Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Prediction of biomass accumulation and tolerance of wheat seedlings to drought and elevated temperatures using hyperspectral imaging

Early prediction of important agricultural traits in wheat opens up broad prospects for the development of approaches to accelerate the selection of genotypes for further breeding trials. This study is devoted to the search for predictors of biomass accumulation and tolerance of wheat to abiotic stressors. Hyperspectral (HS) and chlorophyll fluorescence (ChlF) parameters were analyzed as predictors under laboratory conditions. The predictive ability of reflectance and normalized difference indices (NDIs), as well as their relationship with parameters of photosynthetic activity, which is a key process influencing organic matter production and crop yields, were analyzed. HS parameters calculated using the wavelengths in Red (R) band and the spectral range next to the red edge (FR-NIR) were found to be correlated with biomass accumulation. The same ranges showed potential for predicting wheat tolerance to elevated temperatures. The relationship of HS predictors with biomass accumulation and heat tolerance were of opposite sign. A number of ChlF parameters also showed statistically significant correlation with biomass accumulation and heat tolerance. A correlation between HS and ChlF parameters, that demonstrated potential for predicting biomass accumulation and tolerance, has been shown. No predictors of drought tolerance were found among the HS and ChlF parameters analyzed.

Evaluation of Stem Rust Disease in Wheat Fields by Drone Hyperspectral Imaging

Detecting plant disease severity could help growers and researchers study how the disease impacts cereal crops to make timely decisions. Advanced technology is needed to protect cereals that feed the increasing population using fewer chemicals; this may lead to reduced labor usage and cost in the field. Accurate detection of wheat stem rust, an emerging threat to wheat production, could inform growers to make management decisions and assist plant breeders in making line selections. A hyperspectral camera mounted on an unmanned aerial vehicle (UAV) was utilized in this study to evaluate the severity of wheat stem rust disease in a disease trial containing 960 plots. Quadratic discriminant analysis (QDA) and random forest classifier (RFC), decision tree classification, and support vector machine (SVM) were applied to select the wavelengths and spectral vegetation indices (SVIs). The trial plots were divided into four levels based on ground truth disease severities: class 0 (healthy, severity 0), class 1 (mildly diseased, severity 1-15), class 2 (moderately diseased, severity 16-34), and class 3 (severely diseased, highest severity observed). The RFC method achieved the highest overall classification accuracy (85%). For the spectral vegetation indices (SVIs), the highest classification rate was recorded by RFC, and the accuracy was 76%. The Green NDVI (GNDVI), Photochemical Reflectance Index (PRI), Red-Edge Vegetation Stress Index (RVS1), and Chlorophyll Green (Chl green) were selected from 14 SVIs. In addition, binary classification of mildly diseased vs. non-diseased was also conducted using the classifiers and achieved 88% classification accuracy. This highlighted that hyperspectral imaging was sensitive enough to discriminate between low levels of stem rust disease vs. no disease. The results of this study demonstrated that drone hyperspectral imaging can discriminate stem rust disease levels so that breeders can select disease-resistant varieties more efficiently. The detection of low disease severity capability of drone hyperspectral imaging can help farmers identify early disease outbreaks and enable more timely management of their fields. Based on this study, it is also possible to build a new inexpensive multispectral sensor to diagnose wheat stem rust disease accurately.

Development of Two-Dimensional Model of Photosynthesis in Plant Leaves and Analysis of Induction of Spatial Heterogeneity of CO2 Assimilation Rate under Action of Excess Light and Drought

