101811 Application of Small Unmanned Aerial Vehicles in Canola Yield Prediction Using Yellowness Index.

Poster Number 454-811

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: Agricultural Remote Sensing Poster

Wednesday, November 9, 2016
Phoenix Convention Center North, Exhibit Hall CDE

Ti Zhang1, Hema Sudhakar Duddu2, Menglu Wang2 and Steve Shirtliffe2, (1)University of Saskatchewan, Saskatoon, SK, CANADA
(2)University of Saskatchewan, Saskatoon, SK, Canada
Abstract:
Recent developments in technologies for imaging plants using small Unmanned Aerial Vehicles (s-UAV) make it possible to effectively collect various crop phenotyping data and to predict crop yield via radiometric data. Canola flowers indeterminately over a protracted period with bright yellow flowers. Yellowness of canola petals may be a critical part of a canopy-level signal and a good predictor of seed number and therefore seed yield. Our objectives were 1) to evaluate which wavebands of light may be useful/sensitive for estimation of yellowness, and 2) to determine whether the yellowness index of canola canopy during flowering could be a sensitive predictor of seed number and seed yield. There were 56 canola genotypes grown at Saskatoon, SK, Canada in 2016. Hand-held spectroradiometer was used to collect reflectance values from the ground during flowering. Meanwhile, aerial imagery with geometric and radiometric corrections was collected using an s-UAV mounted with a multispectral and RGB digital cameras. We expect that the ratio of blue band over green band will be a useful index for representing canola yellowness, which is related to canola flowing intensity. The yellowness index integrated over time should be a good predictor of seed number and thus canola yield.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: Agricultural Remote Sensing Poster