The ability to measure flying insect activity and abundance is important for ecologists, conservationists and agronomists alike. However, existing methods are laborious and produce data with low temporal resolution (e.g. trapping and direct observation), or are expensive, technically complex, and require vehicle access to field sites (e.g. radar and lidar entomology). We propose a method called "Camfi" for long-term non-invasive population monitoring and high-throughput behavioural observation of low-flying insects using images and videos obtained from wildlife cameras, which are inexpensive and simple to operate. To facilitate very large monitoring programs, we have developed and implemented a tool for automatic detection and annotation of flying insect targets in still images or video clips based on the popular Mask R-CNN framework. This tool can be trained to detect and annotate insects in a few hours, taking advantage of transfer learning. Our method will prove invaluable for ongoing efforts to understand the behaviour and ecology of declining insect populations and could also be applied to agronomy. The method is particularly suited to studies of low-flying insects in remote areas, and is suitable for very large-scale monitoring programs, or programs with relatively low budgets.
Keywords: Camfi; computer vision; flight behaviour; image analysis; insect conservation; insect ecology; population monitoring; remote sensing.
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