Photosynthesis is a key process in plants that can be strongly affected by the actions of environmental stressors. The stressor-induced photosynthetic responses are based on numerous and interacted processes that can restrict their experimental investigation. The development of mathematical models of photosynthetic processes is an important way of investigating these responses. Our work was devoted to the development of a two-dimensional model of photosynthesis in plant leaves that was based on the Farquhar-von Caemmerer-Berry model of CO₂ assimilation and descriptions of other processes including the stomatal and transmembrane CO₂ fluxes, lateral CO₂ and HCO₃^- fluxes, transmembrane and lateral transport of H⁺ and K⁺, interaction of these ions with buffers in the apoplast and cytoplasm, light-dependent regulation of H⁺-ATPase in the plasma membrane, etc. Verification of the model showed that the simulated light dependences of the CO₂ assimilation rate were similar to the experimental ones and dependences of the CO₂ assimilation rate of an average leaf CO₂ conductance were also similar to the experimental dependences. An analysis of the model showed that a spatial heterogeneity of the CO₂ assimilation rate on a leaf surface should be stimulated under an increase in light intensity and a decrease in the stomatal CO₂ conductance or quantity of the open stomata; this prediction was supported by the experimental verification. Results of the work can be the basis of the development of new methods of the remote sensing of the influence of abiotic stressors (at least, excess light and drought) on plants.

Advances in field-based high-throughput photosynthetic phenotyping

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.

Modified Photochemical Reflectance Indices as New Tool for Revealing Influence of Drought and Heat on Pea and Wheat Plants

In environmental conditions, plants can be affected by the action of numerous abiotic stressors. These stressors can induce both damage of physiological processes and adaptive changes including signaling-based changes. Development of optical methods of revealing influence of stressors on plants is an important task for plant investigations. The photochemical reflectance index (PRI) based on plant reflectance at 531 nm (measuring wavelength) and 570 nm (reference wavelength) can be effective tool of revealing plant stress changes (mainly, photosynthetic changes); however, its efficiency is strongly varied at different conditions. Earlier, we proposed series of modified PRIs with moderate shifts of the measuring wavelength and showed that these indices can be effective for revealing photosynthetic changes under fluctuations in light intensity. The current work was devoted to the analysis of sensitivity of these modified PRIs to action of drought and short-term heat stress. Investigation of spatially-fixed leaves of pea plants showed that the modified PRI with the shorter measuring wavelength (515 nm) was increased under response of drought and heat; by contrast, the modified PRI with the longer wavelength (555 nm) was decreased under response to these stressors. Changes of investigated indices could be related to parameters of photosynthetic light reactions; however, these relations were stronger for the modified PRI with the 555 nm measuring wavelength. Investigation of canopy of pea (vegetation room) and wheat (vegetation room and open-ground) supported these results. Thus, moderate changes in the measuring wavelengths of PRI can strongly modify the efficiency of their use for the estimation of plant physiological changes (mainly photosynthetic changes) under action of stressors. It is probable that the modified PRI with the 555 nm measuring wavelength (or similar indices) can be an effective tool for revealing photosynthetic changes induced by stressors.

New Normalized Difference Reflectance Indices for Estimation of Soil Drought Influence on Pea and Wheat

Soil drought is an important problem in plant cultivation. Remote sensing using reflectance indices (RIs) can detect early changes in plants caused by soil drought. The development of new RIs which are sensitive to these changes is an important applied task. Previously, we revealed 46 normalized difference RIs based on a spectral region of visible light which were sensitive to the action of a short-term water shortage on pea plants under controlled conditions (Remote Sens. 2021, 13, 962). In the current work, we tested the efficiency of these RIs for revealing changes in pea and wheat plants induced by the soil drought under the conditions of both a vegetation room and open ground. RI (613, 605) and RI (670, 432) based on 613 and 605 nm wavelengths and on 670 and 432 nm wavelengths, respectively, were effective for revealing the action of the soil drought on investigated objects. Particularly, RI (613, 605) and RI (670, 432) which were measured in plant canopy, were significantly increased by the strong soil drought. The correlations between these indices and relative water content in plants were strong. Revealed effects were observed in both pea and wheat plants, at the plant cultivation under controlled and open-ground conditions, and using different angles of measurement. Thus, RI (613, 605) and RI (670, 432) seem to be effective tools for the remote sensing of plant changes under soil drought.

