Abstract
Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola (Brassica napus L.) flowers, the bright yellow flowers, indeterminately increase 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-consuming, 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 investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes, including 53 B. napus lines, two Brassica carinata lines, and a Brassica 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 the thresholding method improved the ability of NDYI to detect yellow flowers with coefficient of determination (R (2)) ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel numbers integrated over time covers more growth information and can be a good predictor of pod number and thus, canola yield with R (2) up to 0.42. These results indicate that NDYI-based flowering pixel numbers 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.