Abstract
Global agriculture production is challenged by increasing demands from rising population and a 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 have yet to yield real-world 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 significantly across different plant species, regions, and growth environments. Thus, to cope with the heterogeneous performances of PLSR, this study aims to develop 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, including both transgenic and wild-type lines, with a range of photosynthetic capacities were used to test the framework. Leaf reflectance spectra were measured from 400 to 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 to apply this stacked regression technique to other plant phenotypic traits.
Highlights
Increasing demands for food, fiber, and fuel caused by rising human population and global affluence will be a burden to environment sustainability over the several decades
Among the six regression models, least absolute shrinkage and selection operator (LASSO) displayed the smallest standard deviation in both R2 and root mean square error (RMSE) while the largest standard deviation was found in artificial neural network (ANN) for both R2 and RMSE
Inspired by the recent advancements in the geographic stacking in the remote sensing community (Clinton et al, 2015; Healey et al, 2018), this study revealed that the regression stacking was superior over individual regression techniques (ANN, support vector machine (SVM), LASSO, random forest (RF), Gaussian process (GP), and partial least squares (PLS)) in capturing intraspecies variations of photosynthesis capacities among tobacco lines with genetically altered photosynthetic pathways
Summary
Increasing demands for food, fiber, and fuel caused by rising human population and global affluence will be a burden to environment sustainability over the several decades. These increasing demands are likely to be challenged further with the world’s shrinking farmlands (Sayer et al, 2013; Ort et al, 2015) and with climate change (Tester and Langridge, 2010). Photosynthesis as a process leaves significant room for improvement, which can bolster crop yields (Long et al, 2006; Zhu et al, 2008). Major research efforts are underway to increase photosynthetic energy conversion efficiency by engineering photosynthetic pathways (Yokota and Shigeoka, 2008; Ducat and Silver, 2012; Ort et al, 2015) and exploiting mechanisms underlying natural variation of photosynthesis (Flood et al, 2011; Lawson et al, 2012)
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