Differential Impact of the Pinewood Nematode on Pinus Species Under Drought Conditions

The pinewood nematode (PWN), Bursaphelenchus xylophilus, responsible for the pine wilt disease (PWD), is a major threat to pine forests worldwide. Since forest mortality due to PWN might be exacerbated by climate, the concerns regarding PWD in the Mediterranean region are further emphasized by the projected scenarios of more drought events and higher temperatures. In this context, it is essential to better understand the pine species vulnerability to PWN under these conditions. To achieve that, physiological responses and wilting symptoms were monitored in artificially inoculated Pinus pinaster (P. pinaster), Pinus pinea (P. pinea), and Pinus radiata (P. radiata) saplings under controlled temperature (25/30°C) and water availability (watered/water stressed). The results obtained showed that the impact of PWN is species-dependent, being infected P. pinaster and P. radiata more prone to physiological and morphological damage than P. pinea. For the more susceptible species (P. pinaster and P. radiata), the presence of the nematode was the main driver of photosynthetic responses, regardless of their temperature or water regime conditions. Nevertheless, water potential was revealed to be highly affected by the synergy of PWN and the studied abiotic conditions, with higher temperatures (P. pinaster) or water limitation (P. radiata) increasing the impact of nematodes on trees' water status. Furthermore, water limitation had an influence on nematodes density and its allocation on trees' structures, with P. pinaster revealing the highest nematode abundance and inner dispersion. In inoculated P. pinea individuals, nematodes' population decreased significantly, emphasizing this species resistance to PWN. Our findings revealed a synergistic impact of PWN infection and stressful environmental conditions, particularly on the water status of P. pinaster and P. radiata, triggering disease symptoms and mortality of these species. Our results suggest that predicted drought conditions might facilitate proliferation and exacerbate the impact of PWN on these two species, through xylem cavitation, leading to strong changes in pine forests of the Mediterranean regions.

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.

Electrical Signals, Plant Tolerance to Actions of Stressors, and Programmed Cell Death: Is Interaction Possible?

In environmental conditions, plants are affected by abiotic and biotic stressors which can be heterogenous. This means that the systemic plant adaptive responses on their actions require long-distance stress signals including electrical signals (ESs). ESs are based on transient changes in the activities of ion channels and H⁺-ATP-ase in the plasma membrane. They influence numerous physiological processes, including gene expression, phytohormone synthesis, photosynthesis, respiration, phloem mass flow, ATP content, and many others. It is considered that these changes increase plant tolerance to the action of stressors; the effect can be related to stimulation of damages of specific molecular structures. In this review, we hypothesize that programmed cell death (PCD) in plant cells can be interconnected with ESs. There are the following points supporting this hypothesis. (i) Propagation of ESs can be related to ROS waves; these waves are a probable mechanism of PCD initiation. (ii) ESs induce the inactivation of photosynthetic dark reactions and activation of respiration. Both responses can also produce ROS and, probably, induce PCD. (iii) ESs stimulate the synthesis of stress phytohormones (e.g., jasmonic acid, salicylic acid, and ethylene) which are known to contribute to the induction of PCD. (iv) Generation of ESs accompanies K⁺ efflux from the cytoplasm that is also a mechanism of induction of PCD. Our review argues for the possibility of PCD induction by electrical signals and shows some directions of future investigations in the field.

A high-throughput method for measuring critical thermal limits of leaves by chlorophyll imaging fluorescence

Plant thermal tolerance is a crucial research area as the climate warms and extreme weather events become more frequent. Leaves exposed to temperature extremes have inhibited photosynthesis and will accumulate damage to PSII if tolerance thresholds are exceeded. Temperature-dependent changes in basal chlorophyll fluorescence (T-F0) can be used to identify the critical temperature at which PSII is inhibited. We developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system. We examined how experimental conditions of wet vs dry surfaces for leaves and heating/cooling rate, affect CTMIN and CTMAX across four species. CTMAX estimates were not different whether measured on wet or dry surfaces, but leaves were apparently less cold tolerant when on wet surfaces. Heating/cooling rate had a strong effect on both CTMAX and CTMIN that was species-specific. We discuss potential mechanisms for these results and recommend settings for researchers to use when measuring T-F0. The approach that we demonstrated here allows the high-throughput measurement of a valuable ecophysiological parameter that estimates the critical temperature thresholds of leaf photosynthetic performance in response to thermal extremes.

