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: