Real-time stored product insect detection and identification using deep learning: System integration and extensibility to mobile platforms

Existing stored product insect monitoring methods are time-consuming, costly, and often require specialized equipment or training. This study proposed an integrated insect monitoring system that employs a simple RGB camera and data-driven deep-learning models to detect and identify stored product insect species in warehouses, food facilities, and retail environments. Top-down images of six common insect species were acquired with a setup simulating a conceptualized probe-type monitor under varying lighting and background conditions. These images were preprocessed and manually annotated, resulting in an insect dataset of 2630 images with 14,509 labeled insects. A state-of-the-art computer vision model from YOLO family was selected, and six YOLO variants (YOLOv5s/m/l and YOLOv5s/m/l) were trained and evaluated on the insect dataset. All trained YOLO models delivered an impressive performance in terms of high detection accuracy (above 76% for mAP@[0.50:0.95]) and fast inference time (12-36 ms range). Subsequently, the best-performing YOLOv8l model was integrated and deployed on a mobile device, achieving a good detection performance with average detection speeds of 16 and 29 fps on a desktop computer and smartphone, respectively. The study provided an end-to-end framework for automatic and real-time insect detection and identification in a stored product environment. The lightweight variant of YOLO can be deployed on low-cost edge hardware, mobile devices, or cloud computing. The proposed system is relatively fast, accurate, and inexpensive for insect monitoring and may prove an alternate solution to existing methods. The system would serve as a decision-making tool for stored product facilities managers and can be easily scaled and adapted in a variety of stored product environments.