Abstract
Three species of rice migratory pests (Cnaphalocrocis medinalis, Sogatella furcifera, and Nilaparvata lugens) cause severe yield and economic losses to rice food every year. It is important that these pests are timely and accurately monitored for controlling them and ensuring food security. Insect radar is effective monitoring equipment for migratory pests flying at high altitude. But insect radar is costly and has not been widely used in fields. Searchlight trap is an economical device, which uses light to trap migratory pests at high altitude. But the trapped pests need to be manually identified and counted from a large number of non-target insects, which is inefficient and labor-intensive. In order to replace manual identification of migratory pests, we develop an intelligent monitoring system of migratory pests based on searchlight trap and machine vision. This system includes a searchlight trap based on machine vision, an automatic identification model of migratory pests, a Web client, and a cloud server. The searchlight trap attracts the high-altitude migratory insects through lights at night and kills them with the infrared heater. All trapped insects are dispersed through a multiple layers of insect conveyor belts and a revolving brush. The machine vision module collects the dispersed insect images and sends them to the cloud server through 4G network. The improved model YOLO-MPNet based on YOLOv4 and SENet channel attention mechanism is proposed to detect three species of migratory pests in the images. The results show that the model effectively improves the detection effect of three migratory pests. The precision is 94.14% for C. medinalis, 85.82% for S. furcifera, and 88.79% for N. lugens. The recall is 91.99% for C. medinalis, 82.47% for S. furcifera, and 85.00% for N. lugens. Compared with some state-of-the-art models (Faster R-CNN, YOLOv3, and YOLOv5), our model shows a low false detection and missing detection rates. The intelligent monitoring system can real-timely and automatically monitor three migratory pests instead of manually pest identification and count, which can reduce the technician workload. The trapped pest images and historical data can be visualized and traced, which provides reliable evidence for forecasting and controlling migratory pests.