Towards Machine Vision for Insect Welfare Monitoring and Behavioural Insights

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.

Automated summary

This article presents the application of machine vision in the livestock industry, specifically in the field of insect farming. State of the art object detection and classification techniques were used to accurately count and measure the size of insects, as well as classify their gender. The low-cost Insecto IoT device was introduced for environmental condition monitoring and high resolution image capture.