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利用Dualex植物叶绿素仪对冬小麦氮素进行估算

发布时间:2022-05-06 点击量:118

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Dualex便携式氮平衡-叶绿素-花青素-黄酮醇测量仪

摘要

更好地了解小麦氮素状况对于改善精准农业中的氮肥管理非常重要。在这项研究中,评估了四种不同传感器估算冬小麦氮素的能力。采用高斯过程回归(GPR)方法和顺序向后特征去除(SBBR)程序,确定了不同传感器对小麦氮素指标敏感的植被指数(VIs)的最佳组合。通过参数回归(PR)、多变量线性回归(MLR)和高斯过程回归(GPR),利用VIs估计小麦叶片氮浓度(LNC)、植株氮浓度(PNC)和营养指数(NNI)。研究结果表明,在低冠层覆盖条件下,光学荧光传感器可以更准确地估计冬小麦氮素状况。双氮平衡指数(NBI)是小麦生长早期LNC、PNC和NNI的最佳叶片水平指标。在生长早期,复合指数是LNC、PNC和NNI的最佳冠层水平指标。在生长后期,ASD-VIs为小麦氮素指标提供了准确的估计。这项研究还表明,与MLR和PR方法相比,GPR结合SBBR分析方法可以更准确地估计冬小麦LNC、PNC和NNI,并为这些传感器在不同冬小麦生长阶段提供最佳VI组合。

关键词:叶片氮浓度;植物含氮量;氮素营养指数;高斯过程回归

Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors

Abstract

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.  

Keywords: leaf nitrogen concentration; plant nitrogen content; nitrogen nutrition index; Gaussian process regression

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