Hyperspectral Reflectance of Light-Adapted Leaves Can Predict Both Dark- and Light-Adapted Chl Fluorescence Parameters, and the Effects of Chronic Ozone Exposure on Date Palm (Phoenix dactylifera)

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

An assessment of ozone risk for date palm suggests that phytotoxic ozone dose nonlinearly affects carbon gain

Tropospheric ozone (O3) is a significant phytotoxic air pollutant that has a negative impact on plant carbon gain. Although date palm (Phoenix dactylifera L.) is a globally important crop in arid or semi-arid regions, so far O3 risk assessment for this species has not been reported. This study estimated leaf- and plant-level photosynthetic CO2 uptake for understanding how elevated levels of O3 affects date palm biomass growth. Ozone risks to date palm plants were assessed based on exposure- (AOT40) or flux-based indices (Phytotoxic Ozone Dose, PODy, where y is a threshold of uptake). For this purpose, plants were exposed to three levels of O3 [ambient air, AA (45 ppb as daily average); 1.5 × AA; 2.0 × AA] for 92 days in an O3 Free-Air Controlled Exposure facility. According to the model simulations, the negative effects of O3 on plant-level net photosynthetic CO2 uptake were attributed to reduced gross photosynthetic carbon gain and increased respiratory carbon loss. Season-long O3 exposure and elevated temperatures promoted the negative O3 effect because of a further increase of respiratory carbon loss, which was caused by increased leaf temperature due to stomatal closure. POD1 nonlinearly affected the photosynthetic CO2 uptake, which was closely related to the variation of dry mass increment during the experiment. Although the dose-response relationship suggested that a low O3 dose (POD1 < 5.2 mmol m-2) may even positively affect photosynthetic CO2 uptake in date palms, stomatal O3 uptake at the current ambient O3 levels has potentially a negative impact on date palm growth. The results indicate 5.8 mmol m-2 POD1 or 21.1 ppm h AOT40 as critical levels corresponding to a 4% reduction of net CO2 uptake for date palm, suggesting that this species can be identified as a species moderately sensitive to O3.

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms-such as PLS, VIP, iPLS-VIP, GA, RF, and CARS-the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

The Visibility of Changes in the Antioxidant Compound Profiles of Strawberry and Raspberry Fruits Subjected to Different Storage Conditions Using ATR-FTIR and Chemometrics

Strawberry cultivars Portola and Enduro, as well as raspberry cultivars Enrosadira and Kwazi, were evaluated for their antioxidant potential after treatment with gaseous ozone and different refrigeration storage conditions. Their antioxidant capacity was investigated with ABTS and DPPH methods, and the chemical composition was determined by measuring the total phenolic (TPC) and flavonoid (TFC) compounds. The classification of different samples of berry puree was influenced significantly by both the cultivars and the refrigeration storage method. Moreover, FTIR spectroscopy coupled with chemometrics was used as an alternative technique to conventional methods to determine the chemical composition of strawberries and raspberries. The chemometric discrimination of samples was achieved using principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800-500 cm-1). The fingerprint range between 1500 and 500 cm-1, corresponding to deformation vibrations from polysaccharides, pectin and organic acid content, had a significant impact on the grouping of samples. The results obtained by PCA-LDA scores revealed a clear separation between four classes of samples and demonstrated a high overall classification rate of 97.5% in differentiating between the raspberry and strawberry cultivars.

Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA₃) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA₃ concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R²_CV values ranging from 0.81 to 0.87 and RPD_P values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s-PLS were most significant for SWIR1 and SWIR2, while i-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R² values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C₃ and C₄ plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

Chronic ozone exposure impairs the mineral nutrition of date palm (Phoenix dactylifera) seedlings

Chronic ozone (O₃) exposure in the atmosphere preferentially disturbs metabolic processes in the roots rather than the shoot as a consequence of reduced photosynthesis and carbohydrate allocation from the leaves to the roots. The aim of the present study was to elucidate if mineral nutrition is also impaired by chronic O₃ exposure. For this purpose, date palm (Phoenix dactylifera) plants were fumigated with ambient, 1.5 × ambient and 2 × ambient O₃ in a free air controlled exposure (FACE) system for one growing season and concentrations of major nutrients were analyzed in leaves and roots. In addition, concentrations of C and N and their partitioning between different metabolic C and N pools were determined in both organs. The results showed that calcium (Ca), magnesium (Mg), iron (Fe), zinc (Zn), sodium (Na) and potassium (K) acquisition by roots was diminished by O₃ exposure of the shoot. For Ca, Mg, Fe and Zn reduced uptake by the roots was combined with reduced allocation to the shoot, resulting in a decline of foliar concentrations; for Na and K, allocation to the shoot was maintained at the expense of the roots. Thus, elevated O₃ impaired both mineral uptake by the roots and partitioning of minerals between roots and shoots, but in an element specific way. Thereby, elevated O₃ affected roots and shoots differently already after one growing season. However, considerable changes in total C and N concentrations and their partitioning between different metabolic pools upon chronic O₃ exposure were not observed in either leaves or roots, except for reduced foliar lignin concentrations at 2 × ambient O₃. Significant differences in these parameters were shown between leaves and roots independent of O₃ application. The physiological consequences of the effects of chronic O₃ exposure on mineral acquisition and partitioning between leaves and roots are discussed.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

Rapid Quantification Method for Yield, Calorimetric Energy and Chlorophyll a Fluorescence Parameters in Nicotiana tabacum L. Using Vis-NIR-SWIR Hyperspectroscopy

High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll a fluorescence (ChlF) remains laborious and is of great interest to physiologists and photobiologists. We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700−750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes (i.e., yield (biomass), ΔH°area, ΔH°mass, Fv/Fm, Fv’/Fm’, ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.

