Mark Hansen Mark.Hansen@uwe.ac.uk
Professor of Machine Vision and Machine Learning
Towards machine vision for insect welfare monitoring and behavioural insights
Hansen, Mark F.; Oparaeke, Alphonsus; Gallagher, Ryan; Karimi, Amir; Tariq, Fahim; Smith, Melvyn L.
Authors
Alphonsus Oparaeke
Ryan Gallagher
Amir Karimi
Fahim Tariq
Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof
Abstract
Machine vision has demonstrated its usefulness in the livestock industry in terms of improving welfare in such areas as lameness detection and body condition scoring in dairy cattle. In this article, we present some promising results of applying state of the art object detection and classification techniques to insects, specifically Black Soldier Fly (BSF) and the domestic cricket, with the view of enabling automated processing for insect farming. We also present the low-cost “Insecto” Internet of Things (IoT) device, which provides environmental condition monitoring for temperature, humidity, CO2, air pressure, and volatile organic compound levels together with high resolution image capture. We show that we are able to accurately count and measure size of BSF larvae and also classify the sex of domestic crickets by detecting the presence of the ovipositor. These early results point to future work for enabling automation in the selection of desirable phenotypes for subsequent generations and for providing early alerts should environmental conditions deviate from desired values.
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 15, 2022 |
Online Publication Date | Feb 15, 2022 |
Publication Date | Feb 15, 2022 |
Deposit Date | Feb 15, 2022 |
Publicly Available Date | Feb 16, 2022 |
Journal | Frontiers in Veterinary Science |
Electronic ISSN | 2297-1769 |
Publisher | Frontiers Media |
Peer Reviewed | Peer Reviewed |
Volume | 9 |
Article Number | 835529 |
DOI | https://doi.org/10.3389/fvets.2022.835529 |
Keywords | General Veterinary |
Public URL | https://uwe-repository.worktribe.com/output/9021354 |
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Towards machine vision for insect welfare monitoring and behavioural insights
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