Estimating stomatal conductance with thermal imagery

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.

Alternative method for determining leaf CO2 assimilation without gas exchange measurements: Performance, comparison and sensitivity analysis

We present an alternative method to determine leaf CO2 assimilation rate (An ), eliminating the need for gas exchange measurements in proximal and remote sensing. This method combines the Farquhar-von Caemmerer-Berry photosynthesis model with mechanistic light reaction (MLR) theory and leaf energy balance (EB) analysis. The MLR theory estimates the actual electron transport rate (J) by leveraging chlorophyll fluorescence via pulse amplitude-modulated fluorometry for proximal sensing or sun-induced chlorophyll fluorescence measurements for remote sensing, along with spectral reflectance. The EB equation is used to directly estimate stomatal conductance from leaf temperature. In wheat and soybean, the MLR-EB model successfully estimated An variations, including midday depression, under various environmental and phenological conditions. Sensitivity analysis revealed that the leaf boundary layer conductance (gb ) played an equal, if not more, crucial role compared to the variables for J. This was primarily caused by the indirect influence of gb through the EB equation rather than its direct impact on convective CO2 exchange on the leaf. Although the MLR-EB model requires an accurate estimation of gb , it can potentially reduce uncertainties and enhance applicability in photosynthesis assessment when gas exchange measurements are unavailable.

Using Infrared Thermography for High-Throughput Plant Phenotyping

Infrared thermography offers a rapid, noninvasive method for measuring plant temperature, which provides a proxy for stomatal conductance and plant water status and can therefore be used as an index for plant stress. Thermal imaging can provide an efficient method for high-throughput screening of large numbers of plants. This chapter provides guidelines for using thermal imaging equipment and illustrative methodologies, coupled with essential considerations, to access plant physiological processes.

Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform

BACKGROUND

Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated.

RESULTS

The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines.

CONCLUSION

Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.

Challenges in studying water fluxes within the soil-plant-atmosphere continuum: A tracer-based perspective on pathways to progress

Tracing and quantifying water fluxes in the hydrological cycle is crucial for understanding the current state of ecohydrological systems and their vulnerability to environmental change. Especially the interface between ecosystems and the atmosphere that is strongly mediated by plants is important to meaningfully describe ecohydrological system functioning. Many of the dynamic interactions generated by water fluxes between soil, plant and the atmosphere are not well understood, which is partly due to a lack of interdisciplinary research. This opinion paper reflects the outcome of a discussion among hydrologists, plant ecophysiologists and soil scientists on open questions and new opportunities for collaborative research on the topic "water fluxes in the soil-plant-atmosphere continuum" especially focusing on environmental and artificial tracers. We emphasize the need for a multi-scale experimental approach, where a hypothesis is tested at multiple spatial scales and under diverse environmental conditions to better describe the small-scale processes (i.e., causes) that lead to large-scale patterns of ecosystem functioning (i.e., consequences). Novel in-situ, high-frequency measurement techniques offer the opportunity to sample data at a high spatial and temporal resolution needed to understand the underlying processes. We advocate for a combination of long-term natural abundance measurements and event-based approaches. Multiple environmental and artificial tracers, such as stable isotopes, and a suite of experimental and analytical approaches should be combined to complement information gained by different methods. Virtual experiments using process-based models should be used to inform sampling campaigns and field experiments, e.g., to improve experimental designs and to simulate experimental outcomes. On the other hand, experimental data are a pre-requisite to improve our currently incomplete models. Interdisciplinary collaboration will help to overcome research gaps that overlap across different earth system science fields and help to generate a more holistic view of water fluxes between soil, plant and atmosphere in diverse ecosystems.

Wearable Sensor: An Emerging Data Collection Tool for Plant Phenotyping

The advancement of plant phenomics by using optical imaging-based phenotyping techniques has markedly improved breeding and crop management. However, there remains a challenge in increasing the spatial resolution and accuracy due to their noncontact measurement mode. Wearable sensors, an emerging data collection tool, present a promising solution to address these challenges. By using a contact measurement mode, wearable sensors enable in-situ monitoring of plant phenotypes and their surrounding environments. Although a few pioneering works have been reported in monitoring plant growth and microclimate, the utilization of wearable sensors in plant phenotyping has yet reach its full potential. This review aims to systematically examine the progress of wearable sensors in monitoring plant phenotypes and the environment from an interdisciplinary perspective, including materials science, signal communication, manufacturing technology, and plant physiology. Additionally, this review discusses the challenges and future directions of wearable sensors in the field of plant phenotyping.

