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Development of an automated image registration and depth estimation for fruit detection using deep learning and spectral imaging
There is an increasing demand for food production to meet the ever-growing world population. As such, effective management of farm resources is quite critical for the optimal operation of farm enterprises to meet the demand. This project intends to develop an automated approach to detect and analyze fruits using a camera system. One internship programmer working with Vivid Machines Inc. will develop a novel machine learning-based approach to automate the fruit detection and analysis. The proposed method will be helpful in assisting fruit counting, assessment, and automated harvesting. The project will allow Vivid Machines Inc. to stay ahead of the competition by providing a novel best-in-class system for managing fruits and vegetables for farm enterprises.
Faculty Supervisor:
Kumaradevan Punithakumar;Nilanjan Ray
Student:
Partner:
Vivid Machines
Discipline:
Computer science
Sector:
Professional, scientific and technical services
University:
University of Alberta
Program:
Accelerate
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.