Towards machine vision for insect welfare monitoring and behavioural insights

Hansen, Mark F.; Oparaeke, Alphonsus; Gallagher, Ryan; Karimi, Amir; Tariq, Fahim; Smith, Melvyn L.

doi:10.3389/fvets.2022.835529

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

Mark Hansen Mark.Hansen@uwe.ac.uk
Professor of Machine Vision and Machine Learning

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