Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat

Predicting dark respiration rates of wheat leaves from hyperspectral reflectance

Greater availability of leaf dark respiration (R_dark ) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of R_dark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non-destructive and high-throughput method of estimating R_dark from leaf hyperspectral reflectance data that was derived from leaf R_dark measured by a destructive high-throughput oxygen consumption technique. We generated a large dataset of leaf R_dark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for R_dark . Leaf R_dark (per unit leaf area, fresh mass, dry mass or nitrogen, N) varied 7- to 15-fold among individual plants, whereas traits known to scale with R_dark , leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf R_dark , N, and LMA with r² values of 0.50-0.63, 0.91, and 0.75, respectively, and relative bias of 17-18% for R_dark and 7-12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf R_dark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of R_dark are discussed.

Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms

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 R 2 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 R 2 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.

Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-nitrogen status

Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.

Evaluation of Hyperspectral Reflectance Parameters to Assess the Leaf Water Content in Soybean

Nondestructive assessment of water content and water stress in plants is an important component in the rational use of crop irrigation management in precision agriculture. Spectral measurements of light reflectance in the UV/VIS/NIR region (350–1075 nm) from individual leaves were acquired under a rapid dehydration protocol for validation of the remote sensing water content assessment in soybean plants. Four gravimetrical approaches of leaf water content assessment were used: relative water content (RWC), foliar water content as percent of total fresh mass (FWCt), foliar water content as percent of dry mass (FWCd), and equivalent water thickness (EWT). Leaf desiccation resulted in changes in optical properties with increasing relative reflectance at wavelengths between 580 and 700 nm. The highest positive correlations were observed for the relations between the photochemical reflectance index (PRI) and EWT (rP = 0.860). Data analysis revealed that the specific water absorption band at 970 nm showed relatively weaker sensitivity to water content parameters. The prediction of leaf water content parameters from PRI measurements was better with RMSEs of 12.4% (rP = 0.786), 9.1% (rP = 0.736), and 0.002 (rP = 0.860) for RWC, FWCt, and EWT (p < 0.001), respectively. The results may contribute to more efficient crop water management and confirmed that EWT has a statistically closer relationship with reflectance indices than other monitored water parameters.

Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear Regression (MLR) and Partial Least Squares (PLS)] with or without (W/O) prior wavelength selection (multicollinearity, genetic algorithms, or in combination) were considered. PLS generated better estimates than MLR, although prior wavelength selection improved MLR predictions. When data from the environments were combined, PLS W/O gave the best assessment for most of the traits, while in individual environments, the results varied according to the trait and methodology considered. The highest validation predictions were obtained for chlorophyll a/b (R2Val ≤ 0.87), maximum electron transport rate (R2Val ≤ 0.60), and the irradiance at which the electron transport rate is saturated (R2Val ≤ 0.59). The results of this study, the first to model modulated chlorophyll fluorescence by reflectance, confirming the potential for implementing this tool in blueberry breeding programs, at least for the estimation of a number of important physiological traits. Additionally, the differential effects of the environment on the spectral signature of each cultivar shows this tool could be directly used to assess their tolerance to specific environments.

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High-throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis-related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio-engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype-to-gene identification pipeline.

The nitrogen cost of photosynthesis

Global food security depends on three main cereal crops (wheat, rice and maize) achieving and maintaining high yields, as well as increasing their future yields. Fundamental to the production of this biomass is photosynthesis. The process of photosynthesis involves a large number of proteins that together account for the majority of the nitrogen in leaves. As large amounts of nitrogen are removed in the harvested grain, this needs to be replaced either from synthetic fertilizer or biological nitrogen fixation. Knowledge about photosynthetic properties of leaves in natural ecosystems is also important, particularly when we consider the potential impacts of climate change. While the relationship between nitrogen and photosynthetic capacity of a leaf differs between species, leaf nitrogen content provides a useful way to incorporate photosynthesis into models of ecosystems and the terrestrial biosphere. This review provides a generalized nitrogen budget for a C3 leaf cell and discusses the potential for improving photosynthesis from a nitrogen perspective.

