Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine

Mode of Action of Brassinosteroids: Seed Germination and Seedling Growth and Development-One Hypothesis

Brassinosteroids, as unique plant steroid hormones that bear structural similarity to animal steroids, play a crucial role in modulating plant growth and development. These hormones have a positive impact on plant resistance and, under stressful conditions, stimulate photosynthesis and antioxidative systems (enzymatic and non-enzymatic), leading to a reduced impact of environmental cues on plant metabolism and growth. Although these plant hormones have been studied for several decades, most studies analyze the primary site of action of the brassinosteroid phytohormone, with a special emphasis on the activation of various genes (mainly nuclear) through different signaling processes that influence plant metabolism, growth, and development. This review explores another issue, the secondary influence (the so-called mode of action) of brassinosteroids on changes in growth, development, and chemical composition, as well as thermodynamic and energetic changes, mainly during the early growth of corn seedlings. The interactions of brassinosteroids with other phytohormones and physiologically active substances and the influence of these interactions on the mode of action of brassinosteroid phytohormones were also discussed. Seen from a cybernetic point of view, the approach can be labeled as "black box" or "gray box". "Black box" and "gray box" are terms for cybernetic systems, for which we know the inputs and outputs (in an energetic, biochemical, kinetic, informational, or some other sense), but whose internal structure and/or organization are completely or partially unknown to us. The findings of many researchers have indicated an important role of reactive species, such as oxygen and nitrogen reactive species, in these processes. This ultimately results in the redistribution of matter and energy from source organs to sink organs, with a decrease in Gibbs free energy from the source to sink organs. This quantitative evidence speaks of the exothermic nature and spontaneity of early (corn) seedling development and growth under the influence of 24-epibrassinolide. Based on these findings and a review of the literature on the mode of action of brassinosteroids, a hypothesis was put forward about the secondary effects of BRs on germination and the early growth of plant seedlings.

Inside-out: Synergising leaf biochemical traits with stomatal-regulated water fluxes to enhance transpiration modelling during abiotic stress

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant-environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal-based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing-based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields.

Combining proximal and remote sensing to assess 'Calatina' olive water status

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R2 values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.

Heat transfer processes in 'Shine Muscat' grapevine leaves in solar greenhouses under different irrigation treatments

The use of solar greenhouses in China is increasing because they permit environmental conditions to be controlled. Studies of the heat transfer processes in the leaves of plants cultivated within solar greenhouses are needed. Here, we studied heat transfer processes in 'Shine Muscat' grapevine leaves under moderate deficit irrigation (MDI), severe deficit irrigation (SDI), and full irrigation (FI) treatments under varying weather conditions. The stomatal conductance, leaf temperature, and transpiration rate of both shade and sun grapevine leaves were measured, and the effects of ambient temperature and relative humidity on these variables were determined. A thermal physics model of the leaves was established to explore the heat dissipation process. On sunny days, the transpiration heat transfer of sun leaves in the MDI, SDI, and FI treatments was 2.62 MJ m-2·day-1, 2.44 MJ m-2·day-1, and 3.86 MJ m-2·day-1and 0.818 MJ m-2·day-1, 0.782 MJ m-2·day-1, and 1.185 MJ m-2·day-1 on rainy days, respectively. There was a significant difference in transpiration heat transfer under fully irrigated and deficit irrigation conditions under different weather conditions. Furthermore, transpiration heat transfer accounted for 41.49 % and 25.03 % of the total heat transfer of sun leaves in the FI treatment and 33.94 % and 29.43 % of the total heat transfer of shade leaves on rainy days, respectively, indicating that relative humidity plays a key role in determining transpiration heat transfer and leaf temperature and that its effect was greater on sun leaves than on shade leaves.

Talk is cheap: rediscovering sounds made by plants

A recent study and related commentaries have raised new interest in the phenomenon of ultrasonic sound production by plants exposed to stress, especially drought. While recent technological advancements have allowed the demonstration that these sounds can propagate in the air surrounding plants, we remind readers here that research on sound production by plants is more than 100 years old. The mechanisms and patterns of sound emission from plants subjected to different stress factors are also reasonably understood, thanks to the pioneering work of John Milburn and others. By contrast, experimental evidence for a role of these sounds in plant-animal or plant-plant communication remains lacking and, at present, these ideas remain highly speculative.

Predicting the canopy conductance to water vapor of grapevines using a biophysical model in a hot and arid climate

Canopy conductance is a crucial factor in modelling plant transpiration and is highly responsive to water stress. The objective of this study is to develop a straightforward method for estimating canopy conductance (gc) in grapevines. To predict gc, this study combines stomatal conductance to water vapor (gsw) measurements from grapevine leaves, scaled to represent the canopy size by the leaf area index (LAI), with atmospheric variables, such as net solar radiation (Rn) and air vapor pressure deficit (VPD). The developed model was then validated by comparing its predictions with gc values calculated using the inverse of the Penman Monteith equation. The proposed model demonstrates its effectiveness in estimating the gc, with the highest root-mean-squared-error (RMSE=1.45x10-4 m.s-1) being lower than the minimum gc measured in the field (gc obs=0.0005 m.s-1). The results of this study reveal the significant influence of both VPD and gsw on grapevine canopy conductance.

Water Deficit Diagnosis of Winter Wheat Based on Thermal Infrared Imaging

Field experiments were conducted to analyze the effectiveness of the crop stress index (CWSI) obtained by infrared thermal imaging to indicate crop water status, and to determine the appropriate CWSI threshold range for wheat at different growth stages. The results showed that the sensitivity of plant physiological parameters to soil water was different at different growth stages. The sensitivity of stomatal conductance (Gs) and transpiration rate (Tr) to soil water was higher than that of leaf relative water content (LRWC) and photosynthetic rate (Pn). The characteristics of plant physiology and biomass (yield) at each growth stage showed that the plant production would not suffer from drought stress as long as the soil water content (SWC) was maintained above 57.0% of the field water capacity (FWC) during the jointing stage, 63.0% of the FWC during the flowering stage and 60.0% of the FWC during the filling stage. Correlation analysis showed that the correlation of CWSI with Gs, Tr and Pn was lower than that with LRWC and SWC at the jointing stage. CWSI was extremely significantly negatively correlated with SWC and LRWC (p < 0.01), but significantly negatively correlated with Gs, Tr and Pn (p < 0.05). At the flowering stage, CWSI was extremely significantly negatively correlated with all physiological and soil parameters (p < 0.01). The regression analysis showed that the CWSI of winter wheat was correlated with biomass (grain yield) in a curvilinear relationship at each growth stage. When the CWSI increased to a certain extent, the biomass and yield showed a decreasing trend with the increase in CWSI. Comprehensive analysis of all indexes showed that CWSI can be used as a decision-making index to guide the water-saving irrigation of winter wheat, as long as the CWSI threshold of plants was maintained at 0.26-0.38 during the jointing stage, 0.27-0.32 during the flowering stage and 0.30-0.36 during the filling stage, which could not only avoid the adverse effects of water stress on crop production, but also achieve the purpose of water saving.

Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach

Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants' water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).

