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

Xiaoyu Song,Haikuan Feng,Chenghai Yang,Xingang Xu,Guijun Yang,Dongyan Zhang

doi:10.3390/s22020549

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.

Highlights

Nitrogen (N) is a crucial nutrient required for crop growth and grain formation
To determine which spectral features could sufficiently estimate crop N status indicators, we considered one leaf N status indicator (i.e., the leaf N concentration (LNC)), one plant N status indicator (i.e., the N plant concentration (PNC)), and one relative plant N indicator (i.e., the nutrition index (NNI)), which is calculated as the ratio of the measured N concentration and the critical N concentration
The observed wheat N parameters were well described by the Gaussian process regression (GPR) and traditional parametric regression (PR) methods throughout the two growth stages

Summary

Introduction

Nitrogen (N) is a crucial nutrient required for crop growth and grain formation. Agricultural managers can regulate N management at suitable rates and opportune moments based on the crop’s N requirements. Much meaningful progress in sensor technology for evaluation of plant N status has been achieved in recent years. Leaf sensors, such as chlorophyll meters [7,8,9] and Dualex sensors [10], have been widely used to measure crop N status. These leaf clip sensors show a stable relation with plant N due to their direct contact with the plant. Rapid, precise, and non-destructive acquisition of N information has become an essential technique for crop nutrition and growth diagnosis [5,25], which helps dynamic regulations of N fertilizer use [26]

Objectives

Methods

Results

Discussion

Conclusion