Fiorio Peterson Ricardo, Silva Carlos Augusto Alves Cardoso, Rizzo Rodnei, Demattê José Alexandre Melo, Luciano Ana Cláudia Dos Santos, Silva Marcelo Andrade da
Department of Biosystems Engineering, "Luiz de Queiroz" College of Agriculture, University of São Paulo, 13418900, Piracicaba, São Paulo, Brazil.
Environmental Analysis and Geoprocessing Laboratory, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil.
Heliyon. 2024 Feb 21;10(5):e26819. doi: 10.1016/j.heliyon.2024.e26819. eCollection 2024 Mar 15.
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). 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 > 0.70 and RMSE <1.41 g kg), 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 > 0.81 and RMSE <1.24 g kg. 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.
氮是农作物生产所需的必需养分之一，参与土壤、植物和大气之间的复杂相互作用。因此，对其进行监测在经济和环境方面都很重要。这项工作的目的是根据植物不同营养阶段的高光谱反射数据估算甘蔗叶片中的氮含量。评估是通过完全随机区组设计的实验进行的，氮剂量递增（0、60、120和180千克/公顷）。光谱数据在作物发育的不同阶段（砍收后67、99、144、164、200、228、255和313天；DAC）采集。在实验室中，获取了叶片的高光谱响应和叶片氮含量（LNC）。高光谱数据和LNC值用于采用偏最小二乘回归（PLSR）分析技术生成光谱模型，同时通过投影变量重要性（VIP）计算最相关的光谱带。总体而言，LNC的增加导致可见光（400 - 680纳米）所有波长的反射率降低。获得了可接受的模型（R > 0.70且RMSE < 1.41克/千克），其中最稳健的模型是由可见光（400 - 680纳米）和红边（680 - 750纳米）光谱生成的，R值> 0.81且RMSE < 1.24克/千克。使用来自其他采集的数据进行了独立验证，即留一日期交叉验证（LOOCV），这证实了在新数据集（例如，来自研究期之后的采样）中LNC预测的稳健性和可能性。