Chronic ozone exposure preferentially modifies root rather than foliar metabolism of date palm (Phoenix dactylifera) saplings

In their natural environment, date palms are exposed to chronic atmospheric ozone (O₃) concentrations from local and remote sources. In order to elucidate the consequences of this exposure, date palm saplings were treated with ambient, 1.5 and 2.0 times ambient O₃ for three months in a free-air controlled exposure facility. Chronic O₃ exposure reduced carbohydrate contents in leaves and roots, but this effect was much stronger in roots. Still, sucrose contents of both organs were maintained at elevated O₃, though at different steady states. Reduced availability of carbohydrate for the Tricarboxylic acid cycle (TCA cycle) may be responsible for the observed reduced foliar contents of several amino acids, whereas malic acid accumulation in the roots indicates a reduced use of TCA cycle intermediates. Carbohydrate deficiency in roots, but not in leaves caused oxidative stress upon chronic O₃ exposure, as indicated by enhanced malonedialdehyde, H₂O₂ and oxidized glutathione contents despite elevated glutathione reductase activity. Reduced levels of phenolics and flavonoids in the roots resulted from decreased production and, therefore, do not indicate oxidative stress compensation by secondary compounds. These results show that roots of date palms are highly susceptible to chronic O₃ exposure as a consequence of carbohydrate deficiency.

Approaches to investigate crop responses to ozone pollution: from O3 -FACE to satellite-enabled modeling

Ozone (O₃ ) is a damaging air pollutant to crops. As one of the most reactive oxidants known, O₃ rapidly forms other reactive oxygen species (ROS) once it enters leaves through stomata. Those ROS in turn can cause oxidative stress, reduce photosynthesis, accelerate senescence, and decrease crop yield. To improve and adapt our feed, fuel, and food supply to rising O₃ pollution, a number of Free Air Concentration Enrichment (O₃ -FACE) facilities have been developed around the world and have studied key staple crops. In this review, we provide an overview of the FACE facilities and highlight some of the lessons learned from the last two decades of research. We discuss the differences between C₃ and C₄ crop responses to elevated O₃ , the possible trade-off between productivity and protection, genetic variation in O₃ response within and across species, and how we might leverage this observed variation for crop improvement. We also highlight the need to improve understanding of the interaction between rising O₃ pollution and other aspects of climate change, notably drought. Finally, we propose the use of globally modeled O₃ data that are available at increasing spatial and temporal resolutions to expand upon the research conducted at the limited number of global O₃ -FACE facilities.

Transient Waterlogging Events Impair Shoot and Root Physiology and Reduce Grain Yield of Durum Wheat Cultivars

Durum wheat (Triticum turgidum L. subsp. durum (Desf.) Husn) is a staple crop of the Mediterranean countries, where more frequent waterlogging events are predicted due to climate change. However, few investigations have been conducted on the physiological and agronomic responses of this crop to waterlogging. The present study provides a comprehensive evaluation of the effects of two waterlogging durations (i.e., 14 and 35 days) on two durum wheat cultivars (i.e., Svevo and Emilio Lepido). An integrated analysis of an array of physiological, biochemical, biometric, and yield parameters was performed at the end of the waterlogging events, during recovery, and at physiological maturity. Results established that effects on durum wheat varied depending on waterlogging duration. This stress imposed at tillering impaired photosynthetic activity of leaves and determined oxidative injury of the roots. The physiological damages could not be fully recovered, subsequently slowing down tiller formation and crop growth, and depressing the final grain yield. Furthermore, differences in waterlogging tolerance between cultivars were discovered. Our results demonstrate that in durum wheat, the energy maintenance, the cytosolic ion homeostasis, and the ROS control and detoxification can be useful physiological and biochemical parameters to consider for the waterlogging tolerance of genotypes, with regard to sustaining biomass production and grain yield.

Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

Date palm responses to a chronic, realistic ozone exposure in a FACE experiment

Date palms are highly economically important species in hot arid regions, which may suffer ozone (O₃) pollution equivalently to heat and water stress. However, little is known about date palm sensitivity to O₃. Therefore, to identify their resistance mechanisms against elevated O₃, physiological parameters (leaf gas exchange, chlorophyll fluorescence and leaf pigments) and biomass growth responses to realistic O₃ exposure were tested in an isoprene-emitting date palm (Phoenix dactylifera L. cv. Nabut Saif) by a Free-Air Controlled Exposure (FACE) facility with three levels of O₃ (ambient [AA, 45 ppb as 24-h average], 1.5 x AA and 2 x AA). We found a reduction of photosynthesis only at 2 x AA although some foliar traits known as early indicators of O₃ stress responded already at 1.5 x AA, such as increased dark respiration, reduced leaf pigment content, reduced maximum quantum yield of PSII, inactivation of the oxygen evolving complex of PSII and reduced performance index PI_TOT. As a result, O₃ did not affect most of the growth parameters although significant declines of root biomass occurred only at 2 x AA. The major mechanism in date palm for reducing the severity of O₃ impacts was a restriction of stomatal O₃ uptake due to low stomatal conductance and O₃-induced stomatal closure. In addition, an increased respiration in elevated O₃ may indicate an enhanced capacity of catabolizing metabolites for detoxification and repair. Interestingly, date palm produced low amounts of monoterpenes, whose emission was stimulated in 2 x AA, although isoprene emission declined at both 1.5 and 2 x AA. Our results warrant more research on a biological significance of terpenoids in plant resistance against O₃ stress.