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. 2018 Oct 10:9:1478.
doi: 10.3389/fpls.2018.01478. eCollection 2018.

Passive Reflectance Sensing and Digital Image Analysis Allows for Assessing the Biomass and Nitrogen Status of Wheat in Early and Late Tillering Stages

Salah Elsayed  1 , Gero Barmeier  1 , Urs Schmidhalter  1
Affiliations

Passive Reflectance Sensing and Digital Image Analysis Allows for Assessing the Biomass and Nitrogen Status of Wheat in Early and Late Tillering Stages

Salah Elsayed et al. Front Plant Sci. .

Erratum in

Abstract

Proximal remote sensing systems depending on spectral reflectance measurements and image analysis can acquire timely information to make real-time management decisions compared to laborious destructive measurements. There is a need to make nitrogen management decisions at early development stages of cereals when the first top-dressing is made. However, there is insufficient information available about the possibility of detecting differences in the biomass or the nitrogen status of cereals at early development stages and even less comparing its relationship to destructively obtained information. The performance of hyperspectral passive reflectance sensing and digital image analysis was tested in a 2-year study to assess the nitrogen uptake and nitrogen concentration, as well as the biomass fresh and dry weight at early and late tillering stages of wheat from BBCH 19 to 30. Wheat plants were subjected to different levels of nitrogen fertilizer applications and differences in biomass, and the nitrogen status was further created by varying the seeding rate. To analyze the spectral and digital imaging data simple linear regression and partial least squares regression (PLSR) models were used. The green pixel digital analysis, spectral reflectance indices and PLSR of spectral reflectance from 400 to 1000 nm were strongly related to the nitrogen uptake and the biomass fresh and dry weights at individual measurements and for the combined dataset at the early crop development stages. Relationships between green pixels, spectral reflectance indices and PLSR with the biomass and nitrogen status parameters reached coefficients of determination up to 0.95∗∗ through the individual measurements and the combined data set. Reflectance-based spectral sensing compared to digital image analysis allows detecting differences in the biomass and nitrogen status already at early growth stages in the tillering phase. Spectral reflectance indices are probably more robust and can more easily be applied compared to PLSR models. This might pave the way for more informed management decisions and potentially lead to improved nitrogen fertilizer management at early development stages.

Keywords: digital agriculture; high-throughput sensing; imaging; nitrogen management; phenomics; precision farming; precision phenotyping; spectrometry.

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Figures

FIGURE 1
FIGURE 1
The relationship between green pixels percentage obtained from image analysis and the destructively obtained parameters (A) biomass fresh weight, (B) biomass dry weight, (C) nitrogen concentration, and (D) nitrogen uptake of wheat at four growth stages in 2014. Statistical information is given in Table 5.
FIGURE 2
FIGURE 2
The relationship between green pixels percentage obtained from image analysis and the destructively obtained parameters (A) biomass dry weight, (B) nitrogen concentration, and (C) N uptake of wheat at four growth stages in 2016. Statistical information is given in Table 6.
FIGURE 3
FIGURE 3
The relationship between the spectral index (R710 – R640)/(R710 + R640) and the (A) biomass fresh weight, (B) biomass dry weight, (C) nitrogen concentration, and (D) nitrogen uptake of the wheat cultivar Kerubino at four growth stages grown in experimental plots in 2014. Statistical information is given in Table 7.
FIGURE 4
FIGURE 4
The relationship between the spectral index (R710 – R640)/(R710 + R640) and the (A) biomass dry weight, (B) nitrogen concentration, and (C) nitrogen uptake of the field grown wheat cultivar Kerubino at two growth stages in 2016. Statistical information is given in Table 8.
FIGURE 5
FIGURE 5
Relationships between the observed and predicted: (A) biomass fresh weight, (B) biomass dry weight, (C) nitrogen concentration, and (D) nitrogen uptake at four sampling times for calibration and validation datasets in 2014 using a partial least squares model. For the PLS model a cross-validation was performed. Linear calibration models of all datasets for each parameter were used to validate the model at four measurements dates. Statistical information is given in Table 9.
FIGURE 6
FIGURE 6
Relationships between the observed and predicted: (A) biomass dry weight, (B) nitrogen concentration, and (C) nitrogen uptake at two sampling times at BBCH 19 and 22 for calibration and validation datasets in 2016 using a partial least squares model. For the PLS model a cross-validation was performed. Linear calibration models of all datasets for each parameter were used to validate the model at four measurements dates. Statistical information is given in Table 10.
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