Remote evaluation of rice nitrogen utilization efficiency using chlorophyll-related spectral indices derived from unmanned aerial vehicle imagery.

Abstract

Introduction Nitrogen utilization efficiency (NUtE) directly reflects the efficiency of nitrogen remobilization to grains, serving as a key indicator of yield formation and environmental performance. However, conventional methods for assessing NUtE rely on destructive sampling and laboratory analysis, which are labor-intensive and time-consuming, whereas most existing remote sensing studies estimate NUtE by directly regressing spectral features against the final efficiency value without decomposing it into its underlying physiological components. Methods This study developed a remote-sensing-based indicator of rice NUtE based on chlorophyll-related vegetation indices at key rice growth stages, termed the Nitrogen Utilization Efficiency-Vegetation Index (NUtE-VI). NUtE showed a close and near-linear relationship with the ratio of panicle nitrogen accumulation from heading to dough stage (ΔPNA dough-heading , sink indicator) to leaf nitrogen accumulation at booting stage (LNA booting , source indicator). Therefore, with multi-site field experiments across different rice cultivars and nitrogen treatments, this study employed unmanned aerial vehicle imaging to accurately estimate rice leaf and panicle nitrogen accumulations, enabling rapid, large-scale evaluation of rice NUtE. Results This proposed index showed a strong correlation with measured NUtE (R 2 = 0.72, rRMSE = 10.84%) and effectively captured the distinct patterns of NUtE across different nitrogen treatments and cultivars. Discussion Our developed indicator is generalizable across diverse conditions for high-throughput selection of nitrogen-efficient cultivars and precision nitrogen management in sustainable agriculture.

Plant phenotyping relevance

UAV画像とスペクトル指標からイネの窒素利用効率を推定する手法を開発し、多地点・品種・施肥条件で精度評価しており、植物形質の取得・推定法が中心である。

Code and data availability

The supplied blocks contain no data availability statement, public repository link, or author code/model deposit. The paper describes UAV multispectral imagery, field phenotype measurements, and the NUtE-VI methodology, but no public dataset, image collection, or analysis code is made available in the provided text.

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