Rapid Chlorophyll a Fluorescence Light Response Curves Mechanistically Inform Photosynthesis Modeling

Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning

Background

Autofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research.

Methods

Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stress-based image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress.

Results

The study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT).

Conclusion

Autofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science.

Combining PSII photochemistry and hydraulics improves predictions of photosynthesis and water use from mild to lethal drought

Rising temperatures and increases in drought negatively impact the efficiency and sustainability of both agricultural and forest ecosystems. Although hydraulic limitations on photosynthesis have been extensively studied, a solid understanding of the links between whole plant hydraulics and photosynthetic processes at the cellular level under changing environmental conditions is still missing, hampering our predictive power for plant mortality. Here, we examined plant hydraulic traits and CO2 assimilation rate under progressive water limitation by implementing Photosystem II (PSII) dynamics with a whole plant process model (TREES). The photosynthetic responses to plant water status were parameterized based on measurements of chlorophyll a fluorescence, gas exchange and water potential for Brassica rapa (R500) grown in a greenhouse under fully watered to lethal drought conditions. The updated model significantly improved predictions of photosynthesis, stomatal conductance and leaf water potential. TREES with PSII knowledge predicted a larger hydraulic safety margin and a decrease in percent loss of conductivity. TREES predicted a slower decrease in leaf water potential, which agreed with measurements. Our results highlight the pressing need for incorporating PSII drought photochemistry into current process models to capture cross-scale plant water dynamics from cell to whole plant level.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

Quantifying senescence in bread wheat using multispectral imaging from an unmanned aerial vehicle and QTL mapping

Environmental stresses from climate change can alter source-sink relations during plant maturation, leading to premature senescence and decreased yields. Elucidating the genetic control of natural variations for senescence in wheat (Triticum aestivum) can be accelerated using recent developments in unmanned aerial vehicle (UAV)-based imaging techniques. Here, we describe the use of UAVs to quantify senescence in wheat using vegetative indices (VIs) derived from multispectral images. We detected senescence with high heritability, as well as its impact on grain yield (GY), in a doubled-haploid population and parent cultivars at various growth time points (TPs) after anthesis in the field. Selecting for slow senescence using a combination of different UAV-based VIs was more effective than using a single ground-based vegetation index. We identified 28 quantitative trait loci (QTL) for vegetative growth, senescence, and GY using a 660K single-nucleotide polymorphism array. Seventeen of these new QTL for VIs from UAV-based multispectral imaging were mapped on chromosomes 2B, 3A, 3D, 5A, 5D, 5B, and 6D; these QTL have not been reported previously using conventional phenotyping methods. This integrated approach allowed us to identify an important, previously unreported, senescence-related locus on chromosome 5D that showed high phenotypic variation (up to 18.1%) for all UAV-based VIs at all TPs during grain filling. This QTL was validated for slow senescence by developing kompetitive allele-specific PCR markers in a natural population. Our results suggest that UAV-based high-throughput phenotyping is advantageous for temporal assessment of the genetics underlying for senescence in wheat.

Rapid Light-Response Curve of Chlorophyll Fluorescence in Terrestrial Plants: Relationship to CO2 Exchange among Five Woody and Four Fern Species Adapted to Different Light and Water Regimes