Effects of the fundamental axes of variation in structural diversity on the forest canopy temperature in an urban area

The spatial distribution and heterogeneity of forest canopy elements reveal the fundamental dimensions of plant structure variations. Forests characterized by greater structural complexity and diversity intercept solar radiation more effectively, directly influencing the thermal environment and energy balance of the canopy. However, the axes of variation in the distribution and heterogeneity of the canopy remain largely unknown, which limits our understanding of how structural diversity responds to canopy temperature variability. Here, we derived a set of structural diversity metrics from a dataset of canopy structure measurements obtained using unmanned aerial vehicle-light detection and ranging across major forest communities in an urban area in 2021 and 2022. We also explored the key axes of structural diversity variability and tested their predictive power for canopy temperature. The results showed that: (1) most of the variability within structural diversity (83.6 % and 81.8 %) was captured by the three key axes in 2021 and 2022. The first axis was primarily driven by structural heterogeneity, representing the heterogeneity of vegetation distribution within the canopy. The second axis was primarily influenced by the interaction between height and cover/openness, indicating the vertical structure and horizontal distribution pattern of the canopy. The third axis represented the horizontal coverage and density of the canopy. (2) In both 2021 and 2022, the second axis was identified as the most influential predictor of canopy temperature, as evidenced by R2 values of 0.46 and 0.28, respectively. The model incorporating all three axes of structural diversity achieved the highest accuracy in predicting the canopy temperature for 2021 (R2 = 0.68, AIC = 81.35, ΔAIC = 0, and RMSE = 0.89). Prior research on canopy temperature prediction has overlooked the true potential of principal component axes derived from structural diversity. The findings present a novel approach for selecting structural diversity indicators for future investigation.

Planting design influences green infrastructure performance: Plant species identity and complementarity in rain gardens

Green infrastructure's capacity to mitigate urban environmental problems, like heat island effects and excessive stormwater runoff, is partially governed by its plant community. Traditionally, green infrastructure design has focused on engineered aspects, such as substrate and drainage, rather than on the properties of its living components. Since the functioning of these plant assemblages is controlled by ecophysiological processes that differ by species, the identity and relative abundance of the species used will influence green infrastructure performance. We used trait-based modeling to derive principles for the effective composition of green infrastructure plant assemblages, parameterizing our model using the vegetation and ecophysiological traits of the species within New York City rain gardens. Focusing on two plant traits that influence rain garden performance, leaf surface temperature and stomatal conductance, we simulated the cumulative temperature and transpiration for plant communities of differing species composition and diversity. The outcomes of the model demonstrate that plant species composition, species identity, selection effects, and interspecific complementarity increase green infrastructure performance in much the way biodiversity affects ecosystem functioning in natural systems. More diverse assemblages resulted in more consistent transpiration and surface temperatures, with the former showing a positive, saturating curve as diversity increased. While the dominant factors governing individual species leaf temperature were abiotic, transpiration was more influential at the community level, suggesting that plants within diverse communities may be cooler in aggregate than any individual species on its own. This implies green infrastructure should employ a variety of vegetation; particularly plants with different statures and physical attributes, such as low-growing ground covers, erect herbaceous perennials, and shrubs.

The role of soil temperature in mediterranean vineyards in a climate change context

The wine sector faces important challenges related to sustainability issues and the impact of climate change. More frequent extreme climate conditions (high temperatures coupled with severe drought periods) have become a matter of concern for the wine sector of typically dry and warm regions, such as the Mediterranean European countries. Soil is a natural resource crucial to sustaining the equilibrium of ecosystems, economic growth and people's prosperity worldwide. In viticulture, soils have a great influence on crop performance (growth, yield and berry composition) and wine quality, as the soil is a central component of the terroir. Soil temperature (ST) affects multiple physical, chemical and biological processes occurring in the soil as well as in plants growing on it. Moreover, the impact of ST is stronger in row crops such as grapevine, since it favors soil exposition to radiation and favors evapotranspiration. The role of ST on crop performance remains poorly described, especially under more extreme climatic conditions. Therefore, a better understanding of the impact of ST in vineyards (vine plants, weeds, microbiota) can help to better manage and predict vineyards' performance, plant-soil relations and soil microbiome under more extreme climate conditions. In addition, soil and plant thermal data can be integrated into Decision Support Systems (DSS) to support vineyard management. In this paper, the role of ST in Mediterranean vineyards is reviewed namely in terms of its effect on vines' ecophysiological and agronomical performance and its relation with soil properties and soil management strategies. The potential use of imaging approaches, e.g. thermography, is discussed as an alternative or complementary tool to assess ST and vertical canopy temperature profiles/gradients in vineyards. Soil management strategies to mitigate the negative impact of climate change, optimize ST variation and crop thermal microclimate (leaf and berry) are proposed and discussed, with emphasis on Mediterranean systems.

Bacillus altitudinis-Stabilized Multifarious Copper Nanoparticles Prevent Bacterial Fruit Blotch in Watermelon (Citrullus lanatus L.): Direct Pathogen Inhibition, In Planta Particles Accumulation, and Host Stomatal Immunity Modulation

The nano-enabled crop protecting agents have been emerging as a cost-effective, eco-friendly, and sustainable alternative to conventional chemical pesticides. Here, the antibacterial activity and disease-suppressive potential of biogenic copper nanoparticles (bio-CuNPs) against bacterial fruit blotch (BFB), caused by Acidovorax citrulli (Ac), in watermelon (Citrullus lanatus L.) is discussed. CuNPs are extracellularly biosynthesized using a locally isolated bacterial strain Bacillus altitudinis WM-2/2, and have spherical shapes of 29.11-78.56 nm. Various metabolites, such as alcoholic compounds, carboxylic acids, alkenes, aromatic amines, and halo compounds, stabilize bio-CuNPs. Foliar application of bio-CuNPs increases the Cu accumulation in shoots/roots (66%/27%), and promotes the growth performance of watermelon plants by improving fresh/dry weight (36%/39%), through triggering various imperative physiological and biochemical processes. Importantly, bio-CuNPs at 100 µg mL^-1 significantly suppress watermelon BFB through balancing reactive oxygen species system, improving photosynthesis capacity, and modulating stomatal immunity. Bio-CuNPs show obvious antibacterial activity against Ac by inducing oxidative stress, biofilm inhibition, and cellular integrity disruption. These findings demonstrate that bio-CuNPs can suppress watermelon BFB through direct antibacterial activity and induction of active immune response in watermelon plants, and highlight the value of this approach as a powerful tool to increase agricultural production and alleviate food insecurity.

The functional significance of the stomatal size to density relationship: Interaction with atmospheric [CO2] and role in plant physiological behaviour

The limits for stomatal conductance are set by stomatal size (SS) and density (SD). An inverse relationship between SS and SD has been observed in fossil and living plants. This has led to hypotheses proposing that the ratio of SS to SD influences the diffusion pathway for CO₂ and degree of physiological stomatal control. However, conclusive evidence supportive of a functional role of the SS-SD relationship is not evident, and patterns in SS-SD may simply reflect geometric constraints in stomatal spacing over a leaf surface. We examine published and new data to investigate the potential functional significance of the relationship between SS and SD to atmospheric [CO₂] in multiple generation adaptive responses and short-term acclamatory adjustment of stomatal morphology. Consistent patterns in SS and SD were not evident in fossil and living plants adapted to high [CO₂] over many generations. However, evolutionary adaptation to [CO₂] strongly affected SS and SD responses to elevated [CO₂], with plants adapted to the 'low' [CO₂] of the past 10 million years (Myr) showing adjustment of SS-SD, while members of the same species adapted to 'high' [CO₂] showed no response. This may suggest that SS and SD responses to future [CO₂] will likely constrain the stimulatory effect of 'CO₂-fertilisation' on photosynthesis. Angiosperms generally possessed higher densities of smaller stomata that corresponded to a greater degree of physiological stomatal control consistent with selective pressures induced by declining [CO₂] over the past 90 Myr. Atmospheric [CO₂] has likely shaped stomatal size and density relationships alongside the interaction with stomatal physiological behaviour. The rate and predicted extent of future increases in [CO₂] will have profound impacts on the selective pressures shaping SS and SD. Understanding the trade-offs involved in SS-SD and the interaction with [CO₂], will be central to the development of more productive climate resilient crops.