Emerging Frontiers in Nanotechnology for Precision Agriculture: Advancements, Hurdles and Prospects

This review article provides an extensive overview of the emerging frontiers of nanotechnology in precision agriculture, highlighting recent advancements, hurdles, and prospects. The benefits of nanotechnology in this field include the development of advanced nanomaterials for enhanced seed germination and micronutrient supply, along with the alleviation of biotic and abiotic stress. Further, nanotechnology-based fertilizers and pesticides can be delivered in lower dosages, which reduces environmental impacts and human health hazards. Another significant advantage lies in introducing cutting-edge nanodiagnostic systems and nanobiosensors that monitor soil quality parameters, plant diseases, and stress, all of which are critical for precision agriculture. Additionally, this technology has demonstrated potential in reducing agro-waste, synthesizing high-value products, and using methods and devices for tagging, monitoring, and tracking agroproducts. Alongside these developments, cloud computing and smartphone-based biosensors have emerged as crucial data collection and analysis tools. Finally, this review delves into the economic, legal, social, and risk implications of nanotechnology in agriculture, which must be thoroughly examined for the technology’s widespread adoption.

A simple system for phenotyping of plant transpiration and stomatal conductance response to drought

Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (g_s) of a large number of individual plants. Several methods to measure E and g_s exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min^-1) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30-50 % and back), we showed that leaf temperature as measured in our system correlated well with E and g_s measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and g_s. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.

Development of an accurate low cost NDVI imaging system for assessing plant health

BACKGROUND

Spectral imaging is a key method for high throughput phenotyping that can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI), uses specific wavelengths to compare crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use platforms will generalise its use for precision agriculture. We describe a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets were used to calibrate images into values of reflectance, that are then used to calculated NDVI with improved accuracy compared with systems that use single references/standards.

RESULTS

NDVIpi imagery showed strong performance against standard spectrometry, as an accurate measurement of leaf NDVI. The NDVIpi was also compared to a relatively more expensive commercial camera (Micasense RedEdge), with both cameras having a comparable performance in measuring NDVI. There were differences between the NDVI values of the NDVIpi and the RedEdge, which could be attributed to the measurement of different wavelengths for use in the NDVI calculation by each camera. Subsequently, the wavelengths used by the NDVIpi show greater sensitivity to changes in chlorophyll content than the RedEdge.

CONCLUSION

We present a methodology for a Raspberry Pi based NDVI imaging system that utilizes low cost, off-the-shelf components, and a robust multi-reference calibration protocols that provides accurate NDVI measurements. When compared with a commercial system, comparable NDVI values were obtained, despite the fact that our system was a fraction of the cost. Our results also highlight the importance of the choice of red wavelengths in the calculation of NDVI, which resulted in differences in sensitivity between camera systems.

CART model to classify the drought status of diverse tomato genotypes by VPD, air temperature, and leaf-air temperature difference

Regular water management is crucial for the cultivation of tomato (Solanum lycopersicum L.). Inadequate irrigation leads to water stress and a reduction in tomato yield and quality. Therefore, it is important to develop an efficient classification method of the drought status of tomato for the timely application of irrigation. In this study, a simple classification and regression tree (CART) model that includes air temperature, vapor pressure deficit, and leaf-air temperature difference was established to classify the drought status of three tomato genotypes (i.e., cherry type 'Tainan ASVEG No. 19', large fruits breeding line '108290', and wild accession 'LA2093'). The results indicate that the proposed CART model exhibited a higher predictive sensitivity, specificity, geometric mean, and accuracy performance compared to the logistic model. In addition, the CART model was applicable not only to three tomato genotypes but across vegetative and reproductive stages. Furthermore, while the drought status was divided into low, medium, and high, the CART model provided a higher predictive performance than that of the logistic model. The results suggest that the drought status of tomato can be accurately classified by the proposed CART model. These results will provide a useful tool of the regular water management for tomato cultivation.

Estimation of Maize Foliar Temperature and Stomatal Conductance as Indicators of Water Stress Based on Optical and Thermal Imagery Acquired Using an Unmanned Aerial Vehicle (UAV) Platform

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.

