UAV-based Digital Field Phenotyping for Crop Nitrogen Estimation using RGB Imagery

Abstract

Nitrogen (N) is one of the essential nutrients required for healthy crop growth. Field phenotyping for nitrogen stress symptoms is laborious and time-consuming, that way, it is a major bottleneck in nutrition-inclusive agricultural research. Recent advancements in sensors and image processing facilitate color-based quantification of crop greenness from high-resolution RGB images. In this paper, we present unmanned aerial vehicle (UAV)-based digital field phenotyping for the estimation of crop nitrogen content. For this, we conducted a field experiment during the post-rainy season of 2021 at International Crops Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India with long-stature cereal model crop, sorghum (Sorghum bicolor L.) cultivated under three different regimes varying in moisture and soil nitrogen content. A high-resolution RGB sensor (XenmuseX5S) mounted on DJI Matric 210 quadcopter was used for capturing the spatiotemporal imagery. Five different RGB spectrum vegetation indices indicating crop greenness were correlated with ground truth values of crop N content using simple linear regression and stepwise backward regression. With a prediction potential of R2=0.65 and MAE=0.27 for an independent dataset, we present a stepwise backward linear regression model as a promising approach for real-time estimation of the N status of sorghum crop.

Plant phenotyping relevance

UAV搭載RGBセンサーと画像由来植生指数を用いてソルガムの窒素状態を推定する手法を開発・評価しており、植物フェノタイピング手法が研究の中心である。

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