A machine learning pipeline for automated insect monitoring
<|reference_start|>A machine learning pipeline for automated insect monitoring: 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.<|reference_end|>
@article{jain2024a,
    title={A machine learning pipeline for automated insect monitoring},
    author={Aditya Jain, Fagner Cunha, Michael Bunsen, L'eonard Pasi, Anna
  Viklund, Maxim Larriv'ee, David Rolnick},
    journal={NeurIPS 2023 Workshop on Tackling Climate Change with Machine
  Learning},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13031},
    primaryClass={cs.CV}
}