Experience Gained When Using the Yuneec E10T Thermal Camera in Environmental Research

Thermal imaging is an important source of information for geographic information systems (GIS) in various aspects of environmental research. This work contains a variety of experiences related to the use of the Yuneec E10T thermal imaging camera with a 320 × 240 pixel matrix and 4.3 mm focal length dedicated to working with the Yuneec H520 UAV in obtaining data on the natural environment. Unfortunately, as a commercial product, the camera is available without radiometric characteristics. Using the heated bed of the Omni3d Factory 1.0 printer, radiometric calibration was performed in the range of 18–100 °C (high sensitivity range–high gain settings of the camera). The stability of the thermal camera operation was assessed using several sets of a large number of photos, acquired over three areas in the form of aerial blocks composed of parallel rows with a specific sidelap and longitudinal coverage. For these image sets, statistical parameters of thermal images such as the mean, minimum and maximum were calculated and then analyzed according to the order of registration. Analysis of photos taken every 10 m in vertical profiles up to 120 m above ground level (AGL) were also performed to show the changes in image temperature established within the reference surface. Using the established radiometric calibration, it was found that the camera maintains linearity between the observed temperature and the measured brightness temperature in the form of a digital number (DN). It was also found that the camera is sometimes unstable after being turned on, which indicates the necessity of adjusting the device’s operating conditions to external conditions for several minutes or taking photos over an area larger than the region of interest.

Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR

Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R2 = 0.7890) than at the plot scale (R2 = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (−0.9121 at tree scale and −0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (<1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy.

Challenges for a Massive Implementation of Phenomics in Plant Breeding Programs

Due to climate change and expected food shortage in the coming decades, not only will it be necessary to develop cultivars with greater tolerance to environmental stress, but it is also imperative to reduce breeding cycle time. In addition to yield evaluation, plant breeders resort to many sensory assessments and some others of intermediate complexity. However, to develop cultivars better adapted to current/future constraints, it is necessary to incorporate a new set of traits, such as morphophysiological and physicochemical attributes, information relevant to the successful selection of genotypes or parents. Unfortunately, because of the large number of genotypes to be screened, measurements with conventional equipment are unfeasible, especially under field conditions. High-throughput plant phenotyping (HTPP) facilitates collecting a significant amount of data quickly; however, it is necessary to transform all this information (e.g., plant reflectance) into helpful descriptors to the breeder. To the extent that a holistic characterization of the plant (phenomics) is performed in challenging environments, it will be possible to select the best genotypes (forward phenomics) objectively but also understand why the said individual differs from the rest (reverse phenomics). Unfortunately, several elements had prevented phenomics from developing as desired. Consequently, a new set of prediction/validation methodologies, seasonal ambient information, and the fusion of data matrices (e.g., genotypic and phenotypic information) need to be incorporated into the modeling. In this sense, for the massive implementation of phenomics in plant breeding, it will be essential to count an interdisciplinary team that responds to the urgent need to release material with greater capacity to tolerate environmental stress. Therefore, breeding programs should (i) be more efficient (e.g., early discarding of unsuitable material), (ii) have shorter breeding cycles (fewer crosses to achieve the desired cultivar), and (iii) be more productive, increasing the probability of success at the end of the breeding process (percentage of cultivars released to the number of initial crosses).

Opportunities and limits of controlled-environment plant phenotyping for climate response traits

Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.

Mineral composition and growth of tomato and cucumber affected by imidazolium-based ionic liquids

Imidazolium-based ionic liquids (ILs) have unique and tunable features with high potential in industrial use. However, the utilization of the ILs in industrial processes has recently arisen the question of their disposal and the effect on the environment. Therefore, in the present study, we investigated the effect of two commercial imidazolium-based ILs, 1-butyl-3-methylimidazolium chloride ([Bmim][Cl]) and 1-decyl-3-methylimidazolium chloride ([Dmim][Cl]) on the growth and chemical composition of widely grown vegetables - tomato and cucumber. Different concentrations (10, 100 or 1000 mg L^-1) of [Bmim][Cl] and [Dmim][Cl] were applied to the soil on which tomato was cultivated. After the harvest of tomato fruits, the same soil was used to grow and analyze the growth and chemical composition of cucumber. ILs significantly reduced shoot biomass and yield of tomato and significantly changed concentrations of N, K, Ca, Fe and Mn in the leaves, whereas concentrations of P, Cu and Zn were at the level of respected controls. The number of fruits of cucumber, grown on the soil previously treated with ILs, was significantly reduced along with yield, and mineral composition of leaves was significantly altered, with the exception to Cu. [Dmim][Cl] in general affected both tomato and cucumber more than [Bmim][Cl]. The application of IL with a longer alkyl substituent ([Dmim][Cl]) increased the temperature inside the tomato canopy and accelerated the senescence of plants.