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

Improving traits in wheat has historically been challenging due to its large and polyploid genome, limited genetic diversity and in-field phenotyping constraints. However, within recent years many of these barriers have been lowered. The availability of a chromosome-level assembly of the wheat genome now facilitates a step-change in wheat genetics and provides a common platform for resources, including variation data, gene expression data and genetic markers. The development of sequenced mutant populations and gene-editing techniques now enables the rapid assessment of gene function in wheat directly. The ability to alter gene function in a targeted manner will unmask the effects of homoeolog redundancy and allow the hidden potential of this polyploid genome to be discovered. New techniques to identify and exploit the genetic diversity within wheat wild relatives now enable wheat breeders to take advantage of these additional sources of variation to address challenges facing food production. Finally, advances in phenomics have unlocked rapid screening of populations for many traits of interest both in greenhouses and in the field. Looking forwards, integrating diverse data types, including genomic, epigenetic and phenomics data, will take advantage of big data approaches including machine learning to understand trait biology in wheat in unprecedented detail.

Nondestructive Phenomic Tools for the Prediction of Heat and Drought Tolerance at Anthesis in Brassica Species

Oilseed Brassica species are vulnerable to heat and drought stress, especially in the early reproductive stage. We evaluated plant imaging of whole plant and flower tissue, leaf stomatal conductance, leaf and bud temperature, photochemical reflectance index, quantum yield of photosynthesis, and leaf gas exchange for their suitability to detect tolerance to heat (H) and/or drought (D) stress treatments in 12 Brassica genotypes (G). A replicated factorial experiment was set up with 7 d of stress treatment from the beginning of anthesis with various levels of three factors H, D, and G. Most phenomics tools detected plant stress as indicated by significant main effects of H, D, and H×D. Whole plant volume was highly correlated with fresh weight changes, suggesting that whole plant imaging may be a useful surrogate for fresh weight in future studies. Vcmax, the maximum carboxylation rate of photosynthesis, increased rapidly on day 1 in H and H+D treatments, and there were significant interactions of G×H and G×D. Vcmax of genotypes on day 1 in H and H+D treatments was positively correlated with their harvested seed yield. Vcmax on day 1 and day 3 were clustered with seed yield in H and H+D treatments as shown in the heatmaps of genotypic correlations. TPU, the rate of triose phosphate use, also showed significant positive genotypic correlations with seed yield in H+D treatments. Flower volume showed significant interactions of G×H and G×D on day 7, and flower volume of genotypes on day 7 in H was positively correlated with their harvested seed yield. There were few interactions of G×H or G×D for leaf stomatal conductance, leaf and bud temperature, photochemical reflectance index, and quantum yield of photosynthesis. Vcmax, TPU, and volume of flowers are potential nondestructive phenomic traits for heat or combined heat and drought stress tolerance screening in Brassica germplasm.

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

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.

Translating High-Throughput Phenotyping into Genetic Gain

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and 'phenotypers' in an effective manner.

Prescreening in large populations as a tool for identifying elevated CO2-responsive genotypes in plants

Elevated atmospheric CO2 concentration (e[CO2]) can stimulate the photosynthesis and productivity of C3 species including food and forest crops. Intraspecific variation in responsiveness to e[CO2] can be exploited to increase productivity under e[CO2]. However, active selection of genotypes to increase productivity under e[CO2] is rarely performed across a wide range of germplasm, because of constraints of space and the cost of CO2 fumigation facilities. If we are to capitalise on recent advances in whole genome sequencing, approaches are required to help overcome these issues of space and cost. Here, we discuss the advantage of applying prescreening as a tool in large genome×e[CO2] experiments, where a surrogate for e[CO2] was used to select cultivars for more detailed analysis under e[CO2] conditions. We discuss why phenotypic prescreening in population-wide screening for e[CO2] responsiveness is necessary, what approaches could be used for prescreening for e[CO2] responsiveness, and how the data can be used to improve genetic selection of high-performing cultivars. We do this within the framework of understanding the strengths and limitations of genotype-phenotype mapping.