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Getting the Bugs Out: Entomology Using Computer Vision
  • +1
  • Stefan Schneider,
  • Graham Taylor,
  • Stefan Kremer,
  • John Fryxell
Stefan Schneider
University of Guelph

Corresponding Author:[email protected]

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Graham Taylor
University of Guelph
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Stefan Kremer
University of Guelph
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John Fryxell
University of Guelph
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Abstract

Deep learning for computer vision has shown promising results in the field of entomology. Deep learning performance is maximized primarily by bulk labeled data which, outside of rare circumstances, are limited in ecological studies. Currently, to utilize deep learning systems, ecologists undergo extensive data collection efforts, or limit their problem to niche tasks. These solutions do not scale to region agnostic models. There are solutions using data augmentation, simulators, generative models, and self-supervised learning that supplement limited data labels. Here, we highlight the success of deep learning for computer vision within entomology, discuss data collection efforts, provide methodologies for annotation efficient learning, and conclude with practical guidelines for how ecologists can empower accessible automated ecological monitoring on a global scale.
10 Oct 2022Submitted to Ecology Letters
13 Oct 2022Submission Checks Completed
13 Oct 2022Assigned to Editor
13 Oct 2022Review(s) Completed, Editorial Evaluation Pending
27 Oct 2022Reviewer(s) Assigned
29 Nov 2022Editorial Decision: Revise Major
05 Jan 20231st Revision Received
05 Jan 2023Review(s) Completed, Editorial Evaluation Pending
06 Jan 2023Submission Checks Completed
06 Jan 2023Assigned to Editor
12 Jan 2023Reviewer(s) Assigned
03 Feb 2023Editorial Decision: Revise Minor
14 Feb 2023Review(s) Completed, Editorial Evaluation Pending
14 Feb 20232nd Revision Received
15 Feb 2023Submission Checks Completed
15 Feb 2023Assigned to Editor
16 Feb 2023Reviewer(s) Assigned
18 Mar 2023Editorial Decision: Revise Minor
30 Mar 2023Review(s) Completed, Editorial Evaluation Pending
30 Mar 20233rd Revision Received
31 Mar 2023Submission Checks Completed
31 Mar 2023Assigned to Editor
31 Mar 2023Reviewer(s) Assigned
04 Apr 2023Editorial Decision: Accept