Breeding for drought and heat tolerance in wheat

Many approaches have been adopted to enhance the heat and drought tolerance of wheat with mixed success. An assessment of the relative merits of different strategies is presented. Wheat is the most widely grown crop globally and plays a key role in human nutrition. However, it is grown in environments that are prone to heat and drought stress, resulting in severely reduced yield in some seasons. Increased climate variability is expected to have a particularly adverse effect of wheat production. Breeding for stable yield across both good and bad seasons while maintaining high yield under optimal conditions is a high priority for most wheat breeding programs and has been a focus of research activities. Multiple strategies have been explored to enhance the heat and drought tolerance of wheat including extensive genetic analysis and modify the expression of genes involved in stress responses, targeting specific physiological traits and direct selection under a range of stress scenarios. These approaches have been combined with improvements in phenotyping, the development of genetic and genomic resources, and extended screening and analysis techniques. The results have greatly expanded our knowledge and understanding of the factors that influence yield under stress, but not all have delivered the hoped-for progress. Here, we provide an overview of the different strategies and an assessment of the most promising approaches.

Morpho-physiological responses of indica rice (Oryza sativa sub. indica) to aluminum toxicity at seedling stage

Aluminum (Al) toxicity in acidic soils is a major problem in rice crop production, especially in the acid sulfate soil (pH < 4.0). Selecting Al-tolerant varieties of rice with low toxicity is one of the most appropriate strategies to overcome this problem. In the present study, we investigated the Al content in different rice genotypes, IR64 (high yielding), RD35 (local acidic-tolerant), and Azucena (AZU, positive-check Al-tolerant), and their physiological and morphological adaptations under a wide range Al (10, 25, 50 mM [Al₂(SO₄)₃]) treatments in the greenhouse conditions. Under 50-mM Al treatment, Al levels in the root tissues of rice seedlings cvs. AZU and IR64 were increased by 2.74- and 2.10-fold over control. Interestingly, Al contents in the roots of cv. RD35 were also exhibited by 2.04-fold over control. Similarly, Al contents in the leaves trend to increase in relation to a degree of Al treatments, leading to increase leaf temperature, chlorophyll degradation, limited CO₂ assimilation, and negative effect on root traits under 50 mM Al were evidently observed. Therefore, leaf temperature was considered a sensitive parameter regulated by high concentration of Al (50 mM), leading to increase in crop water stress index (CWSI > 0.6) and decrease in stomata conductance. Net photosynthetic rate (P_n) and transpiration rate (E) in rice seedlings of cv. RD35 subjected to 50 mM Al were significantly dropped by 74.76% and 47.71% over the control, respectively, resulting in reduced growth performances in terms of root length (26.57% reduction) and shoot fresh weight (46.15% reduction). An enrichment of Al in the root tissues without toxicity in rice cv. AZU may further help in discovering the Al homeostasis. In summary, Al enrichment in rice genotypes grown under Al-treatments was evidently observed in the root, leading to the limited root growth, root length, and root dry weight, especially in cv. RD35. Al restriction in the root tissues of cv. AZU (Al-tolerant) may play a key role as defense mechanisms to avoid translocation to other organs and the stomata closure was an alternative key factor to limit H₂O transpiration.

Effect of Pisolithus tinctorious on Physiological and Hormonal Traits in Cistus Plants to Water Deficit: Relationships among Water Status, Photosynthetic Activity and Plant Quality

Cistus species can form ectomycorrhizae and arbuscular mycorrhizal fungus that can bring benefits when plants are under water stress conditions. However, the application of some ectomycorrhizae on the water uptake under drought through physiological traits and hormonal regulation is less known. The experiment was performed during three months in a growth chamber with Cistus albidus plants in which the combined effect of the ectomycorrhiza Pisolithus tinctorious inoculation and two irrigation treatments (control and water-stressed plants) were applied. Irrigation absence caused significant decrease in aerial growth and tended to decrease soil water potential at the root surface, leading to a decrease in leaf water potential. Under these conditions, the abscisic acid and salicylic acid content increased while the precursor of ethylene decreased. Although the mycorrhization percentages were not high, the inoculation of P. tinctorious improved the water status and slightly cushioned the rise in leaf temperature of water-stressed plants. The ectomycorrhiza decreased the scopoletin values in leaves of plants subjected to deficit irrigation, indicating that inoculated plants had been able to synthesize defense mechanisms. Therefore, Pisolithus tinctorious alleviated some of the harmful effects of water scarcity in Cistus plants, being its use a sustainable option in gardening or restoration projects.

