Impact Factor 3.677
2017 JCR, Clarivate Analytics 2018

The world's most-cited Plant Sciences journal

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Plant Sci. | doi: 10.3389/fpls.2019.00730

Hyperspectral leaf reflectance as proxy for photosynthetic capacities: an ensemble approach based on multiple machine learning algorithms

  • 1Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana–Champaign, United States
  • 2Department of Plant Biology, University of Illinois at Urbana-Champaign, United States
  • 3Global Change and Photosynthesis Research, USDA ARS, United States
  • 4Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, United States
  • 5National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, United States

Global agriculture production is challenged by increasing demands from a rising population and changing climate, which may be alleviated through development of genetically improved crop cultivars. Research into increasing photosynthetic energy conversion efficiency has proposed many strategies to improve production but has yet to yield solutions, largely because of a phenotyping bottleneck. Partial least squares regression (PLSR) is a statistical technique that is increasingly used to relate hyperspectral reflectance to key photosynthetic capacities associated with carbon uptake (maximum carboxylation rate of Rubisco, Vc,max) and conversion of light energy (maximum electron transport rate supporting RuBP regeneration, Jmax) to alleviate this bottleneck. However, its performance varies across different plant species, regions, and growth environments. Thus, to cope with the heterogenious performances of PLSR, this study develops a new approach to estimate photosynthetic capacities. A framework was developed that combines six machine learning algorithms, including artificial neural network (ANN), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), random forest (RF), Gaussian process (GP), and PLSR to optimize high-throughput analysis of the two photosynthetic variables. Six tobacco genotypes with a range of photosynthetic capacities were used to test the framework. Leaf reflectance spectra were measured from 400-2500 nm using a high-spectral-resolution spectroradiometer. Corresponding photosynthesis vs. intercellular CO2 concentration response curves were measured for each leaf using a leaf gas-exchange system. Results suggested that the mean R2 value of the six regression techniques for predicting Vc,max (Jmax) ranged from 0.60 (0.45) to 0.65 (0.56) with the mean RMSE value varying from 47.1 (40.1) to 54.0 (44.7) µmol m-2 s-1. Regression stacking for Vc,max (Jmax) performed better than the individual regression techniques with increases in R2 of 0.1 (0.08) and decreases in RMSE by 4.1 (6.6) µmol m-2 s-1, equal to 8% (15%) reduction in RMSE. Better predictive performance of the regression stacking is likely attributed to the varying coefficients (or weights) in the level-2 model (the LASSO model) and the diverse ability of each individual regression technique to utilize spectral information for the best modeling performance. Further refinements can be made apply this stacked regression technique to other plant phenotypic traits.

Keywords: Photosynthesis, High throughput phenotyping, machine learning, Stacked regression, Gas exchange (photosynthesis)

Received: 14 Feb 2019; Accepted: 16 May 2019.

Edited by:

Andreas Hund, ETH Zürich, Switzerland

Reviewed by:

Elias Kaiser, Max Planck Institute for Molecular Plant Physiology, Germany
Steven M. Driever, Wageningen University & Research, Netherlands  

Copyright: © 2019 Fu, Meacham-Hensold, Bernacchi and Guan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Carl J. Bernacchi, Global Change and Photosynthesis Research, USDA ARS, Urbana, 61801, Illinois, United States, bernacch@illinois.edu