Grant: Engage Grants Program
University of British Columbia
$25,000.00
Automated vineyard yield estimation using computer vision
FY 2013-14
Summary
Quails' Gate Winery is one of Okanagan's leading producers of premium VQA wines and has a demonstrated history of participating in viticulture research to improve vineyard operations and grape quality. Accurate and reliable early yield estimation is crucial for vineyard managers to make the necessary adjustments to their labors before the harvest. With this information they can improve production and reduce cost. However, having reliable yield estimation is nontrivial as it is subject to several underlying parameters including temporal and climate variations, soil conditions, and pest infestation. Current industry practices rely mainly on manual random sampling from selected vineyards during the growing season and extrapolate the sample measurements to the entire vineyard. Quails' Gate has been interested in the use of technology to improve grape quality and production while reducing the resources required to farm and harvest each acre of land. The existing manual sampling practice cannot meet the industry demand, and the need for an automated and fast alternativeapproach is pressing. Quails' Gate will join our research group to develop a reliable computer vision-based yield estimation method that is insensitive to human error and can be carried out by unskilled personnel. The proposed method processes sequential images taken by moving a digital camera along the rows of vineyard facing the fruit-zone at an approximately constant distance. Postprocessing of the sequential images, we will automatically detect and count the individual berries using robust visual tracking algorithms developed in our research laboratory. This process will be used to predict the yield and also study the parameters causing temporal and spatial yield variations for six of Quails' vineyards. The wide use of the proposed technique will promote higher quality wine through precise crop control, and bolster the financial viability of existing and new wineries and vineyards. The proposed research is a stepping stone towards wider collaborations between theapplicant and Quails' Gate in near future in the area of machine vision applications in viticulture research in BC and Canada.
Program
Engage Grants Program
Recipient
Location
British Columbia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
452538-2013
Name nom
Najjaran, Homayoun
Department département
Okanagan - Engineering, School of
Organization id
2
Institution établissement
University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2013
Competition year année de concours
2013
Award amount
25000
Program id
EGP
Program name en
Engage Grants Program
Program name fr
Programme de subventions d'engagement partenariat
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1554
Committee name en
Pacific Internal Decision Committee
Committee name fr
Comité de décision interne pour le Pacifique
Area of application code
100
Area of application group en
Agriculture and primary food production
Area of application group fr
Agriculture et produits alimentaires primaires
Area of application en
Agriculture and primary food production
Area of application fr
Agriculture et produits alimentaires primaires
Research subject code
2603
Research subject group en
Robotics
Research subject group fr
Robotique
Research subject en
Computer vision
Research subject fr
Vision artificielle
Partie
1
Nb partie
1
Application title
Automated vineyard yield estimation using computer vision
Keyword
NSERC
Application summary
Quails' Gate Winery is one of Okanagan's leading producers of premium VQA wines and has a demonstrated history of participating in viticulture research to improve vineyard operations and grape quality. Accurate and reliable early yield estimation is crucial for vineyard managers to make the necessary adjustments to their labors before the harvest. With this information they can improve production and reduce cost. However, having reliable yield estimation is nontrivial as it is subject to several underlying parameters including temporal and climate variations, soil conditions, and pest infestation. Current industry practices rely mainly on manual random sampling from selected vineyards during the growing season and extrapolate the sample measurements to the entire vineyard. Quails' Gate has been interested in the use of technology to improve grape quality and production while reducing the resources required to farm and harvest each acre of land. The existing manual sampling practice cannot meet the industry demand, and the need for an automated and fast alternativeapproach is pressing. Quails' Gate will join our research group to develop a reliable computer vision-based yield estimation method that is insensitive to human error and can be carried out by unskilled personnel. The proposed method processes sequential images taken by moving a digital camera along the rows of vineyard facing the fruit-zone at an approximately constant distance. Postprocessing of the sequential images, we will automatically detect and count the individual berries using robust visual tracking algorithms developed in our research laboratory. This process will be used to predict the yield and also study the parameters causing temporal and spatial yield variations for six of Quails' vineyards. The wide use of the proposed technique will promote higher quality wine through precise crop control, and bolster the financial viability of existing and new wineries and vineyards. The proposed research is a stepping stone towards wider collaborations between theapplicant and Quails' Gate in near future in the area of machine vision applications in viticulture research in BC and Canada.