Wallace Jesse Rudolf Amenuvegbe, Reber Therese Maria Joanna, Dreyer David, Beaton Brendan, Zeil Jochen, Warrant Eric
Research School of Biology, The Australian National University, Canberra, ACT, Australia.
National Collections & Marine Infrastructure, CSIRO, Parkville, VIC, Australia.
Front Insect Sci. 2023 Sep 13;3:1240400. doi: 10.3389/finsc.2023.1240400. eCollection 2023.
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.
测量飞行昆虫的活动和数量的能力对于生态学家、保护主义者和农学家来说都很重要。然而，现有的方法既费力，产生的数据时间分辨率又低（例如诱捕和直接观察），或者成本高昂、技术复杂，且需要车辆进入野外场地（例如雷达和激光雷达昆虫学）。我们提出了一种名为“Camfi”的方法，用于使用从野生动物相机获得的图像和视频对低空飞行昆虫进行长期非侵入性种群监测和高通量行为观察，这些相机价格低廉且操作简单。为了推动大规模监测项目，我们基于流行的Mask R-CNN框架开发并实现了一种用于在静止图像或视频片段中自动检测和标注飞行昆虫目标的工具。利用迁移学习，该工具可以在几个小时内训练完成以检测和标注昆虫。我们的方法对于当前了解昆虫种群数量下降的行为和生态的努力将被证明具有巨大价值，并且也可应用于农学领域。该方法特别适用于对偏远地区低空飞行昆虫的研究，适用于大规模监测项目或预算相对较低的项目。
