A machine learning pipeline for automated insect monitoring
This addresses the need for large-scale insect monitoring to combat biodiversity decline, though it is incremental as it adapts existing camera trap methods to moths.
The authors tackled the problem of inadequate insect abundance data by developing a complete, open-source machine learning pipeline for automated moth monitoring via camera traps, resulting in tools already deployed across three continents for scalable data collection.
Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosystem services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.