In-Field Wheat Reflectance: How to Reach the Organ Scale?

Abstract

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.

Plant phenotyping relevance

コムギの葉・穂を分離して器官スケールの反射率を高スループット測定する画像・分光法を開発し、検証・生育期間適用しており、植物表現型取得が研究の中心である。

Code and data availability

The supplied blocks describe the paper's wheat BRF acquisition and organ segmentation methods but contain no authors' public dataset, image, code, or model deposit. The only GitHub mention is MicaSense's third-party imageprocessing library, which is a generic external tool, not a paper-specific asset. The YOLOv5/DeepMC

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