Automatic Crop Canopy Temperature Measurement Using a Low-Cost Image-Based Thermal Sensor: Application in a Pomegranate Orchard under a Permanent Shade Net House

Water scarcity in arid and semi-arid areas has led to the development of regulated deficit irrigation (RDI) strategies on most species of fruit trees in order to improve water productivity. For a successful implementation, these strategies require continuous feedback of the soil and crop water status. This feedback is provided by physical indicators from the soil-plant-atmosphere continuum, as is the case of the crop canopy temperature, which can be used for the indirect estimation of crop water stress. Infrared Radiometers (IRs) are considered as the reference tool for temperature-based water status monitoring in crops. Alternatively, in this paper, we assess the performance of a low-cost thermal sensor based on thermographic imaging technology for the same purpose. The thermal sensor was tested in field conditions by performing continuous measurements on pomegranate trees (Punica granatum L. 'Wonderful') and was compared with a commercial IR. A strong correlation (R² = 0.976) between the two sensors was obtained, demonstrating the suitability of the experimental thermal sensor to monitor the crop canopy temperature for irrigation management.

Extraction of 3D distribution of potato plant CWSI based on thermal infrared image and binocular stereovision system

As the largest component of crops, water has an important impact on the growth and development of crops. Timely, rapid, continuous, and non-destructive detection of crop water stress status is crucial for crop water-saving irrigation, production, and breeding. Indices based on leaf or canopy temperature acquired by thermal imaging are widely used for crop water stress diagnosis. However, most studies fail to achieve high-throughput, continuous water stress detection and mostly focus on two-dimension measurements. This study developed a low-cost three-dimension (3D) motion robotic system, which is equipped with a designed 3D imaging system to automatically collect potato plant data, including thermal and binocular RGB data. A method is developed to obtain 3D plant fusion point cloud with depth, temperature, and RGB color information using the acquired thermal and binocular RGB data. Firstly, the developed system is used to automatically collect the data of the potato plants in the scene. Secondly, the collected data was processed, and the green canopy was extracted from the color image, which is convenient for the speeded-up robust features algorithm to detect more effective matching features. Photogrammetry combined with structural similarity index was applied to calculate the optimal homography transform matrix between thermal and color images and used for image registration. Thirdly, based on the registration of the two images, 3D reconstruction was carried out using binocular stereo vision technology to generate the original 3D point cloud with temperature information. The original 3D point cloud data were further processed through canopy extraction, denoising, and k-means based temperature clustering steps to optimize the data. Finally, the crop water stress index (CWSI) of each point and average CWSI in the canopy were calculated, and its daily variation and influencing factors were analyzed in combination with environmental parameters. The developed system and the proposed method can effectively detect the water stress status of potato plants in 3D, which can provide support for analyzing the differences in the three-dimensional distribution and spatial and temporal variation patterns of CWSI in potato.

Infrared Thermal Imaging and Morpho-Physiological Indices Used for Wheat Genotypes Screening under Drought and Heat Stress

Bread wheat, one of the largest broadacre crops, often experiences various environmental stresses during critical growth stages. Terminal drought and heat stress are the primary causes of wheat yield reduction worldwide. This study aimed to determine the drought and heat stress tolerance level of a group of 46 diverse wheat genotypes procured from the Australian Grains Gene Bank, Horsham, VIC Australia. Two separate drought stress (DS) and heat stress (HS) pot experiments were conducted in separate growth chambers. Ten days after complete anthesis, drought (40 ± 3% field capacity for 14 days) and heat stress (36/22 °C for three consecutive days) were induced. A significant genotype × environment interaction was observed and explained by various morpho-physiological traits, including rapid, non-destructive infrared thermal imaging for computational water stress indices. Except for a spike length in DS and harvest index in HS, the analysis of variance showed significant differences for all the recorded traits. Results showed grains per spike, grains weight per spike, spike fertility, delayed flag leaf senescence, and cooler canopy temperature were positively associated with grain yield under DS and HS. The flag leaf senescence and chlorophyll fluorescence were used to measure each genotype's stay-green phenotype and photosystem II activity after DS and HS. This study identified the top ten best and five lowest-performing genotypes from drought and heat stress experiments based on their overall performance. Results suggest that if heat or drought adaptive traits are brought together in a single genotype, grain yield can be improved further, particularly in a rainfed cropping environment.

Modulation of GmFAD3 expression alters abiotic stress responses in soybean

KEY MESSAGE

This study focused on enhancing resilience of soybean crops to drought and salinity stresses by overexpression of GmFAD3A gene, which plays an important role in modulating membrane fluidity and ultimately influence plants response to various abiotic stresses. Fatty acid desaturases (FADs) are a class of enzymes that mediate desaturation of fatty acids by introducing double bonds. They play an important role in modulating membrane fluidity in response to various abiotic stresses. However, a comprehensive analysis of GmFAD3 in drought and salinity stress tolerance in soybean is lacking. We used bean pod mottle virus (BPMV)-based vector for achieving rapid and efficient overexpression as well as silencing of Omega-3 Fatty Acid Desaturase gene from Glycine max (GmFAD3) to assess the functional role of GmFAD3 in abiotic stress responses in soybean. Higher levels of recombinant BPMV-GmFAD3A transcripts were detected in overexpressing soybean plants. Overexpression of GmFAD3A in soybean resulted in increased levels of jasmonic acid and higher expression of GmWRKY54 as compared to mock-inoculated, vector-infected and FAD3-silenced soybean plants under drought and salinity stress conditions. The GmFAD3A-overexpressing plants showed higher levels of chlorophyll content, efficient photosystem-II, relative water content, transpiration rate, stomatal conductance, proline content and also cooler canopy under drought and salinity stress conditions as compared to mock-inoculated, vector-infected and FAD3-silenced soybean plants. Results from the current study revealed that GmFAD3A-overexpressing soybean plants exhibited tolerance to drought and salinity stresses. However, soybean plants silenced for GmFAD3 were vulnerable to drought and salinity stresses.

Challenging the water stress index concept: Thermographic assessment of Arabidopsis transpiration

Water stress may greatly limit plant functionality and growth. Stomatal closure and consequently reduced transpiration are considered as early and sensitive plant responses to drought and salinity stress. An important consequence of stomatal closure under water stress is the rise of leaf temperature (Tleaf ), yet Tleaf is not only fluctuating with stomatal closure. It is regulated by several plant parameters and environmental factors. Thermal imaging and different stress indices, incorporating actual leaf/crop temperature and reference temperatures, were developed in previous studies toward normalizing for effects unassociated to water stress on Tleaf , aiming at a more efficient water stress assessment. The concept of stress indices has not been extensively studied on the model plant Arabidopsis thaliana. Therefore, the aim of this study was to examine the different indices employed in previous studies in assessing rosette transpiration rate (E) in Arabidopsis plants grown under two different light environments and subjected to salinity. After salinity imposition, E was gravimetrically quantified, and thermal imaging was employed to quantify rosette (Trosette ) and artificial reference temperature (Twet, Tdry ). Trosette and several water stress indices were tested for their relation to E. Among the microclimatic growth conditions tested, RWSI1 ([Trosette  - Twet ]/[Tdry  - Twet ]) and RWSI2 ([Tdry  - Trosette ]/[Tdry  - Twet ]) were well linearly-related to E, irrespective of the light environment, while the sole use of either Twet or Tdry in different combinations with Trosette returned less accurate results. This study provides evidence that selected combinations of Trosette , Tdry , and Twet can be utilized to assess E under water stress irrespective of the light environment.

