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Using an Unmanned Aerial Vehicle with Multispectral with RGB Sensors to Analyze Canola Yield in the Canadian Prairies
1K. Hodge, 2F. Akhter, 3L. Bainard, 4A. Smith
1. Agriculture and Agri-Food Canada, Science and Technology Branch, Regina, SK Canada
2. Agriculture and Agri-Food Canada, Science and Technology Branch, Indian Head, SK Canada
3. Agriculture and Agri-Food Canada, Science and Technology Branch, Swift Current, SK Canada
4. Agriculture and Agri-Food Canada, Science and Technology Branch, Lethbridge, AB Canada

In 2017 canola was planted on 9 million hectares in Canada surpassing wheat as the most widely planted crop in Canada.  Saskatchewan is the dominant producer with nearly 5 million hectares planted in 2017.  This crop, seen both as one of the highest-yielding and most profitable, is also one of most expensive and input-intensive for producers on the Canadian Prairies.   In this study, the effect of natural and planted shelterbelts on canola yield was compared with canola yield from fields with no tree or other natural vegetation. Yield was measured in 15 canola fields, 5 of which had naturally occurring trees, 5 with a row of planted trees and 5 without any trees, along three, 350 m transects, near Indian Head, Saskatchewan.  During the growing season RGB and radiometrically corrected multispectral data from an unmanned aerial vehicle were collected at least once at each site.  A variety of vegetation indices, such as normalized difference vegetation index, enhanced vegetation index, soil adjusted vegetation index and other variables were correlated with ground based measurements to determine the impact tree rows have on yield.  Preliminary results from one replicate site which includes the natural shelterbelt, planted shelterbelt and control site are described here.  A weak and inconstant correlation was found between yield and distance from natural vegetation.  

Keyword: Canola, canola yield, drone, unmanned aerial vehicles, NDVI, NDYI, Saskatchewan agriculture