AUTHOR=Zhang Ti , Vail Sally , Duddu Hema S. N. , Parkin Isobel A. P. , Guo Xulin , Johnson Eric N. , Shirtliffe Steven J. 

TITLE=Phenotyping Flowering in Canola (Brassica napus L.) and Estimating Seed Yield Using an Unmanned Aerial Vehicle-Based Imagery

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.686332

DOI=10.3389/fpls.2021.686332

ISSN=1664-462X

ABSTRACT=Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola (Brassica napus L.) flowers indeterminately and the bright yellow flowers accumulate over a protracted period. Flower production of canola plays an important role in yield determination. Yellowness of canola petals may be a critical reflectance signal and a good predictor of pod number and therefore seed yield. However, quantifying flowering based on traditional visual scales is subjective, time and labor consuming. Recent developments in phenotyping technologies using Unmanned Aerial Vehicles (UAVs) make it possible to effectively capture crop information and to predict crop yield via imagery. Our objectives were to 1) investigate the application of vegetation indices in estimating canola flower number, and 2) develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes including 53 B. napus lines, two B. carinata lines, and a B. juncea variety were grown near Saskatoon, SK, Canada from 2016 to 2018, and near Melfort and Scott, SK, Canada in 2017. Aerial imagery with geometric and radiometric corrections was collected through the flowering stage using a UAV mounted with a multispectral camera. We found that the normalized difference yellowness index (NDYI) was a useful vegetation index for representing canola yellowness, which is related to canola flowering intensity during the full flowering stage. However, the flowering pixel number estimated by thresholding method improved NDYI’s ability to detect yellow flowers with coefficient of determination (R2) values ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel number integrated over time covers more growth information and can be a good predictor of pod number and thus canola yield with a R2 value up to 0.42. These results indicate that NDYI-based flowering pixel number can perform well in estimating flowering intensity. Integrated flowering intensity extracted from imagery over time can be a potential phenotype associated with canola seed yield.