Thermal imagery of woodland tree canopies provides new insights into drought-induced tree mortality

Our understanding of how water dynamics determines the probability of tree mortality during drought is incomplete. Here we help address this shortcoming by coupling approaches from the disciplines of ecophysiology, geophysics and remote sensing in a woodland ecosystem undergoing protracted drying. Water uptake and use strategies varied between the dominant canopy species of the ecosystem. At one extreme were species that tightly regulate their water status, which is broadly consistent with the definition of isohydry. The higher leaf temperatures revealed by thermal imagery of these isohydric species are likely a reflection of reduced latent cooling owing to a stringent control of transpiration rate. Where silty sediments occur in the root zone, this strategy may have the effect of limiting the water sources available to these species during prolonged drought because of an insufficient hydraulic gradient for water uptake. In contrast were species that allowed their water status to fluctuate, operating in a fashion more consistent with anisohydry. For these species, latent cooling owing to relatively high transpiration rates maintained leaf temperatures near, or below, the ambient air temperature. The resulting drawdown in leaf water potential between soil and leaves in these anisohydric species may generate a sufficient hydraulic gradient to enable water uptake from silty soil during seasonal or prolonged droughts. In this way the spatial distribution of fine textured soil could indicate areas where the isohydric hydraulic control strategy is disadvantageous during prolonged droughts or where annual soil water recharge has fallen below a critical threshold.

Selection strategies to introgress water deficit tolerance derived from Solanum galapagense accession LA1141 into cultivated tomato

Crop wild relatives have been used as a source of genetic diversity for over one hundred years. The wild tomato relative Solanum galapagense accession LA1141 demonstrates the ability to tolerate deficit irrigation, making it a potential resource for crop improvement. Accessing traits from LA1141 through introgression may improve the response of cultivated tomatoes grown in water-limited environments. Canopy temperature is a proxy for physiological traits which are challenging to measure efficiently and may be related to water deficit tolerance. We optimized phenotypic evaluation based on variance partitioning and further show that objective phenotyping methods coupled with genomic prediction lead to gain under selection for water deficit tolerance. The objectives of this work were to improve phenotyping workflows for measuring canopy temperature, mapping quantitative trait loci (QTLs) from LA1141 that contribute to water deficit tolerance and comparing selection strategies. The phenotypic variance attributed to genetic causes for canopy temperature was higher when estimated from thermal images relative to estimates based on an infrared thermometer. Composite interval mapping using BC₂S₃ families, genotyped with single nucleotide polymorphisms, suggested that accession LA1141 contributed alleles that lower canopy temperature and increase plant turgor under water deficit. QTLs for lower canopy temperature were mapped to chromosomes 1 and 6 and explained between 6.6 and 9.5% of the total phenotypic variance. QTLs for higher leaf turgor were detected on chromosomes 5 and 7 and explained between 6.8 and 9.1% of the variance. We advanced tolerant BC₂S₃ families to the BC₂S₅ generation using selection indices based on phenotypic values and genomic estimated breeding values (GEBVs). Phenotypic, genomic, and combined selection strategies demonstrated gain under selection and improved performance compared to randomly advanced BC₂S₅ progenies. Leaf turgor, canopy temperature, stomatal conductance, and vapor pressure deficit (VPD) were evaluated and compared in BC₂S₅ progenies grown under deficit irrigation. Progenies co-selected for phenotypic values and GEBVs wilted less, had significantly lower canopy temperature, higher stomatal conductance, and lower VPD than randomly advanced lines. The fruit size of water deficit tolerant selections was small compared to the recurrent parent. However, lines with acceptable yield, canopy width, and quality parameters were recovered. These results suggest that we can create selection indices to improve water deficit tolerance in a recurrent parent background, and additional crossing and evaluation are warranted.

A review of remote sensing for potato traits characterization in precision agriculture

Potato is one of the most significant food crops globally due to its essential role in the human diet. The growing demand for potato, coupled with severe environmental losses caused by extensive farming activities, implies the need for better crop protection and management practices. Precision agriculture is being well recognized as the solution as it deals with the management of spatial and temporal variability to improve agricultural returns and reduce environmental impact. As the initial step in precision agriculture, the traditional methods of crop and field characterization require a large input in labor, time, and cost. Recent developments in remote sensing technologies have facilitated the process of monitoring crops and quantifying field variations. Successful applications have been witnessed in the area of precision potato farming. Thus, this review reports the current knowledge on the applications of remote sensing technologies in precision potato trait characterization. We reviewed the commonly used imaging sensors and remote sensing platforms with the comparisons of their strengths and limitations and summarized the main applications of the remote sensing technologies in potato. As a result, this review could update potato agronomists and farmers with the latest approaches and research outcomes, as well as provide a selective list for those who have the intentions to apply remote sensing technologies to characterize potato traits for precision agriculture.

Empirical Setting of the Water Stressed Baseline Increases the Uncertainty of the Crop Water Stress Index in a Humid Temperate Climate in Different Water Regimes

Water productivity of rice is imperative for global water security. Currently, water saving management techniques have been proposed and applied to rice systems. The crop water stress index (CWSI) is a major index for evaluating crop water use. The utility of the CWSI in rice in a humid temperate climate has been given little attention. Previous studies have focused on upland crops and readily available constant reference baselines, primarily the water stressed baseline (WSB), which does not inherently reflect transpiration flux. This study examined the performance of the estimated non-water stressed baseline (NWSB) and WSB for rice in a humid climate and the CWSI sensitivity under variable reference baseline scenarios in a 2-year pot trial under phytotron and field environment conditions with two rice genotypes (IRAT109 and Takanari) in a flooded (FL) and aerobic (AR) water regime. We observed that the dynamics of CWSI is dependent not only on the water regimes but could be strongly influenced by genotype sensitivity to vapor pressure deficit (VPD). A higher slope (pooled data) in the field environment (−5.68 °C kPa−1) compared to the phytotron (−3.04 °C kPa−1) reflected transpiration water loss sensitivity to VPD thresholds. Further studies with diverse rice germplasms to explore generalizability to field conditions and reformulation of reference baselines considering the VPD threshold sensitivity could prove to be significant.

