Machine learning and computer vision for plant health

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Greenhouse plants must be continuously monitored for detecting pests and diseases. It is also important toexamine the crop's fruit production by estimating their numbers and the right time for harvesting. Theseprocesses are currently performed manually via tedious and time consuming visual inspection procedures,which can take weeks for an average size greenhouse. The main focus of this project is on developingcomputational tools for the automated, fast, and accurate interpretation of image data gathered from the plants.In particular, in collaboration with Ecoation, we will develop computer vision and machine learning softwaretools to analyze plant image data and identify important horticultural features and plant health status. Thisproject is essential for the company's development of machine learning and computer vision products tosignificantly reduce the need for human scouting for plant problems due to its precise and timely alertingcapabilities. With the development this project can bring, Ecoation anticipates incremental reductions in croploss due to the use of the Ecoation System in greenhouses. As the result of our early stage detection of cropstress, just in BC, it can save growers $20 million each year.