Prediction of leaf nitrogen in sugarcane ( Saccharum spp.) by Vis-NIR-SWIR spectroradiometry.

Abstract

Nitrogen is one of the essential nutrients for the production of agricultural crops, participating in a complex interaction among soil, plant and the atmosphere. Therefore, its monitoring is important both economically and environmentally. The aim of this work was to estimate the leaf nitrogen contents in sugarcane from hyperspectral reflectance data during different vegetative stages of the plant. The assessments were performed from an experiment designed in completely randomized blocks, with increasing nitrogen doses (0, 60, 120 and 180 kg ha -1 ). The acquisition of the spectral data occurred at different stages of crop development (67, 99, 144, 164, 200, 228, 255 and 313 days after cutting; DAC). In the laboratory, the hyperspectral responses of the leaves and the Leaf Nitrogen Contents (LNC) were obtained. The hyperspectral data and the LNC values were used to generate spectral models employing the technique of Partial Least Squares Regression (PLSR) Analysis, also with the calculation of the spectral bands of greatest relevance, by the Variable Importance in Projection (VIP). In general, the increase in LNC promoted a smaller reflectance in all wavelengths in the visible (400-680 nm). Acceptable models were obtained (R 2 > 0.70 and RMSE -1 ), the most robust of which were those generated from spectra in the visible (400-680 nm) and red-edge (680-750 nm), with values of R 2 > 0.81 and RMSE -1 . An independent validation, leave-one-date-out cross validation (LOOCV), was performed using data from other collections, which confirmed the robustness and the possibility of LNC prediction in new data sets, derived, for instance, from samplings subsequent to the period of study.

Plant phenotyping relevance

ハイパースペクトル反射データからサトウキビ葉の窒素含量を推定する分光計測・PLSRモデルを開発し、独立交差検証で性能を評価しており、表現型取得手法が研究の中心である。

Code and data availability

The supplied blocks describe sugarcane leaf hyperspectral data and PLSR analysis but contain no public dataset deposit, author code release, or availability statement. The only URLs present are CC BY-NC license links, which are not paper-specific assets. The analysis software mentioned (ParLeS 3.1) is a cited third-pty

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