The rapid light response of electron transport rate (ETRR), obtained from chlorophyll fluorescence parameters by short illumination periods (10-30 s) at each light level, can provide a rapid and easy measurement of photosynthetic light response in plants. However, the relationship between ETRR and the steady-state light response of CO2 exchange rate (AS) of terrestrial plants has not been studied in detail. In this study, we compared the ETRR and AS for five woody and four fern species with different light and/or water adaptations. Under well-watered conditions, a constant temperature (25 °C) and with stomatal conductance (gs) not being a main limiting factor for photosynthesis, ETRR and AS were closely related, even when merging data for regression analysis for a species grown under different light conditions and measured under different light intensity and air humidity. However, when Alnus formosana was treated with low soil water and air humidity, because of the decrease in AS mainly due to stomatal closure, the ETRR-AS relation was not so close. In addition, at both 100 and 2000 μmol m-2 s-1 photosynthetic photon flux density (PPFD), ETRR and AS were significantly correlated within a plant group (i.e., woody plants and ferns) regardless of the broad difference in AS due to different species or environmental factors. The results indicate that the relationship between the ETRR and AS is varied by species. We concluded that 1) ETRR could reflect the variation in AS at each irradiance level within a species under well-watered conditions and 2) ETRR at 100 μmol m-2 s-1 PPFD (as the efficiency of light capture) or 2000 μmol m-2 s-1 PPFD (as a maximum photosynthetic parameter) could be used to compare the photosynthetic capacity within a plant group, such as woody plants and ferns.

Modeling vegetative vigour in grapevine: unraveling underlying mechanisms

Mechanistic modeling constitutes a powerful tool to unravel complex biological phenomena. This study describes the construction of a mechanistic, dynamic model for grapevine plant growth and canopy biomass (vigor). To parametrize and validate the model, the progeny from a cross of Ramsey (Vitis champinii) × Riparia Gloire (V. riparia) was evaluated. Plants with different vigor were grown in a greenhouse during the summer of 2014 and 2015. One set of plants was grafted with Cabernet Sauvignon. Shoot growth rate (b), leaf area (LA), dry biomass, whole plant and root specific hydraulic conductance (k_H and L_pr), stomatal conductance (g_s), and water potential (Ψ) were measured. Partitioning indices and specific leaf area (SLA) were calculated. The model includes an empirical fit of a purported seasonal pattern of bioactive GAs based on published seasonal evolutionary levels and reference values. The model provided a good fit of the experimental data, with R = 0.85. Simulation of single trait variations defined the individual effect of each variable on vigor determination. The model predicts, with acceptable accuracy, the vigor of a young plant through the measurement of L_pr and SLA. The model also permits further understanding of the functional traits that govern vigor, and, ultimately, could be considered useful for growers, breeders and those studying climate change.

Selenium mitigates salt-induced oxidative stress in durum wheat (Triticum durum Desf.) seedlings by modulating chlorophyll fluorescence, osmolyte accumulation, and antioxidant system

Hydroponic experiments were conducted to investigate the effects of different concentrations of sodium selenate (Na₂SeO₄) and sodium selenite (Na₂SeO₃) on durum wheat seed germination and seedling growth under salt stress. The treatments used were 0 and 50 mM NaCl solutions, each supplemented with Na₂SeO₄ or Na₂SeO₃ at 0, 0.1, 1, 2, 4, 8, or 10 μM. Salt alone significantly inhibited seed germination and reduced seedling growth. Addition of low concentrations (0.1-4 μM) of Na₂SeO₄ or Na₂SeO₃ mitigated the adverse effects of salt stress on seed germination, biomass accumulation, and other physiological attributes. Among them, 1 μM Na₂SeO₄ was most effective at restoring seed germination rate, germination energy, and germination index, significantly increasing these parameters by about 12.35, 24.17, and 11.42%, respectively, compared to salt-stress conditions. Adding low concentrations of Na₂SeO₄ or Na₂SeO₃ to the salt solution also had positive effects on chlorophyll fluorescence indices, decreased the concentrations of free proline and malondialdehyde, as well as electrolyte leakage, and increased catalase, superoxide dismutase, and peroxidase activities in roots and shoots. However, high concentrations (8-10 μM) of Na₂SeO₄ or Na₂SeO₃ disrupted seed germination and seedling growth, with damage caused by Na₂SeO₃ being more severe than that by Na₂SeO₄. It is thus clear that exogenous selenium can improve the adaptability of processing wheat to salt stress and maintain higher photosynthetic rate by decreasing the accumulation of reactive oxygen species and alleviating the degree of membrane lipid peroxidation. Na₂SeO₄ was more effective than Na₂SeO₃ at all given concentrations.