Real-Time Assessment of Mandarin Crop Water Stress Index

The use of plant-based indicators and other conventional means to detect the level of water stress in crops may be challenging, due to their difficulties in automation, their arduousness, and their time-consuming nature. Non-contact and non-destructive sensing methods can be used to detect the level of water stress in plants continuously and to provide automatic sensing and controls. This research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants. The experiment used a four-by-three randomized complete block design with 80−100% FC water treatment as full field capacity and three deficit irrigation treatments at 70−75% FC, 60−65% FC, and 50−55% FC. Air temperature, canopy temperature, and vapor pressure deficits were measured and employed to deduce the empirical crop water stress index, using the Idso approach (CWSI(Idso)) as well as baseline equations to calculate non-water stress and water stressed conditions. The relative leaf water content (RLWC) of mandarin plants was also determined for the growing season. From the experiment, CWSI(Idso) and CWSI were estimated using the Workswell Wiris Agro R infrared camera (CWSIW) and showed a high correlation (R2 = 0.75 at p < 0.05) in assessing the extent of water stress in mandarin plants. The results also showed that at an altitude of 12 m above the mandarin canopy, the WWARIC was able to identify water stress using three modes (empirical, differential, and theoretical). The WWARIC’s color map feature, presented in real time, makes the camera a suitable device, as there is no need for complex computations or expert advice before determining the extent of the stress the crops are subjected to. The results prove that this novel use of the WWARIC demonstrated sufficient precision, swiftness, and intelligibility in the real-time detection of the mandarin water stress index and, accordingly, assisted in scheduling irrigation.

Pearl Millet Aquaporin Gene PgPIP2;6 Improves Abiotic Stress Tolerance in Transgenic Tobacco

Pearl millet [Pennisetum glaucum (L) R. Br.] is an important cereal crop of the semiarid tropics, which can withstand prolonged drought and heat stress. Considering an active involvement of the aquaporin (AQP) genes in water transport and desiccation tolerance besides several basic functions, their potential role in abiotic stress tolerance was systematically characterized and functionally validated. A total of 34 AQP genes from P. glaucum were identified and categorized into four subfamilies, viz., plasma membrane intrinsic proteins (PIPs), tonoplast intrinsic proteins (TIPs), nodulin-26-like intrinsic proteins (NIPs), and small basic intrinsic proteins (SIPs). Sequence analysis revealed that PgAQPs have conserved characters of AQP genes with a closer relationship to sorghum. The PgAQPs were expressed differentially under high vapor pressure deficit (VPD) and progressive drought stresses where the PgPIP2;6 gene showed significant expression under high VPD and drought stress. Transgenic tobacco plants were developed by heterologous expression of the PgPIP2;6 gene and functionally characterized under different abiotic stresses to further unravel their role. Transgenic tobacco plants in the T2 generations displayed restricted transpiration and low root exudation rates in low- and high-VPD conditions. Under progressive drought stress, wild-type (WT) plants showed a quick or faster decline of soil moisture than transgenics. While under heat stress, PgPIP2;6 transgenics showed better adaptation to heat (40°C) with high canopy temperature depression (CTD) and low transpiration; under low-temperature stress, they displayed lower transpiration than their non-transgenic counterparts. Cumulatively, lower transpiration rate (Tr), low root exudation rate, declined transpiration, elevated CTD, and lower transpiration indicate that PgPIP2;6 plays a role under abiotic stress tolerance. Since the PgPIP2;6 transgenic plants exhibited better adaptation against major abiotic stresses such as drought, high VPD, heat, and cold stresses by virtue of enhanced transpiration efficiency, it has the potential to engineer abiotic stress tolerance for sustained growth and productivity of crops.

Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With qDTY2.1 and qDTY3.1, Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping

Reproductive stage drought stress (RSDS) is a major challenge in rice production worldwide. Cultivar development with drought tolerance has been slow due to the lack of precise high throughput phenotyping tools to quantify drought stress-induced effects. Most of the available techniques are based on destructive sampling and do not assess the progress of the plant's response to drought. In this study, we have used state-of-the-art image-based phenotyping in a phenomics platform that offers a controlled environment, non-invasive phenotyping, high accuracy, speed, and continuity. In rice, several quantitative trait loci (QTLs) which govern grain yield under drought determine RSDS tolerance. Among these, qDTY2.1 and qDTY3.1 were used for marker-assisted breeding. A set of 35 near-isogenic lines (NILs), introgressed with these QTLs in the popular variety, Pusa 44 were used to assess the efficiency of image-based phenotyping for RSDS tolerance. NILs offered the most reliable contrast since they differed from Pusa 44 only for the QTLs. Four traits, namely, the projected shoot area (PSA), water use (WU), transpiration rate (TR), and red-green-blue (RGB) and near-infrared (NIR) values were used. Differential temporal responses could be seen under drought, but not under unstressed conditions. NILs showed significant level of RSDS tolerance as compared to Pusa 44. Among the traits, PSA showed strong association with yield (80%) as well as with two drought tolerances indices, stress susceptibility index (SSI) and tolerance index (TOL), establishing its ability in identifying the best drought tolerant NILs. The results revealed that the introgression of QTLs helped minimize the mean WU per unit of biomass per day, suggesting the potential role of these QTLs in improving WU-efficiency (WUE). We identified 11 NILs based on phenomics traits as well as performance under imposed drought in the field. The study emphasizes the use of phenomics traits as selection criteria for RSDS tolerance at an early stage, and is the first report of using phenomics parameters in RSDS selection in rice.

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.

Detecting Plant Stress Using Thermal and Optical Imagery From an Unoccupied Aerial Vehicle

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green-red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.

Potential Phenotyping Methodologies to Assess Inter- and Intravarietal Variability and to Select Grapevine Genotypes Tolerant to Abiotic Stress

Plant phenotyping is an emerging science that combines multiple methodologies and protocols to measure plant traits (e.g., growth, morphology, architecture, function, and composition) at multiple scales of organization. Manual phenotyping remains as a major bottleneck to the advance of plant and crop breeding. Such constraint fostered the development of high throughput plant phenotyping (HTPP), which is largely based on imaging approaches and automatized data retrieval and processing. Field phenotyping still poses major challenges and the progress of HTPP for field conditions can be relevant to support selection and breeding of grapevine. The aim of this review is to discuss potential and current methods to improve field phenotyping of grapevine to support characterization of inter- and intravarietal diversity. Vitis vinifera has a large genetic diversity that needs characterization, and the availability of methods to support selection of plant material (polyclonal or clonal) able to withstand abiotic stress is paramount. Besides being time consuming, complex and expensive, field experiments are also affected by heterogeneous and uncontrolled climate and soil conditions, mostly due to the large areas of the trials and to the high number of traits to be observed in a number of individuals ranging from hundreds to thousands. Therefore, adequate field experimental design and data gathering methodologies are crucial to obtain reliable data. Some of the major challenges posed to grapevine selection programs for tolerance to water and heat stress are described herein. Useful traits for selection and related field phenotyping methodologies are described and their adequacy for large scale screening is discussed.

A Review of Crop Water Stress Assessment Using Remote Sensing

Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved.

Use of Thermal Imaging to Assess Water Status in Citrus Plants in Greenhouses

The direct examination of plant canopy temperature can assist in optimizing citrus irrigation management in greenhouses. This study aimed to develop a method to measure canopy temperature using thermal imaging in one-year-old citrus plants in a greenhouse to identify plants with water stress and verify its potential to be used as a tool to assess citrus water status. The experiment was conducted for 48 days (27 November 2019 to 13 January 2020). We evaluated the influence of five levels of irrigation on two citrus species (‘Red Ruby’ grapefruit (Citrus paradisi) and ‘Valencia’ sweet orange (Citrus sinensis (L.) Osbeck)). Images were taken using a portable thermal camera and analyzed using open-source software. We determined canopy temperature, leaf photosynthesis and transpiration, and plant biomass. The results indicated a positive relationship between the amount of water applied and the temperature response of plants exposed to different water levels. Grapefruit and sweet orange plants that received less water and were submitted to water restrictions showed higher canopy temperatures than the air (up to 6 °C). The thermal images easily identified water-stressed plants. Our proof-of-concept study allowed quickly obtaining the canopy temperature using readily available equipment and can be used as a tool to assess citrus water status in one-year-old citrus plants in greenhouses and perhaps in commercial operations with mature trees in the field after specific experimentation. This technique, coupled with an automated system, can be used for irrigation scheduling. Thus, setting up a limit temperature is necessary to start the irrigation system and set the irrigation time based on the soil water content. To use this process on a large scale, it is necessary to apply an automation routine to process the thermal images in real time and remove the weeds from the background to determine the canopy temperature.

