Toward a Hardware Implementation of Lidar-based Real-time Insect Detection

Real-time monitoring of insects has important applications in entomology, such as managing agricultural pests and monitoring species populations-which are rapidly declining. However, most monitoring methods are labor intensive, invasive, and not automated. Lidar-based methods are a promising, non-invasive alternative, and have been used in recent years for various insect detection and classification studies. In a previous study, we used supervised machine learning to detect insects in lidar images that were collected near Hyalite Creek in Bozeman, Montana. Although the classifiers we tested successfully detected insects, the analysis was performed offline on a laptop computer. For the analysis to be useful in real-time settings, the computing system needs to be an embedded system capable of computing results in real-time. In this paper, we present work-in-progress towards implementing our software routines in hardware on a field programmable gate array.