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.

Substrate composition affects the development of water stress and subsequent recovery by inducing physiological changes in Cistus albidus plants

Organic residues (compost) can be used as growth medium but may contain phytotoxic ions that, combined with a water deficit may alter the behavior of plants. The experiment was carried out in a growth chamber with Cistus albidus in a commercial substrate, C (sphagnum peat, coconut fiber and perlite, 8:7:1) and a mixture of compost substrates, Cp (slurry compost, coconut fiber and perlite, 3:6:1). Plants were grown in pots under well-watered, maintaining values of Ψ_l around -0.9 MPa (WW) and water-stressed (WS) conditions, where the irrigation was removed until reached values of Ψ_l around -3.0 MPa (water stress period), after then, water was re-established in all plants (recovery period). Although, the well-watered plants had a leaf water potential (Ψ_l) around -0.9 MPa, stomatal conductance (g_s) was 125 mmol m^-2s^-1 in the commercial substrate and 30 mmol m^-2s-1 in compost. The time taken to reach the threshold value at which water stress occurs was 13 days in the commercial substrate and 53 days in compost. Water-stressed plants in the commercial substrate had significantly lower values of Ψ_l and g_s than well-watered. Plants in compost maintained values of g_s similar in both irrigation treatments (WW and WS) and accumulated less biomass than those that grown in commercial. The water stress in compost led an increase in the adaxial epidermis, parenchyma and mesophyll, whereas water stress in commercial the proportions of the different tissues decreased. Higher lipid peroxidation values were found in plants grown in both substrates under water stress. The recovery time of the plants, until manage Ψ_l values around -0.9 MPa, depended on the type of substrate. The restoration of irrigation in commercial substrate act as a new stress, as reflected in the photochemical mechanisms.

Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought

During water stress, crops undertake adjustments in functional, structural, and biochemical traits. Hyperspectral data and machine learning techniques (PLS-R) can be used to assess water stress responses in plant physiology. In this study, we investigated the potential of hyperspectral optical (VNIR) measurements supplemented with thermal remote sensing and canopy height (hc) to detect changes in leaf physiology of soybean (C3) and maize (C4) plants under three levels of soil moisture in controlled environmental conditions. We measured canopy evapotranspiration (ET), leaf transpiration (Tr), leaf stomatal conductance (gs), leaf photosynthesis (A), leaf chlorophyll content and morphological properties (hc and LAI), as well as vegetation cover reflectance and radiometric temperature (TL,Rad). Our results showed that water stress caused significant ET decreases in both crops. This reduction was linked to tighter stomatal control for soybean plants, whereas LAI changes were the primary control on maize ET. Spectral vegetation indices (VIs) and TL,Rad were able to track these different responses to drought, but only after controlling for confounding changes in phenology. PLS-R modeling of gs, Tr, and A using hyperspectral data was more accurate when pooling data from both crops together rather than individually. Nonetheless, separated PLS-R crop models are useful to identify the most relevant variables in each crop such as TL,Rad for soybean and hc for maize under our experimental conditions. Interestingly, the most important spectral bands sensitive to drought, derived from PLS-R analysis, were not exactly centered at the same wavelengths of the studied VIs sensitive to drought, highlighting the benefit of having contiguous narrow spectral bands to predict leaf physiology and suggesting different wavelength combinations based on crop type. Our results are only a first but a promising step towards larger scale remote sensing applications (e.g., airborne and satellite). PLS-R estimates of leaf physiology could help to parameterize canopy level GPP or ET models and to identify different photosynthetic paths or the degree of stomatal closure in response to drought.