Identification for surrogate drought tolerance in maize inbred lines utilizing high-throughput phenomics approach

Screening for drought tolerance requires precise techniques like phonemics, which is an emerging science aimed at non-destructive methods allowing large-scale screening of genotypes. Large-scale screening complements genomic efforts to identify genes relevant for crop improvement. Thirty maize inbred lines from various sources (exotic and indigenous) maintained at Dryland Agriculture Research Station were used in the current study. In the automated plant transport and imaging systems (LemnaTec Scanalyzer system for large plants), top and side view images were taken of the VIS (visible) and NIR (near infrared) range of the light spectrum to capture phenes. All images were obtained with a thermal imager. All sensors were used to collect images one day after shifting the pots from the greenhouse for 11 days. Image processing was done using pre-processing, segmentation and flowered by features' extraction. Different surrogate traits such as pixel area, plant aspect ratio, convex hull ratio and calliper length were estimated. A strong association was found between canopy temperature and above ground biomass under stress conditions. Promising lines in different surrogates will be utilized in breeding programmes to develop mapping populations for traits of interest related to drought resilience, in terms of improved tissue water status and mapping of genes/QTLs for drought traits.

Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot

There is a growing need to provide support and applicable tools to farmers and the agro-industry in order to move from their traditional water status monitoring and high-water-demand cropping and irrigation practices to modern, more precise, reduced-demand systems and technologies. In precision viticulture, very few approaches with ground robots have served as moving platforms for carrying non-invasive sensors to deliver field maps that help growers in decision making. The goal of this work is to demonstrate the capability of the VineScout (developed in the context of a H2020 EU project), a ground robot designed to assess and map vineyard water status using thermal infrared radiometry in commercial vineyards. The trials were carried out in Douro Superior (Portugal) under different irrigation treatments during seasons 2019 and 2020. Grapevines of Vitis vinifera L. Touriga Nacional were monitored at different timings of the day using leaf water potential (Ψl) as reference indicators of plant water status. Grapevines’ canopy temperature (Tc) values, recorded with an infrared radiometer, as well as data acquired with an environmental sensor (Tair, RH, and AP) and NDVI measurements collected with a multispectral sensor were automatically saved in the computer of the autonomous robot to assess and map the spatial variability of a commercial vineyard water status. Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.57 in the morning time and a r2cv of 0.42 in the midday. The root mean square error of cross-validation (RMSEcv) was 0.191 MPa and 0.139 MPa at morning and midday, respectively. Spatial–temporal variation maps were developed at two different times of the day to illustrate the capability to monitor the grapevine water status in order to reduce the consumption of water, implementing appropriate irrigation strategies and increase the efficiency in the real time vineyard management. The promising outcomes gathered with the VineScout using different sensors based on thermography, multispectral imaging and environmental data disclose the need for further studies considering new variables related with the plant water status, and more grapevine cultivars, seasons and locations to improve the accuracy, robustness and reliability of the predictive models, in the context of precision and sustainable viticulture.

Thermal Analysis of Stomatal Response under Salinity and High Light

A non-destructive thermal imaging method was used to study the stomatal response of salt-treated Arabidopsis thaliana plants to excessive light. The plants were exposed to different levels of salt concentrations (0, 75, 150, and 220 mM NaCl). Time-dependent thermograms showed the changes in the temperature distribution over the lamina and provided new insights into the acute light-induced temporary response of Arabidopsis under short-term salinity. The initial response of plants, which was associated with stomatal aperture, revealed an exponential growth in temperature kinetics. Using a single-exponential function, we estimated the time constants of thermal courses of plants exposed to acute high light. The saline-induced impairment in stomatal movement caused the reduced stomatal conductance and transpiration rate. Limited transpiration of NaCl-treated plants resulted in an increased rosette temperature and decreased thermal time constants as compared to the controls. The net CO₂ assimilation rate decreased for plants exposed to 220 mM NaCl; in the case of 75 mM NaCl treatment, an increase was observed. A significant decline in the maximal quantum yield of photosystem II under excessive light was noticeable for the control and NaCl-treated plants. This study provides evidence that thermal imaging as a highly sensitive technique may be useful for analyzing the stomatal aperture and movement under dynamic environmental conditions.

LED and HPS Supplementary Light Differentially Affect Gas Exchange in Tomato Leaves

Using light emitting diodes (LED) instead of conventionally used high pressure sodium (HPS) lamps as a supplemental light source in greenhouses results in a higher efficacy (µmol light per J electricity) and makes it possible to customize the light spectrum. To explore the effects of LED and HPS on gas exchange, thermal relations, photosynthesis, and water status of young tomato plants, seven genotypes were grown in a greenhouse under LED (95% red, 5% blue) or HPS lamps in four experiments differing in the fraction of lamp light over natural light. HPS lights emit a broader spectrum of red (40%), green–yellow (50%), blue (5%), and far-red (5%) and a substantial amount of infrared radiation (heat). Young tomato plants grown under LED showed lower leaf temperature and higher stomatal density, stomatal conductance (g_s) and transpiration rate (E) than plants grown under HPS; this may be due to the different supplemental light spectrum. The young plants grown under LED tended to have increased photosynthetic capacity. Furthermore, the water stress indices CWSI and I_G, which were obtained using thermal imaging, were positively correlated with gas exchange-derived g_s and E, putting forward the use of thermal imaging for the phenotyping of transpiration. Under LED light, photosynthetic gas exchange was generally increased, which agreed with the water stress indices. The extent of this increase was genotype-dependent. All differences between LED and HPS were smaller in the experiments where the fraction of lamp light over natural light was smaller.

Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens

With water resources constantly becoming scarcer, and 70% of freshwater used for the agriculture sector, there is a growing need for innovative methods to increase water use efficiency (WUE) of food production systems and provide nutrient-dense food to an increasing population. Sensor technology has recently been introduced to the horticulture industry to increase resource use efficiency and minimize the environmental impacts of excessive water use. Identifying the effects of irrigation levels on crop performance is crucial for the success of sensor-based water management. This research aimed to optimize WUE in a soilless microgreen production system through identification of an optimal irrigation level using a sensor that could facilitate the development of a more efficient, low-cost automated irrigation system. A dielectric moisture sensor was implemented to monitor water levels at five irrigation setpoints: 7.5, 17.5, 25, 30, and 35 percent of the effective volume of the container (EVC) during a 14-day growth cycle. To validate the sensor performance, the same irrigation levels were applied to a parallel trial, without sensor, and water levels were monitored gravimetrically. Plant water status and stress reaction were evaluated using infrared thermal imaging, and the accumulation of osmolytes (proline) was determined. Results showed that, proline concentration, canopy temperature (Tc), canopy temperature depression (CTD), and crop water stress index (CWSI) increased at 7.5% EVC in both sensor-based and gravimetric treatments, and infrared index (Ig) and fresh yield decreased. The dielectric moisture sensor was effective in increasing WUE. The irrigation level of 17.5% EVC was found to be optimal. It resulted in a WUE of 88 g/L, an improvement of 30% over the gravimetric method at the same irrigation level. Furthermore, fresh yield increased by 11.5%. The outcome of this study could contribute to the automation of precision irrigation in hydroponically grown microgreens.

Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle

Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities' green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016-2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities.

Thermal Imaging for Plant Stress Detection and Phenotyping

In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.

High-throughput phenotyping platform for analyzing drought tolerance in rice

A new imaging platform was constructed to analyze drought-tolerant traits of rice. Rice was used to quantify drought phenotypes through image-based parameters and analyzing tools. Climate change has increased the frequency and severity of drought, which limits crop production worldwide. Developing new cultivars with increased drought tolerance and short breeding cycles is critical. However, achieving this goal requires phenotyping a large number of breeding populations in a short time and in an accurate manner. Novel cutting-edge technologies such as those based on remote sensors are being applied to solve this problem. In this study, new technologies were applied to obtain and analyze imaging data and establish efficient screening platforms for drought tolerance in rice using the drought-tolerant mutant osphyb. Red-Green-Blue images were used to predict plant area, color, and compactness. Near-infrared imaging was used to determine the water content of rice, infrared was used to assess plant temperature, and fluorescence was used to examine photosynthesis efficiency. DroughtSpotter technology was used to determine water use efficiency, plant water loss rate, and transpiration rate. The results indicate that these methods can detect the difference between tolerant and susceptible plants, suggesting their value as high-throughput phenotyping methods for short breeding cycles as well as for functional genetic studies of tolerance to drought stress.

The Application of Ground-Based and Satellite Remote Sensing for Estimation of Bio-Physiological Parameters of Wheat Grown Under Different Water Regimes

Remote sensing technologies have been widely studied for the estimation of crop biometric and physiological parameters. The number of sensors and data acquisition methods have been increasing, and their evaluation is becoming a necessity. The aim of this study was to assess the performance of two remote sensing data for describing the variations of biometric and physiological parameters of durum wheat grown under different water regimes (rainfed, 50% and 100% of irrigation requirements). The experimentation was carried out in Policoro (Southern Italy) for two growing seasons. The Landsat 8 and Sentinel-2 images and radiometric ground-based data were acquired regularly during the growing season with plant biometric (leaf area index and dry aboveground biomass) and physiological (stomatal conductance, net assimilation, and transpiration rate) parameters. Water deficit index was closely related to plant water status and crop physiological parameters. The enhanced vegetation index showed slightly better performance than the normalized difference vegetation index when plotted against the leaf area index with R2 = 0.73. The overall results indicated that the ground-based vegetation indices were in good agreement with the satellite-based indices. The main constraint for effective application of satellite-based indices remains the presence of clouds during the acquisition time, which is particularly relevant for winter–spring crops. Therefore, the integration of remote sensing and field data might be needed to optimize plant response under specific growing conditions and to enhance agricultural production.

Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case

Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 < 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra.

Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance

The midday stem water potential (Ψs) and stomatal conductance (gs) have been traditionally used to monitor the water status of cherry trees (Prunus avium L.). Due to the complexity of direct measurement, the use of infrared thermography has been proposed as an alternative. This study compares Ψs and gs against crop water stress indexes (CWSI) calculated from thermal infrared (TIR) data from high-resolution (HR) and low-resolution (LR) cameras for two cherry tree cultivars: 'Regina' and 'Sweetheart'. For this purpose, a water stress-recovery cycle experiment was carried out at the post-harvest period in a commercial drip-irrigated cherry tree orchard under three irrigation treatments based on Ψs levels. The water status of trees was measured weekly using Ψs, gs, and compared to CWSIs, computed from both thermal cameras. Results showed that the accuracy in the estimation of CWSIs was not statistically significant when comparing both cameras for the representation of Ψs and gs in both cultivars. The performance of all evaluated physiological indicators presented similar trends for both cultivars, and the averaged differences between CWSI's from both cameras were 11 ± 0.27%. However, these CWSI's were not able to detect differences among irrigation treatments as compared to Ψs and gs.

A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments

Thermal infrared cameras provide unique information on surface temperature that can benefit a range of environmental, industrial and agricultural applications. However, the use of uncooled thermal cameras for field and unmanned aerial vehicle (UAV) based data collection is often hampered by vignette effects, sensor drift, ambient temperature influences and measurement bias. Here, we develop and apply an ambient temperature-dependent radiometric calibration function that is evaluated against three thermal infrared sensors (Apogee SI-11(Apogee Electronics, Santa Monica, CA, USA), FLIR A655sc (FLIR Systems, Wilsonville, OR, USA), TeAx 640 (TeAx Technology, Wilnsdorf, Germany)). Upon calibration, all systems demonstrated significant improvement in measured surface temperatures when compared against a temperature modulated black body target. The laboratory calibration process used a series of calibrated resistance temperature detectors to measure the temperature of a black body at different ambient temperatures to derive calibration equations for the thermal data acquired by the three sensors. As a point-collecting device, the Apogee sensor was corrected for sensor bias and ambient temperature influences. For the 2D thermal cameras, each pixel was calibrated independently, with results showing that measurement bias and vignette effects were greatly reduced for the FLIR A655sc (from a root mean squared error (RMSE) of 6.219 to 0.815 degrees Celsius (℃)) and TeAx 640 (from an RMSE of 3.438 to 1.013 ℃) cameras. This relatively straightforward approach for the radiometric calibration of infrared thermal sensors can enable more accurate surface temperature retrievals to support field and UAV-based data collection efforts.

Maize, sorghum, and pearl millet have highly contrasting species strategies to adapt to water stress and climate change-like conditions

This study compared maize, sorghum and pearl-millet, leading C₄ cereals, for the transpiration rate (TR) response to increasing atmospheric and soil water stress. The TR response to transiently increasing VPD (0.9-4.1 kPa) and the transpiration and leaf area expansion response to progressive soil drying were measured in controlled conditions at early vegetative stage in 10-16 genotypes of each species grown in moderate or high vapor pressure deficit (VPD) conditions. Maize grown under moderate VPD conditions restricted TR under high VPD, but not sorghum and pearl millet. By contrast, when grown under high VPD, all species increased TR upon increasing VPD, suggesting a loss of TR responsiveness. Sorghum and pearl-millet grown under high VPD reduced leaf area, but not maize. Upon progressive soil drying, maize reduced transpiration at higher soil moisture than sorghum and pearl millet, especially under high VPD, and leaf area expansion declined at similar or lower soil moisture than transpiration in maize and sorghum. It is concluded that maize conserves water by restricting transpiration upon increasing VPD and under higher soil moisture than sorghum and millet, giving maize significantly higher TE, whereas sorghum and pearl millet rely mostly on reduced leaf area and somewhat on transpiration restriction.

