The reflectance of wheat crops provides information on their architecture or physiology. However, the methods currently used for close-range reflectance computation do not allow for the separation of the wheat canopy organs: the leaves and the ears. This study details a method to achieve high-throughput measurements of wheat reflectance at the organ scale. A nadir multispectral camera array and an incident light spectrometer were used to compute bi-directional reflectance factor (BRF) maps. Image thresholding and deep learning ear detection allowed for the segmentation of the ears and the leaves in the maps. The results showed that the BRF measured on reference targets was constant throughout the day but varied with the acquisition date. The wheat organ BRF was constant throughout the day in very cloudy conditions and with high sun altitudes but showed gradual variations in the morning under sunny or partially cloudy sky. As a consequence, measurements should be performed close to solar noon and the reference panel should be captured at the beginning and end of each field trip to correct the BRF. The method, with such precautions, was tested all throughout the wheat growing season on two varieties and various canopy architectures generated by a fertilization gradient. The method yielded consistent reflectance dynamics in all scenarios.
Keywords: camera; ears; image; leaves; multispectral; phenotyping; spectrometer.