Machine Vision Yield Monitor for Vegetable Crops

Machine Vision (MV) is a technology that aims to translate visual data from images or videos into useful information for industrial processes. The use of MV in precision agriculture is currently expanding and has helped to accelerate and improve sorting, grading and yield estimation. The goal of this research project is to develop a MV yield monitor for the large scale industrial farm Delfland Inc. that will identify, sort and count vegetables according to their size. The system will be designed to function with multiple types of vegetable harvesters including onions, carrots, lettuce and shallots. The system will also output this data as a geographical map showing the yield distribution of the field, providing farmers with important feedback concerning the state of their fields that can help to improve overall harvesting strategies.

Faculty Supervisor:

Viacheslav Adamchuk

Student:

Amanda Boatswain Jacques

Partner:

Delfland Inc

Discipline:

Engineering

Sector:

Information and communications technologies

University:

McGill University