Prospective impacts of oil spills on floodplain vegetation: Both crude oil and diluted bitumen increase foliar temperatures, senescence and abscission in three cottonwood (Populus) species

Oil pipelines are vulnerable at river crossings since floods can expose and rupture pipes, releasing oil that floats and coats floodplain vegetation. This study investigated the consequences of oil coatings on leaves of cottonwoods (riparian poplars), the predominant trees in floodplain woodlands around the Northern Hemisphere. The study compared conventional crude oil (CO) versus diluted bitumen (dilbit, DB), heavy oil originating from the Alberta oil sands; with petroleum jelly (PJ) as a reference. The treatments increased leaf surface temperatures (T_leaf) in narrowleaf and plains cottonwoods (Populus angustifolia, P. deltoides) and balsam poplars (P. balsamifera) (Control = 21.8°C, PJ = 23.7°C; CO = 26.2°C; DB = 28.1°C; T_air = 25°C). The leaf warming followed stomatal occlusion from the foliar coating, which would reduce transpiration and evaporative cooling, combined with increased solar warming with the darker oils. T_leaf varied across the three cottonwood species, with cooler, narrow, narrowleaf cottonwood leaves; intermediate plains cottonwood leaves; and warmer, darker, balsam poplar leaves (average T_leaf: narrowleaf = 23.8°C, plains = 24.3°C, and balsam = 26.7°C), with similar warming in each species following the different treatments. Across species and treatments, T_leaf was tightly correlated with foliar condition, which assessed turgor versus wilting of leaf blades and petioles, along with leaf necrosis and senescence (r² = 0.980, narrowleaf; 0.998, plains; 0.852, balsam). This tight association indicates validity of both T_leaf and foliar condition as diagnostic measures. Crude oil and dilbit had similar foliar impacts, and for both, leaf abscission occurred within 2 to 3 weeks. Consequently, following an oil spill, remediation should commence quickly but extending vegetation removal beyond a few weeks would have limited benefit since the contaminated leaves would have abscised.

Hoechst-tagged Fluorescein Diacetate for the Fluorescence Imaging-based Assessment of Stomatal Dynamics in Arabidopsis thaliana

In plants, stomata regulate water loss through transpiration for plant growth and survival in response to various environmental stressors; and simple methods to assess stomatal dynamics are needed for physiological studies. Herein, we report a fluorescence-imaging-based method using fluorescein diacetate tagged with Hoechst 33342, a nuclear staining chemical probe (HoeAc₂Fl) for the qualitative assessment of stomatal dynamics. In our method, the stomatal movement is inferred by simple monitoring of the fluorescence intensity in the nucleus of the stomata.

A New Low-Cost Device Based on Thermal Infrared Sensors for Olive Tree Canopy Temperature Measurement and Water Status Monitoring

In recent years, many olive orchards, which are a major crop in the Mediterranean basin, have been converted into intensive or super high-density hedgerow systems. This configuration is more efficient in terms of yield per hectare, but at the same time the water requirements are higher than in traditional grove arrangements. Moreover, irrigation regulations have a high environmental (through water use optimization) impact and influence on crop quality and yield. The mapping of (spatio-temporal) variability with conventional water stress assessment methods is impractical due to time and labor constraints, which often involve staff training. To address this problem, this work presents the development of a new low-cost device based on a thermal infrared (IR) sensor for the measurement of olive tree canopy temperature and monitoring of water status. The performance of the developed device was compared to a commercial thermal camera. Furthermore, the proposed device was evaluated in a commercially managed olive orchard, where two different irrigation treatments were established: a full irrigation treatment (FI) and a regulated deficit irrigation (RDC), aimed at covering 100% and 50% of crop evapotranspiration (ETc), respectively. Predawn leaf water potential (ΨPD) and stomatal conductance (gs), two widely accepted indicators for crop water status, were regressed to the measured canopy temperature. The results were promising, reaching a coefficient of determination R2 ≥ 0.80. On the other hand, the crop water stress index (CWSI) was also calculated, resulting in a coefficient of determination R2 ≥ 0.79. The outcomes provided by the developed device support its suitability for fast, low-cost, and reliable estimation of an olive orchard’s water status, even suppressing the need for supervised acquisition of reference temperatures. The newly developed device can be used for water management, reducing water usage, and for overall improvements to olive orchard management.

A Comparison of the Variable J and Carbon-Isotopic Composition of Sugars Methods to Assess Mesophyll Conductance from the Leaf to the Canopy Scale in Drought-Stressed Cherry

Conductance of CO2 across the mesophyll (Gm) frequently constrains photosynthesis (PN) but cannot be measured directly. We examined Gm of cherry (Prunus avium L.) subjected to severe drought using the variable J method and carbon-isotopic composition (δ13C) of sugars from the centre of the leaf, the leaf petiole sap, and sap from the largest branch. Depending upon the location of the plant from which sugars are sampled, Gm may be estimated over scales ranging from a portion of the leaf to a canopy of leaves. Both the variable J and δ13C of sugars methods showed a reduction in Gm as soil water availability declined. The δ13C of sugars further from the source of their synthesis within the leaf did not correspond as closely to the diffusive and C-isotopic discrimination conditions reflected in the instantaneous measurement of gas exchange and chlorophyll-fluorescence utilised by the variable J approach. Post-photosynthetic fractionation processes and/or the release of sugars from stored carbohydrates (previously fixed under different environmental and C-isotopic discrimination conditions) may reduce the efficacy of the δ13C of sugars from leaf petiole and branch sap in estimating Gm in a short-term study. Consideration should be given to the spatial and temporal scales at which Gm is under observation in any experimental analysis.

Maize Open-Pollinated Populations Physiological Improvement: Validating Tools for Drought Response Participatory Selection

Participatory selection—exploiting specific adaptation traits to target environments—helps to guarantees yield stability in a changing climate, in particular under low-input or organic production. The purpose of the present study was to identify reliable, low-cost, fast and easy-to-use tools to complement traditional selection for an effective participatory improvement of maize populations for drought resistance/tolerance. The morphological and eco-physiological responses to progressive water deprivation of four maize open-pollinated populations were assessed in both controlled and field conditions. Thermography and Chl a fluorescence, validated by gas exchange indicated that the best performing populations under water-deficit conditions were ‘Fandango’ and to a less extent ‘Pigarro’ (both from participatory breeding). These populations showed high yield potential under optimal and reduced watering. Under moderate water stress, ‘Bilhó’, originating from an altitude of 800 m, is one of the most resilient populations. The experiments under chamber conditions confirmed the existence of genetic variability within ‘Pigarro’ and ‘Fandango’ for drought response relevant for future populations breeding. Based on the easiness to score and population discriminatory power, the performance index (PIABS) emerges as an integrative phenotyping tool to use as a refinement of the common participatory maize selection especially under moderate water deprivation.

Estimating 3D Chlorophyll Content Distribution of Trees Using an Image Fusion Method Between 2D Camera and 3D Portable Scanning Lidar

An image fusion method has been proposed for plant images taken using a two-dimensional (2D) camera and three-dimensional (3D) portable lidar for obtaining a 3D distribution of physiological and biochemical plant properties. In this method, a 2D multispectral camera with five bands (475–840 nm) and a 3D high-resolution portable scanning lidar were applied to three sets of sample trees. After producing vegetation index (VI) images from multispectral images, 3D point cloud lidar data were projected onto the 2D plane based on perspective projection, keeping the depth information of each of the lidar points. The VI images were 2D registered to the lidar projected image based on the projective transformation and VI 3D point cloud images were reconstructed based on the depth information. Based on the relationship between the VI values and chlorophyll contents taken by a soil and plant analysis development (SPAD)-502 plus chlorophyll meter, 3D distribution images of the chlorophyll contents were produced. Similarly, a thermal 3D image for a sample was also produced. The resultant chlorophyll distribution images offered vertical and horizontal distributions, and those for each orientation for each sample, showing the spatial variability of the distribution and the difference between the samples.