利用Dualex植物葉綠素儀對冬小麥氮素進行估算

Dualex便攜式氮平衡-葉綠素-花青素-黃酮醇測量儀

摘要

更好地了解小麥氮素狀況對于改善精準農業(yè)中的氮肥管理非常重要。在這項研究中，評估了四種不同傳感器估算冬小麥氮素的能力。采用高斯過程回歸（GPR）方法和順序向后特征去除（SBBR）程序，確定了不同傳感器對小麥氮素指標敏感的植被指數（VIs）的最佳組合。通過參數回歸（PR）、多變量線性回歸（MLR）和高斯過程回歸（GPR），利用VIs估計小麥葉片氮濃度（LNC）、植株氮濃度（PNC）和營養(yǎng)指數（NNI）。研究結果表明，在低冠層覆蓋條件下，光學熒光傳感器可以更準確地估計冬小麥氮素狀況。雙氮平衡指數（NBI）是小麥生長早期LNC、PNC和NNI的最佳葉片水平指標。在生長早期，復合指數是LNC、PNC和NNI的最佳冠層水平指標。在生長后期，ASD-VIs為小麥氮素指標提供了準確的估計。這項研究還表明，與MLR和PR方法相比，GPR結合SBBR分析方法可以更準確地估計冬小麥LNC、PNC和NNI，并為這些傳感器在不同冬小麥生長階段提供最佳VI組合。

關鍵詞：葉片氮濃度；植物含氮量；氮素營養(yǎng)指數；高斯過程回歸

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), m*riable 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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