Differential responses to two heatwave intensities in a Mediterranean citrus orchard are identified by combining measurements of fluorescence and carbonyl sulfide (COS) and CO2 uptake

The impact of extreme climate episodes such as heatwaves on plants physiological functioning and survival may depend on the event intensity, which requires quantification. We unraveled the distinct impacts of intense (HW) and intermediate (INT) heatwave days on carbon uptake, and the underlying changes in the photosynthetic system, in a Mediterranean citrus orchard using leaf active (pulse amplitude modulation; PAM) and canopy level passive (sun-induced; SIF) fluorescence measurements, together with CO₂ , water vapor, and carbonyl sulfide (COS) exchange measurements. Compared to normal (N) days, gross CO₂ uptake fluxes (gross primary production, GPP) were significantly reduced during HW days, but only slightly decreased during INT days. By contrast, COS uptake flux and SIF_A (at 760 nm) decreased during both HW and INT days, which was reflected in leaf internal CO₂ concentrations and in nonphotochemical quenching, respectively. Intense (HW) heatwave conditions also resulted in a substantial decrease in electron transport rates, measured using leaf-scale fluorescence, and an increase in the fractional energy consumption in photorespiration. Using the combined proxy approach, we demonstrate a differential ecosystem response to different heatwave intensities, which allows the trees to preserve carbon assimilation during INT days but not during HW days.

Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light

Plants are affected by numerous environmental factors that influence their physiological processes and productivity. Early revealing of their action based on measuring spectra of reflected light and calculating reflectance indices is an important stage in the protection of agricultural plants. Photochemical reflectance index (PRI) is a widely used parameter related to photosynthetic changes in plants under action of stressors. We developed a new system for proximal imaging of PRI based on using short pulses of measuring light detected simultaneously in green (530 nm) and yellow (570 nm) spectral bands. The system has several advances compared to those reported in literature. Active light illumination and subtraction of the ambient light allow for PRI measurements without periodic calibrations. Short duration of measuring pulses (18 ms) minimizes their influence on plants. Measurements in two spectral bands operated by separate cameras with aligned fields of visualization allow one to exclude mechanically switchable parts like filter wheels thus minimizing acquisition time and increasing durability of the setup. Absolute values of PRI and light-induced changes in PRI (ΔPRI) in pea leaves and changes of these parameters under action of light with different intensities, water shortage, and heating have been investigated using the developed setup. Changes in ΔPRI are shown to be more robust than the changes in the absolute value of PRI which is in a good agreement with our previous studies. Values of PRI and, especially, ΔPRI are strongly linearly related to the energy-dependent component of the non-photochemical quenching and can be potentially used for estimation of this component. Additionally, we demonstrate that the developed system can also measure fast changes in PRI (hundreds of milliseconds and seconds) under leaf illumination by the pulsed green-yellow measuring light. Thus, the developed system of proximal PRI imaging can be used for PRI measurements (including fast changes in PRI) and estimation of stressors-induced photosynthetic changes.

A light-induced decrease in the photochemical reflectance index (PRI) can be used to estimate the energy-dependent component of non-photochemical quenching under heat stress and soil drought in pea, wheat, and pumpkin

The remote sensing of a plant's physiological state is a key problem of precision agriculture. The photochemical reflectance index (PRI), which is based on the intensities of the reflected light at 531 and 570 nm, is an important tool for the remote sensing of photosynthetic processes in plants. In particular, the PRI can be strongly connected with the non-photochemical quenching of chlorophyll fluorescence (NPQ) and the quantum yield of photosystem II (ФPSII); however, this connection is dependent on illumination, the intensity of stressor actions, the time scale of measurements, etc. The aim of the present work was to analyze the connection of PRI with the energy-dependent component of NPQ (NPQF) and ФPSII under heating and soil drought conditions. Pea, wheat, and pumpkin seedlings, which were grown under controlled conditions, were investigated. A PAM fluorometer Dual-PAM-100 and spectrometer S-100 were used for measurements of photosynthetic parameters and PRI, respectively. It was shown that heat stress increased the NPQF and the magnitude of light-induced changes in PRI (ΔPRI) and decreased ФPSII in pea seedlings. The decreased ФPSII and increased ΔPRI were observed in wheat after heating, but significant changes in NPQF were absent; the significant decrease in ФPSII was observed in pumpkin seedlings, while there were no significant changes in the other parameters. ΔPRI and NPQF after heating were significantly correlated. However, a significant correlation of the absolute values of PRI with photosynthetic parameters was absent. The soil drought increased NPQF and the magnitude of ΔPRI and decreased ФPSII in peas. ΔPRI was strongly correlated with photosynthetic parameters, but this correlation was absent for the absolute value of PRI. Thus, ΔPRI is strongly connected with the magnitude of NPQF and can be used as an estimator of this parameter.

Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization

The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2−Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops.

Evaluating Multi-Angle Photochemical Reflectance Index and Solar-Induced Fluorescence for the Estimation of Gross Primary Production in Maize

The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE), and for use in the improvement of estimating gross primary production (GPP) in LUE models. Over the last two decades, solar-induced fluorescence (SIF) observations from remote sensing have been used to evaluate the distribution of GPP over a range of spatial and temporal scales. However, both PRI and SIF observations have been decoupled from photosynthesis under a variety of non-physiological factors, i.e., sun-view geometry and environmental variables. These observations are important for estimating GPP but rarely reported in the literature. In our study, multi-angle PRI and SIF observations were obtained during the 2018 growing season in a maize field. We evaluated a PRI-based LUE model for estimating GPP, and compared it with the direct estimation of GPP using concurrent SIF measurements. Our results showed that the observed PRI varied with view angles and that the averaged PRI from the multi-angle observations exhibited better performance than the single-angle observed PRI for estimating LUE. The PRI-based LUE model when compared to SIF, demonstrated a higher ability to capture the diurnal dynamics of GPP (the coefficient of determination (R2) = 0.71) than the seasonal changes (R2 = 0.44), while the seasonal GPP variations were better estimated by SIF (R2 = 0.50). Based on random forest analyses, relative humidity (RH) was the most important driver affecting diurnal GPP estimation using the PRI-based LUE model. The SIF-based linear model was most influenced by photosynthetically active radiation (PAR). The SIF-based linear model did not perform as well as the PRI-based LUE model under most environmental conditions, the exception being clear days (the ratio of direct and diffuse sky radiance > 2). Our study confirms the utility of multi-angle PRI observations in the estimation of GPP in LUE models and suggests that the effects of changing environmental conditions should be taken into account for accurately estimating GPP with PRI and SIF observations.

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Potato is the world's fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models

Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (Lactuca sativa L. var. capitata). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm-2), edible biomass (RMSE = 6.87 g m-2), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76-68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.

Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants

Measurement and analysis of the numerous reflectance indices of plants is an effective approach for the remote sensing of plant physiological processes in agriculture and ecological monitoring. A photochemical reflectance index (PRI) plays an important role in this kind of remote sensing because it can be related to early changes in photosynthetic processes under the action of stressors (excess light, changes in temperature, drought, etc.). In particular, we previously showed that light-induced changes in PRIs could be strongly related to the energy-dependent component of the non-photochemical quenching in photosystem II. The aim of the present work was to undertake comparative analysis of the efficiency of using light-induced changes in PRIs (ΔPRIs) based on different wavelengths for the estimation of the parameters of photosynthetic light reactions (including the parameters of photosystem I). Pea plants were used in the investigation; the photosynthetic parameters were measured using the pulse-amplitude-modulated (PAM) fluorometer Dual-PAM-100 and the intensities of the reflected light were measured using the spectrometer S100. The ΔPRIs were calculated as ΔPRI(band,570), where the band was 531 nm for the typical PRI and 515, 525, 535, 545, or 555 nm for modified PRIs; 570 nm was the reference wavelength for all PRIs. There were several important results: (1) ∆PRI(525,570), ∆PRI(531,570), ∆PRI(535,570), and ∆PRI(545,570) could be used for estimation of most of the photosynthetic parameters under light only or under dark only conditions. (2) The combination of dark and light conditions decreased the efficiency of ∆PRIs for the estimation of the photosynthetic parameters; ∆PRI(535,570) and ∆PRI(545,570) had maximal efficiency under these conditions. (3) ∆PRI(515,570) and ∆PRI(525,570) mainly included the slow-relaxing component of PRI; in contrast, ∆PRI(531,570), ∆PRI(535,570), ∆PRI(545,570), and ∆PRI(555,570) mainly included the fast-relaxing component of PRI. These components were probably caused by different mechanisms.

Burning-induced electrical signals influence broadband reflectance indices and water index in pea leaves

Electrical signals (ESs) can be induced by local action of stressors in plants; they influence numerous physiological processes (photosynthesis, transpiration, respiration, genes expression, synthesis of phytohormones, etc.) and, thereby, induce a systemic adaptation response. Development of optical methods of a remote sensing of this response can be important for agricultural and ecological monitoring. Preliminarily, we showed (Sukhova et al., Plant Sign Behav 2019; 14:e1610301) that burning-induced ESs induced changes in leaf reflectance at broad spectral bands (400-500, 500-600, 600-700, and 700-800 nm). The aims of the present work were (i) investigation of ESs influence on difference reflectance indices (RIs) calculated on the basis of these broad spectral bands and (ii) analysis of connection of the indices with water content in plants. Pea seedlings were investigated. ESs were induced by burning of the first mature leaf; ESs had high amplitudes in the second leaf and had low amplitudes in the fourth leaf. It was shown that ESs induced significant changes in RIs, which were calculated on basis of intensities of the reflected light at (i) 400-500 and 600-700 nm, (ii) 500-600 and 700-800 nm, and (iii) 600-700 and 700-800 nm. The effect was strong in the second leaf and weak in the fourth leaf; that is, the response was dependent on the magnitude of ESs. ESs-induced changes in RI were strongly connected with ESs-induced decrease of leaf water content which was estimated on basis of decrease of water index. Thus, broadband RIs can be used for revealing the ESs-induced systemic stress response in plants.

Simulation of a nonphotochemical quenching in plant leaf under different light intensities

An analysis of photosynthetic response on action of stressors is an important problem, which can be solved by experimental and theoretical methods, including mathematical modeling of photosynthetic processes. The aim of our work was elaboration of a mathematical model, which simulated development of a nonphotochemical quenching under different light conditions. We analyzed two variants of the model: the first variant included a light-induced activation of the electron transport chain; in contrast, the second variant did not describe this activation. Both variants of the model described interactions between transitions from open reaction centers to closed ones (and vice versa) and development of the nonphotochemical quenching. Investigation of both variants of the model showed well qualitative and quantitative accordance between simulated and experimental changes in coefficient of the nophotochemical quenching which were analyzed under different light regimes: (i) the stepped increase of the light intensity without dark intervals between steps, (ii) periodical illuminations by different light intensities with constant durations which were separated by constant dark intervals, and (iii) periodical illuminations by the constant light intensity with different durations which were separated by different dark intervals. Thus, the model can be used for theoretical prediction of stress changes in photosynthesis under fluctuations in light intensity and search of optimal regimes of plant illumination.

Photoresponse to different lighting strategies during red leaf lettuce growth

The objective of the study was to investigate the effects of growth-stage specific lighting for the physiological homeostasis of red leaf lettuce (Lactuca sativa L. cv. Red Cos), by measuring the productivity of photosynthesis and primary metabolism. In the experiments, the main photosynthetic photon flux consisted of red (R) and blue (B) light, supplemented with blue, green (G) or UV-A wavelengths. Decrease of fructose, accompanied by significant decrease of stomatal conductance (gs), the ratio of intracellular to ambient CO2 concentration (Ci/Ca), photosynthetic rate (Pr), light adapted actual quantum yield of PSII photochemistry (ΦPSII), biomass formation and significant increase of transpiration rate (Tr) suggest that supplemental UV-A during maturity stage, after supplemental green irradiation during seedling stage (BRG to BRUV) was the least favourable condition for red leaf lettuce. However, constant irradiation with supplemental green (BRG) or supplemental green irradiation after increased blue exposure (B↑R to BRG) resulted in significant increase of Pr, gs, Ci/Ca, and light use efficiency(LUE), and decrease of Tr and Water use efficiency (WUE). Significant increase of leaf area was observed under supplemental green in both seedlings (BR; BRG) and matured plants (B↑R to BRG). Significant increase of specific leaf area was found under supplemental green (BRG) for seedlings and under increased blue (B↑R) for matured plants. Accordingly, the most favourable growth-stage specific lighting spectrum strategy for red leaf lettuce, based on photosynthetic and primary metabolite response, is supplemental green irradiation after increased blue exposure (B↑R to BRG), whereas, the most favourable condition for seedlings is BRG. According to the PCA correlation matrix, associations among the measured data indicate that WUE negatively correlated with gs and Ci/Ca, while LUE positively correlated with gs and Pr. However, weak correlations between ФPSII, LUE and photochemical reflectance index (PRI) suggest that selected light conditions were not optimal for red leaf lettuce.

Genotype Specific Photosynthesis x Environment Interactions Captured by Automated Fluorescence Canopy Scans Over Two Fluctuating Growing Seasons

Photosynthesis reacts dynamic and in different time scales to changing conditions. Light and temperature acclimation balance photosynthetic processes in a complex interplay with the fluctuating environment. However, due to limitations in the measurements techniques, these acclimations are often described under steady-state conditions leading to inaccurate photosynthesis estimates in the field. Here we analyze the photosynthetic interaction with the fluctuating environment and canopy architecture over two seasons using a fully automated phenotyping system. We acquired over 700,000 chlorophyll fluorescence transients and spectral measurements under semi-field conditions in four crop species including 28 genotypes. As expected, the quantum efficiency of the photosystem II (Fv/Fm in the dark and Fq'/Fm' in the light) was determined by light intensity. It was further significantly affected by spectral indices representing canopy structure effects. In contrast, a newly established parameter, monitoring the efficiency of electron transport (Fr2/Fv in the dark respective Fr2'/Fq' in the light), was highly responsive to temperature (R2 up to 0.75). This parameter decreased with temperature and enabled the detection of cold tolerant species and genotypes. We demonstrated the ability to capture and model the dynamic photosynthesis response to the environment over entire growth seasons. The improved linkage of photosynthetic performance to canopy structure, temperature and cold tolerance offers great potential for plant breeding and crop growth modeling.

Influence of electrical signals on pea leaf reflectance in the 400-800-nm range

Local action of stressors induces generation and propagation of electrical signals (ESs), which influence numerous physiological processes (including photosynthesis, expression of genes, production of phytohormones, etc.) in undamaged parts of plants; i.e. they induce a systemic stress response. Development of methods of remote sensing of this response (in particular, optical methods) is an important practical task for agricultural and ecological monitoring. However, this problem is not sufficiently researched. Earlier, we reported that ESs influence the photochemical reflectance index, which can be calculated on the basis of reflected light at 531 and 570 nm, and these changes are connected with photosynthetic changes. The aim of the current work is investigation of the influence of ESs on reflectance at broad spectral bands (400-500 nm, 500-600 nm, 600-700 nm and 700-800 nm). We showed that burning-induced ESs caused transient increase of intensity of reflected light at the all investigated spectral bands of visible light: reflectance at 600-700 nm had the maximal magnitude of changes and reflectance at 700-800 nm had the minimal magnitude of changes. Dynamics of the reflectance changes were distinguished from dynamics of photosynthetic changes, induced by ESs; i.e. ESs-induced changes in reflectance seem to be weakly connected with the photosynthetic response. Thus, our results show that changes in reflectance at broad spectral bands can also be used for remote sensing of the ESs-induced systemic stress response in plants.

Analysis of Light-Induced Changes in the Photochemical Reflectance Index (PRI) in Leaves of Pea, Wheat, and Pumpkin Using Pulses of Green-Yellow Measuring Light

