Bio-surveillance and metabolic efficiency assessment and prediction using Artificial Intelligence via automated multispectral imagery in cattle production

This research project is intended to implement and evaluate a machine learning framework utilizing multispectral imagery to optimize protein production efficiency from two Canadian livestock industries, dairy production and beef cattle production. The assessment protocol will include the surveillance of pathophysiological conditions (e.g. bovine respiratory disease, mastitis, estrus detection), animal well-being (e.g. stress susceptibility, lameness, and poor body condition scores) and metabolic efficiency (e.g. residual intake and growth) affecting carbon footprints for these sectors. The complexities of these systems obligate the use of artificial intelligence to acquire, process, analyze and predict bio-surveillance parameters and metabolic efficiency.

Faculty Supervisor:

Clover Bench

Student:

Partner:

Animal Inframetrics;Alpha Phenomics Inc.

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program: