Reference number : GC-2017-Q3-00479, amendment number : , agreement type : G, recipient legal name : Hamarneh, Ghassan (Simon Fraser University), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Greenhouse plants must be continuously monitored for detecting pests and diseases. It is also important to_x000D_
examine the crop's fruit production by estimating their numbers and the right time for harvesting. These_x000D_
processes are currently performed manually via tedious and time consuming visual inspection procedures,_x000D_
which can take weeks for an average size greenhouse. The main focus of this project is on developing_x000D_
computational tools for the automated, fast, and accurate interpretation of image data gathered from the plants._x000D_
In particular, in collaboration with Ecoation, we will develop computer vision and machine learning software_x000D_
tools to analyze plant image data and identify important horticultural features and plant health status. This_x000D_
project is essential for the company's development of machine learning and computer vision products to_x000D_
significantly reduce the need for human scouting for plant problems due to its precise and timely alerting_x000D_
capabilities. With the development this project can bring, Ecoation anticipates incremental reductions in crop_x000D_
loss due to the use of the Ecoation System in greenhouses. As the result of our early stage detection of crop_x000D_
stress, just in BC, it can save growers $20 million each year., program purpose french : Greenhouse plants must be continuously monitored for detecting pests and diseases. It is also important to_x000D_
examine the crop's fruit production by estimating their numbers and the right time for harvesting. These_x000D_
processes are currently performed manually via tedious and time consuming visual inspection procedures,_x000D_
which can take weeks for an average size greenhouse. The main focus of this project is on developing_x000D_
computational tools for the automated, fast, and accurate interpretation of image data gathered from the plants._x000D_
In particular, in collaboration with Ecoation, we will develop computer vision and machine learning software_x000D_
tools to analyze plant image data and identify important horticultural features and plant health status. This_x000D_
project is essential for the company's development of machine learning and computer vision products to_x000D_
significantly reduce the need for human scouting for plant problems due to its precise and timely alerting_x000D_
capabilities. With the development this project can bring, Ecoation anticipates incremental reductions in crop_x000D_
loss due to the use of the Ecoation System in greenhouses. As the result of our early stage detection of crop_x000D_
stress, just in BC, it can save growers $20 million each year., agreement title english : Machine learning and computer vision for plant health, agreement title french : Machine learning and computer vision for plant health, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-12-13, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada