Resumen
Traditional traps for Spodoptera frugiperda (J. E. Smith) monitoring require manual counting, which is time-consuming and laborious. Automatic monitoring devices based on machine vision for pests captured by sex pheromone lures have the problems of large size, high power consumption, and high cost. In this study, we developed a micro- and low-power pest monitoring device based on machine vision, in which the pest image was acquired timely and processed using the MATLAB algorithm. The minimum and maximum power consumption of an image was 6.68 mWh and 78.93 mWh, respectively. The minimum and maximum days of monitoring device captured image at different resolutions were 7 and 1486, respectively. The optimal image resolutions and capture periods could be determined according to field application requirements, and a micro-solar panel for battery charging was added to further extend the field life of the device. The results of the automatic counting showed that the counting accuracy of S. frugiperda was 94.10%. The automatic monitoring device had the advantages of low-power consumption and high recognition accuracy, and real-time information on S. frugiperda could be obtained. It is suitable for large-scale and long-term pest monitoring and provides an important reference for pest control.