L2M – Automated Weight Monitoring for Free-Range Beef Cattle

The beef cattle industry faces significant challenges in cattle weight monitoring, particularly in free-range systems where traditional methods are labor-intensive, inaccurate, or infrequent. This results in delayed growth, increased feeding costs, and a larger environmental footprint, requiring the development of innovative and sustainable technologies, as well as collaborative efforts. To address this, we propose an automated, non-invasive weight monitoring system that combines vision-based sensors and machine learning to track cattle growth in real time without disrupting farm operations. By providing data analysis and insights, this solution will enable farmers to make data-driven decisions and optimize herd management to sustainably and efficiently improve livestock quality of life and health, optimize resource use, and boost productivity. Through the L2M Launch program, the proposed solution will be refined from a market perspective by tackling specific commercialization challenges and developing a comprehensive business model that reflects the economic and operational realities of the beef cattle industry to ensure product-market fit, while also promoting entrepreneurial skills and enabling research discoveries to move out of the laboratory and be commercialized, thereby accelerating and growing the translation of research excellence to impact the Canadian economy.

Faculty Supervisor:

Tsz Ho Kwok

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Agriculture and Food; Artificial Intelligence; Technology

University:

Concordia University

Program: