Smart Greenhouse: Applied computer vision and machine learning in greenhouse cultivation

Many of the current greenhouse cultivation processes can be labor intensive, unable to accurately capture all information on a plant, and hard to manage as grower’s operation scale. By proposing a new method of collecting and analyzing data in these greenhouse using computer vision and machine learning, interns will try to improve the efficiencies of these processes. This proposed system aims to collect valuable information such as plant dimensions and fruit sizes that was previously very inefficient for human labour to do, and to predicts most optimal growing environment with this data. Not only will the interns gain experience in applying their technical expertise in a real-world problem, this project also helps partner organization to discover and develop new products for greenhouse growers.

Faculty Supervisor:

Martin Barczyk;Qing Zhao


Bowen Xie;Mingjie Han;Linjian Xiang




Engineering - computer / electrical


Professional, scientific and technical services


University of Alberta



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