A machine learning pipeline for automated insect monitoring

TL;DR

This paper presents an open-source machine learning pipeline for automated insect monitoring using camera traps, enabling scalable data collection and species identification to address insect decline.

Contribution

It introduces a comprehensive, open-source software pipeline for automated insect monitoring, combining object detection, classification, and tracking, adapted for moths.

Findings

Pipeline is already in use across three continents.

Enables scalable and automated insect data collection.

Facilitates fine-grained species identification.

Abstract

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.