Unverified paper record
Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy
Heliyon · 1 Mar 2021 · 10.1016/j.heliyon.2021.e06566
Abstract
Estimating nitrogen (N) concentration in situ is fundamental for managing the fertilization of the sugarcane crop. The purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations in two stages (plant cane and first ratoon) under variable rates of N application. A randomized complete-block experimental design was applied, with four levels of N fertilization: 0, 80, 160, and 240 kg N ha -1 . Six sampling events were carried out during the rapid growth stage, where the canopy reflectance spectra with a hyperspectral sensor were measured, and tissue samples for N determination in plant cane and first ratoon were taken, from 60 days after emergence (DAE) and 60 days after harvest (DAH), respectively, until days 210 DAE and 210 DAH. To build the models, partial least squares regression analysis was used and was trained by three transformations of the spectral data: (i) average reflectance spectrum (R), (ii) multiple scatter correction and Savitzky-Golay filter MSC-SG) reflectance spectrum, and (iii) calculated vegetation indices (VIs).
Plant phenotyping relevance
サトウキビのキャノピー窒素濃度という植物形質を、ハイパースペクトル計測と回帰モデルで推定する手法開発が研究の中心である。
abstractThe purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations
abstractthe canopy reflectance spectra with a hyperspectral sensor were measured
abstractTo build the models, partial least squares regression analysis was used
Code and data availability
The paper's spectral reflectance measurements, N concentration data, and PLS analysis are explicitly declared confidential ('The data that has been used is confidential.'), and no author code, models, or datasets are publicly deposited. The only URLs present are the license and cited references (e.g., the generic 'pros
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