High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel

Yufeng Ge; Abbas Atefi; Huichun Zhang; Chenyong Miao; Raghuprakash Kastoori Ramamurthy; Brandi Sigmon; Jinliang Yang; James C Schnable

doi:10.1186/s13007-019-0450-8

High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel

Plant Methods. 2019 Jun 26:15:66. doi: 10.1186/s13007-019-0450-8. eCollection 2019.

Authors

Yufeng Ge¹, Abbas Atefi¹, Huichun Zhang^{1

2}, Chenyong Miao³, Raghuprakash Kastoori Ramamurthy³, Brandi Sigmon⁴, Jinliang Yang³, James C Schnable³

Affiliations

¹ 1Department of Biological Systems Engineering, L.W. Chase Hall 203, University of Nebraska - Lincoln, Lincoln, NE 68583 USA.
² 2College of Mechanical and Electrical Engineering, Nanjing Forestry University, Nanjing, China.
³ 3Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE USA.
⁴ 4Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, NE USA.

Abstract

Background: Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS-NIR-SWIR, 400-2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS-NIR-SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS-NIR-SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties.

Results: Some VIs were able to estimate CHL and N (R² > 0.68), but failed to estimate the other four leaf properties. Models developed with PLSR and SVR exhibited comparable performance to each other, and provided improved accuracy relative to VI models. CHL were estimated most successfully, with R² (coefficient of determination) > 0.94 and ratio of performance to deviation (RPD) > 4.0. N was also predicted satisfactorily (R² > 0.85 and RPD > 2.6). LWC, SLA and K were predicted moderately well, with R² ranging from 0.54 to 0.70 and RPD from 1.5 to 1.8. The lowest prediction accuracy was for P, with R² < 0.5 and RPD < 1.4.

Conclusion: This study showed that VIS-NIR-SWIR reflectance spectroscopy is a promising tool for low-cost, nondestructive, and high-throughput analysis of a number of leaf physiological and biochemical properties. Full-spectrum based modeling approaches (PLSR and SVR) led to more accurate prediction models compared to VI-based methods. We called for the construction of a leaf VIS-NIR-SWIR spectral library that would greatly benefit the plant phenotyping community for the research of plant leaf traits.

Keywords: Hyperspectral; Machine learning; Macronutrients; Partial least squares regression; Plant phenotyping; Support vector regression; Vegetation indices.