Estimation of Nitrogen Concentration in Winter Wheat Leaves Based on a Novel Spectral Index and Machine Learning Model.

Abstract

Assessing crop nitrogen status is crucial for optimizing fertilization strategies and promoting sustainable production. Although hyperspectral data offer significant advantages for monitoring subtle physiological changes in crops, accurately determining nitrogen status based on spectral information remains challenging. In this study, field experiments were conducted during the jointing stage of winter wheat on the Loess Plateau from 2018 to 2020. Concurrent measurements of leaf nitrogen concentration (LNC) and hyperspectral reflectance were collected to derive three types of spectral parameters: traditional vegetation indices, two-dimensional optimal spectral indices, and three-dimensional optimal spectral indices. Spectral parameters exhibiting a significant correlation with LNC ( p 2 = 0.827, RMSE = 2.803 mg g -1 , MRE = 7.664%), significantly outperforming other model-input combinations. This study confirms the feasibility and high accuracy of winter wheat LNC inversion using novel multidimensional spectral indices and provides a new approach for real-time, non-destructive monitoring of nitrogen status in winter wheat.

Plant phenotyping relevance

冬小麦葉の窒素濃度という植物形質を、ハイパースペクトル情報・新規多次元スペクトル指数・機械学習で非破壊推定する手法が研究の中心であり、精度評価も行っている。

Code and data availability

The paper reports winter wheat LNC and hyperspectral measurements and MATLAB-based machine learning analysis, but no public dataset, code, or model deposit is provided. The Data Availability Statement only offers to direct further inquiries to the corresponding authors, so any paper-specific assets would require author

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