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Grant: Applied Research and Development Grants - Level 1

Nova Scotia Community College

$25,000.00

An Evaluation of Various Vegetation Indexes for Predicting Wild Blueberry Yield

EF 2019-20

Résumé
Wagner Forest NS Ltd. is a forest management company with a core commitment to the practice of sustainable forestry. Wagner manages approximately 500,000 acres of forest land in Nova Scotia and 845,000 acres in Ontario. For the past seven years, Wagner has been investing both time and money into developing cutover forest land into blueberry fields. The company will have approximately 1000 acres developed by the end of 2019. Wagner would like to collaborate with NSCC's Centre of Geographic Sciences (COGS) Applied Research team to investigate the feasibility of utilizing remote sensing and spatial analytics to monitor plant health and predict wild blueberry yields.Several UAV surveys will be conducted during the growing season using a multispectral sensor. From this imagery, various vegetation indices will be calculated to assist with measuring plant health. Following the harvest the various vegetation indices will be correleated with the blueberry harvest to determine the best index for predicting harvest yield.The Applied Research team at COGS will lead the project, assist in survey design and methodology, conduct aerial image collection, and perform post processing of images. The goal of the project is to determine the best vegetation index and the optimal time to collect imagery to best predict wild blueberry yield.
Programme
Applied Research and Development Grants - Level 1
Emplacement
Nova Scotia, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
538303-2018
Name nom
Joseph, Alain
Department département
Waterfront Campus
Organization id
16240
Institution établissement
Nova Scotia Community College
Province en
Nova Scotia
Province fr
Nouvelle-Écosse
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2019
Competition year année de concours
2018
Award amount
25000
Program id
CARD1
Program name en
Applied Research and Development Grants - Level 1
Program name fr
Subventions de recherche et développement appliquée - niveau 1
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
1561
Committee name en
Applied Research and Development Grants
Committee name fr
Subventions de recherche et développement appliquée
Area of application code
1209
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Agriculture
Area of application fr
Agriculture
Research subject code
4102
Research subject group en
Geographical information
Research subject group fr
Information géographique
Research subject en
Remote sensing
Research subject fr
Télédétection
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
An Evaluation of Various Vegetation Indexes for Predicting Wild Blueberry Yield
Keyword
NSERC
Application summary
Wagner Forest NS Ltd. is a forest management company with a core commitment to the practice of sustainable forestry. Wagner manages approximately 500,000 acres of forest land in Nova Scotia and 845,000 acres in Ontario. For the past seven years, Wagner has been investing both time and money into developing cutover forest land into blueberry fields. The company will have approximately 1000 acres developed by the end of 2019. Wagner would like to collaborate with NSCC's Centre of Geographic Sciences (COGS) Applied Research team to investigate the feasibility of utilizing remote sensing and spatial analytics to monitor plant health and predict wild blueberry yields.Several UAV surveys will be conducted during the growing season using a multispectral sensor. From this imagery, various vegetation indices will be calculated to assist with measuring plant health. Following the harvest the various vegetation indices will be correleated with the blueberry harvest to determine the best index for predicting harvest yield.The Applied Research team at COGS will lead the project, assist in survey design and methodology, conduct aerial image collection, and perform post processing of images. The goal of the project is to determine the best vegetation index and the optimal time to collect imagery to best predict wild blueberry yield.