Position/Title: M.Sc. by Thesis
Office: ANNU 043
- BSc - Animal Biology (University of Guelph, 2016-2020)
- MSc – Animal Biosciences (University of Guelph, 2021-current)
- Live body weight estimation methods for pigs (May 2020 – August 2020) Advisor: Dr. Dan Tulpan Review previous approaches about measuring and estimating live body weight of pigs.
- Towards automatic estimation of live body weight of pigs from digital images (September 2020 – December 2020) Advisor: Dr. Dan Tulpan, Dr. Renee Bergeron Explore the possibility of developing a semi-automated image-based system to estimate the live body weight of pigs using a reference object.
Advisor: Dr. Dan Tulpan
1. Wang, Z.*, Shadpour, S., Chan, E., Rotondo, V., Wood, K. M., & Tulpan, D. (2021). ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images. Journal of Animal Science, 99(2), skab022.
I am currently a first-year MSc Thesis student in Animal Biosciences under the supervision of Dr. Dan Tulpan. I completed my BSc.H at the University of Guelph. My interest in animal science started when I took farm tours and animal labs in undergraduate study. Then when I stepped into the statistics and computer programming field, I realized that there were possibilities to investigate in a crossing field, which was projects conducted by Dr. Dan Tulpan’s lab.
The live body weight (LBW) is an important parameter providing guidance for estimation of growth and feed conversion efficiency, body condition, presence of disease, and management of housing, nutrition, and animal health in different life stages of livestock. My master research is focused on exploring the feasibility of a semi-automatic analytic system that estimates the LBW of pigs by applying machine learning methods that use approximated morphometric measurements extracted from digital images acquired with a consumer-level camera in the presence of a reference object. In contrast to traditional manual measuring and weighing methods, this novel system is cost-efficient and time-efficient, and it can significantly contribute to the development of automatic intelligent solutions for scientific research and commercial animal production.