Hyperspectral Imaging for Estimating Nitrogen Use Efficiency in Maize Hybrids

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Hyperspectral Imaging for Estimating Nitrogen Use Efficiency in Maize Hybrids

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posted on 27.04.2021, 15:15 by Monica Britt Olson
Increasing the capability of maize hybrids to use nitrogen (N) more efficiently is a common goal that contributes to reducing farmer costs and limiting negative environmental impacts. However, development of such hybrids is costly and arduous due to the repeated need for laborious field and laboratory measurements of whole-plant biomass and N uptake in large early-stage breeding programs. This research evaluated alternative in-season methodologies, including field-based physiological measurements and hyperspectral remote imagery, as surrogate or predictive measures of important end-of-season N efficiency parameters.

Differences in the genetic potential of 285 hybrids (derived from test crosses to a single tester) with respect to N Internal Efficiency (NIE, grain yield per unit of accumulated plant N) were investigated at two Indiana locations in 2015. The hybrids (representing both early and late maturity groups) were grown at one low N rate and a single plant density. Germplasm sources included USDA, Dow AgroSciences, and “control” checks. Various secondary traits (plant height, stalk diameter, LAI, green leaf counts, and SPAD measurements) were evaluated for their potential role as surrogate measurements for N metrics at maturity (R6) such as plant N content or NIE. Four band (RGB, NIR) multispectral airborne remote sensing imagery at R1 and R3/R4 was also collected. The key findings were: 1) identification of the 10 highest and 10 lowest ranked hybrids for each maturity group in both grain yield and NIE categories, 2) the discovery of 5 top performing hybrids which had both high NIE and high yield, 3) strong correlations of leaf SPAD (at R1 and R2/R3) to grain yield or plant N at R6, 4) none of the surrogate measurements were significantly correlated to NIE, and 5) vegetation indices (NDVI and SR) from the remote imaging were not predictive of hybrid differences in yields or whole plant N content at R6. From these results we concluded genetic potential exists among current maize germplasm for NIE breeding improvements, but that more in-depth investigations were needed into other surrogate measures of relevant N efficiency traits in hybrid comparisons.

Next, hyperspectral imaging was investigated as a potential predictor of agronomic parameters related to N Use Efficiency (NUE, understood here as grain yield relative to applied N fertilizer input). Hyperspectral vegetation indices (HSI) were used to extract the image features for predicting N concentration (whole plant N at R6, %N), Nitrogen Conversion Efficiency (biomass per unit of plant N at R6, NCE), and NIE. Images were collected at V16/V18 and R1/R2 by manned aircraft and unmanned aerial vehicles (UAVs) at 50 cm spatial resolution. Nine maize hybrids, or a subset, were grown under N stress conditions with two plant densities over three site years in either 2014 or 2017. Forty HSI-based mixed models were analyzed for their predictability relative to the ground reference values of %N, NCE, and NIE. Two biomass and structural indices (HBSI1682,855 and HBSI2682,910 at R1) were predictive of NCE values and capably differentiated the highest and lowest ranked NCE hybrids. The highest prediction accuracy for NIE was achieved by two biochemical indices (HBCI8515,550 at both V16 and R1, and HBCI9490,550 at R1). These also allowed for hybrid differentiation of the highest and lowest ranked NIE hybrids. From these results, we concluded that accurate end-season prediction of hybrid differences in NCE and NIE was possible from mid-season hyperspectral imaging (yet not for %N). Furthermore, the quality of the predictions was dependent on image timing, actual HSI, and the targeted N parameter at maturity.

One benefit to hyperspectral imaging is that it can provide greater discrimination of imaged materials through its narrow, contiguous bands. However, the data are highly correlated in some ranges. This problem was mitigated through the use of partial least squares regressions (PLSR) to predict the three N parameters from the field data described previously. Data were divided into train and test; then ten-fold cross validation was performed. The twelve models evaluated included those with 89 image bands of 5 nm widths and a selected, reduced set of hyperspectral bands as predictors. The key findings were that PLSR models based on V16 and R1 images provided accurate predictions for final whole-plant %N (R2 = 0.73, CV = 11%; R2 = 0.68, CV = 10%) and NCE at R6 (R2 = 0.71, CV = 10%; R2 = 0.73, CV = 12%) but not NIE. Additionally, accurate hybrid differentiation was possible with the combination of hyperspectral imaging and PLSR at R1 to predict %N and NCE values at R6 stage.

The PLSR and HSI results from this research showed that hyperspectral imaging has the potential for prediction of NUE parameters through non-destructive remote sensing at a broad scale. Additional validation is needed through the study of other genotypes and locations. Nevertheless, practical application of these methods through the integration into early stage breeding programs may allow the early identification of the highest and lowest ranked hybrids providing data-driven decisions for selecting genotypes. Implementation of improved imaging approaches may drive the increased development of maize hybrids with superior NUE.

History

Degree Type

Doctor of Philosophy

Department

Agronomy

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Tony J. Vyn

Additional Committee Member 2

Melba M. Crawford

Additional Committee Member 3

Mitch R. Tuinstra

Additional Committee Member 4

John P. Davies

Additional Committee Member 5

Christopher Boomsma

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