imperative to manage costs associated with camera lenses employed in practical agricultural scenarios, result in low-resolution images. This low resolution significantly amplifies the intricacy of pest identification. Our research is dedicated to the detection of insects in low-resolution images, we collected a large dataset of low-resolution images of common pests from agricultural fields, with resolutions ranging from 8 to 12 million pixels, and deployed the Insect-YOLO model based on this dataset. Tailored for capturing pests on diverse crops, Insect-YOLO boasts streamlined parameters, swift detection speeds, and exceptional accuracy. Enhanced by the Convolutional Block Attention Module (CBAM), it systematically extracts complex pest features, integrating multi-scale information to optimize feature representation. In comparative evaluations against YOLO v5, v7, v8, RetinaNet, and Faster R-CNN, Insect-YOLO demonstrated exceptional performance, achieving a mean Average Precision at IoU 0.5 (mAP50) of 93.8%, highlighting its superiority in pest detection. Simultaneously, linear regression analysis was performed to assess the correlation between the computer-detected and manually counted insect numbers, revealing a strong correlation that underscores the efficacy of our method. Ultimately, the pest detection algorithm was integrated into the "Remote Pest Monitoring and Analysis System" of the Agricultural IoT Monitoring Platform. This integration enables high accuracy and efficiency in detecting diverse pests from real-time, low-resolution field images and constitutes a critical component of a comprehensive pest monitoring system, serving as a foundation for pest prediction and intelligent monitoring technologies.