There is a need for an improved method of data collection on calf growth and body condition, as this data is useful for making informed breeding decisions. To better improve the data collection process, this project is developing and validating a touch-less automated visualization method for determining calf growth and condition that collects and analyzes growth data. A prototype is currently being developed which will take visual images and other spectral data, automating the process of collecting calf growth data, reducing the current labour-intensive methods that are currently being employed.
What is the challenge?
Successful calf growth in both dairy and beef cattle is useful data to have when making informed breeding decisions, improving animal welfare, and understanding the cost of production. This project will develop the technology that will determine calf growth and condition.
Addressing the problem:
The goal is to develop a touchless automated visualization method for determining calf growth and condition, that collects and analyzes growth data in the field. This project will create and test a prototype that takes visual images and other spectral data.
A new system for automated measurement of growth and body condition will enable greater production, understanding of calf growth and improved animal health will drive decision-making on farms.
Key message for decision makers:
Automating the process of collecting calf growth and condition can reduce the labour associated with collecting weights, have higher accuracy and precision on calf growth, flag animals that require attention and assist the producer with on-farm decisions.
Collaborators and students:
Dr. Kate Wood (Co-Investigator), Dr. Dan Tulpan (Co-Investigator), Dr. Megan Van Schaik (Co-Investigator), Dr. John Van de Vegte (Co-Investigator), and Dr. Marlene Paibomesai (Co-Investigator)