Esther ChanPosition/Title: M.Sc. by thesis email: echan06@uoguelph.ca Phone: Office: |
Education
Bachelor of Science - Animal Biology, University of Guelph, 2020
Research Experience
Laboratory Assistant in Evolutionary Biology, University of Guelph (April 2018 – August 2020)
- Worked closely with researchers, from the lab of Dr. Roy Danzmann, in the preparation and organization of multiple trials
- Organized several thousand individual identifications and data – both physically and electronically using Excel
- Supervised and worked with high school co-op students in extracting data from samples
Current Education
As of current, I am an MSc-Thesis student with Dr. Dan Tulpan as my academic advisor. Growing up, animals had always fascinated me and so I did my undergraduate education in Animal Biology at the University of Guelph. During my undergraduate education, I enrolled in several computer science courses and developed an interest in incorporating it into the agriculture industry. I learned that body weight is an important factor monitored in animal husbandry and contributed to the assessment of health and welfare. However, methods to obtain it were labor-intensive and stressful for the animal which reduced productivity and welfare. As such, I decided to investigate less stressful and labor-intensive ways to obtain body weight using technology.
The objective of my Master’s is to evaluate the accuracy of machine learning models in predicting beef cattle body weight from images using morphometric data extracted from 2D lateral images. Their performances were then to be compared to the performances of the same models but operating with measurements extracted from the live animals. I hope to improve my understanding of predicting body weight using machine learning so it can be incorporated into and further advance the agriculture industry.
Featured Publications
Wang, Z., Shadpour, S., Chan, E., Rotondo, V., Wood, K., & Tulpan, D. (2021). ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images. Journal Of Animal Science, 99(2):1-15. doi: 10.1093/jas/skab022