The photochemical reflectance index (PRI) is a widely used spectral index which can show stress-induced changes in photosynthesis (e.g., increase of the nonphotochemical quenching of chlorophyll fluorescence (NPQ)). The artificial illumination of plants improves the efficiency of estimation of photosynthetic processes on the basis of PRI measurements. However, the simultaneous activity of different light sources with different locations can disturb the measurement of PRI. Using pulses of a green-yellow measuring light can potentially solve this problem. The aim of the present work was to investigate the possibility of using green-yellow light pulses for the investigation of light-induced changes in PRI in higher plants (pea, wheat, and pumpkin) and for the analysis of connection between PRI and the energy-dependent component of NPQ (NPQF). First, we showed that using green-yellow light pulses eliminated shifts of reflected light, which were connected with the application of a red actinic light. Second, analysis of light dependences of NPQF, the absolute value of PRI, and changes in PRI (the difference between the PRI under the actinic light and the initial value of PRI without this light, ΔPRI) showed that the dynamics of the increase of NPQF and the decrease of PRI and ΔPRI were similar. Changes in NPQF and ΔPRI were found to be significant. In contrast, changes in the absolute value of PRI were not significant in most of the variants of the experiments. Third, scatter plots between NPQF and ΔPRI showed similar linear correlations for investigated species; moreover, a total set of experimental points (for pea, wheat, and pumpkin) were also described by the same linear regression. Thus, our results show that (i) pulses of green-yellow measuring light can be used for measurements of PRI, and (ii) ΔPRI is a more effective indicator for the estimation of NPQ than the absolute value of PRI.

The electrical signal-induced systemic photosynthetic response is accompanied by changes in the photochemical reflectance index in pea

Plants can be affected by numerous environmental stressors with spatially heterogeneous actions on their bodies. A fast systemic photosynthetic response, which is connected with long-distance electrical signalling, plays an important role in the adaptation of higher plants to the action of stressors. Potentially, measurement of the response by using a photochemical reflectance index (PRI) could be the basis of monitoring photosynthesis under spatially heterogeneous stressors; however, the method has not been previously used for investigating the systemic photosynthetic response. We investigated changes in PRI and photosynthetic parameters (quantum yields of PSI and PSII and nonphotochemical quenching) in intact leaves of pea (Pisum sativum L.) after local heating of another leaf and the propagation of electrical signals through the plant body. We showed that electrical signals decreased the quantum yields of PSI and PSII and increased the nonphotochemical quenching of intact leaves in times ranging from minutes to tens of minutes; the changes were strongly connected with changes in PRI. Additional analysis showed that changes in PRI were caused by an increase of the energy-dependent quenching induced by electrical signals. Thus PRI can be potentially used for monitoring the systemic photosynthetic response connected with long-distance electrical signalling.

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Long-distance electrical signals as a link between the local action of stressors and the systemic physiological responses in higher plants

Our review is devoted to the analysis of the role of long-distance electrical signals in the development of the fast systemic physiological responses in higher plants. The characteristics and mechanisms of basic electrical signals (variation potential, action potential and system potential) are analyzed, and a potential schema of the generation and propagation of the system potential is proposed. The review summarizes the physiological changes induced by the variation potential, action potential and system potential in higher plants, including changes in gene expressions, the production of phytohormones, photosynthesis, phloem mass-flow, respiration, ATP content, transpiration and plant growth. Potential mechanisms of the changes are analyzed. Finally, a hypothetical schema, which describes a hierarchy of the variation potential, action potential and system potential, in the development of the fast systemic non-specific adaptation of plants to stressors, is proposed.

Chemical Sensing Employing Plant Electrical Signal Response-Classification of Stimuli Using Curve Fitting Coefficients as Features

In order to exploit plants as environmental biosensors, previous researches have been focused on the electrical signal response of the plants to different environmental stimuli. One of the important outcomes of those researches has been the extraction of meaningful features from the electrical signals and the use of such features for the classification of the stimuli which affected the plants. The classification results are dependent on the classifier algorithm used, features extracted and the quality of data. This paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal. A curve fitting approach in extracting features from the raw signal for classification of the applied stimuli has been adopted in this work, thereby evaluating whether the shape of the raw signal is dependent on the stimuli applied. Four types of curve fitting models—Polynomial, Gaussian, Fourier and Exponential, have been explored. The fitting accuracy (i.e., fitting of curve to the actual raw signal) depicted through R-squared values has allowed exploration of which curve fitting model performs best. The coefficients of the curve fit models were then used as features. Thereafter, using simple classification algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) etc. within the curve fit coefficient space, we have verified that within the available data, above 90% classification accuracy can be achieved. The successful hypothesis taken in this work will allow further research in implementing plants as environmental biosensors.