Trunk Girdling Increased Stomatal Conductance in Cabernet Sauvignon Grapevines, Reduced Glutamine, and Increased Malvidin-3-Glucoside and Quercetin-3-Glucoside Concentrations in Skins and Pulp at Harvest

Girdling is a traditional horticultural practice applied at fruit set or other phenological stages, and is used mostly as a vine management. In grapevines, it is used primarily for table grapes to improve berry weight, sugar content, color, and to promote early harvest. The objective of this study was to evaluate the effect of trunk girdling applied at veraison, in 'Cabernet Sauvignon' wine grapes (Vitis vinifera L.), on agronomical and physiological parameters during vine development from the onset of ripening (veraison) to harvest, and additionally to quantify the effect of girdling on primary and secondary metabolism. Girdling was applied 146 days after pruning (dap) at veraison, when berry sampling for metabolomics and agronomical evaluations commenced, with a further three sampling dates until harvest, at 156 dap (30% maturation, 10 days after girdling-dag), 181 dap (70% maturation, 35 dag), and 223 dap (commercial harvest, 77 dag). Skin/pulp and seed tissues were extracted separately and metabolomics was performed using one-dimensional proton nuclear magnetic resonance (1D 1H NMR) spectroscopy and high performance liquid chromatography (HPLC-DAD). At harvest, girdling significantly increased stomatal conductance (gs) in vines, decreased glutamine concentrations, and increased anthocyanin and flavonol concentrations in the skin/pulp tissues of grape berries. Berry weight was reduced by 27% from 181 dap to harvest, and was significantly higher in grapes from girdled vines at 181 dap. Sugars, organic acids, and other amino acids in skin/pulp or seeds were not significantly different, possibly due to extra-fascicular phloem vessels transporting metabolites from leaves to the roots. Using a metabolomics approach, differences between skin/pulp and seeds tissues were meaningful, and a greater number of secondary metabolites in skin/pulp was affected by girdling than in seeds. Girdling is a simple technique that could easily be applied commercially on vine management to improve berry color and other phenolics in 'Cabernet Sauvignon' grapes.

Breeder friendly phenotyping

The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.

Thermography methods to assess stomatal behaviour in a dynamic environment

Although thermography allows rapid, non-invasive measurements of large numbers of plants, it has not been used extensively due to the difficulty in deriving biologically relevant information such as leaf transpiration (E) and stomatal conductance (gsw) from thermograms. Methods normalizing leaf temperature using temperatures from reference materials (e.g. with and without evaporative flux) to generate stress indices are generally preferred due to their ease of use to assess plant water status. Here, a simplified method to solve dynamic energy balance equations is presented, which enables the calculation of 'wet' and 'dry' leaf temperatures in order to derive stress indices, whilst providing accurate estimates of E and gsw. Comparing stress indices and gas exchange parameters highlights the limitation of stress indices in a dynamic environment and how this problem can be overcome using artificial leaf references with known conductance. Additionally, applying the equations for each pixel of a thermogram to derive the rapidity of stomatal response over the leaf lamina in wheat revealed the spatial heterogeneity of stomatal behaviour. Rapidity of stomatal movements is an important determinant of water use efficiency, and our results showed 'patchy' responses that were linked to both the spatial and temporal response of gsw.

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

Association mapping and genetic dissection of drought-induced canopy temperature differences in rice

Drought-stressed plants display reduced stomatal conductance, which results in increased leaf temperature by limiting transpiration. In this study, thermal imaging was used to quantify the differences in canopy temperature under drought in a rice diversity panel consisting of 293 indica accessions. The population was grown under paddy field conditions and drought stress was imposed for 2 weeks at flowering. The canopy temperature of the accessions during stress negatively correlated with grain yield (r= -0.48) and positively with plant height (r=0.56). Temperature values were used to perform a genome-wide association (GWA) analysis using a 45K single nucleotide polynmorphism (SNP) map. A quantitative trait locus (QTL) for canopy temperature under drought was detected on chromosome 3 and fine-mapped using a high-density imputed SNP map. The candidate genes underlying the QTL point towards differences in the regulation of guard cell solute intake for stomatal opening as the possible source of temperature variation. Genetic variation for the significant markers of the QTL was present only within the tall, low-yielding landraces adapted to drought-prone environments. The absence of variation in the shorter genotypes, which showed lower leaf temperature and higher grain yield, suggests that breeding for high grain yield in rice under paddy conditions has reduced genetic variation for stomatal response under drought.

Spatial and Temporal Variability of Plant Leaf Responses Cascade after PSII Inhibition: Raman, Chlorophyll Fluorescence and Infrared Thermal Imaging

The use of photosystem II (PSII) inhibitors allows simulating cascade of defense and damage responses, including the oxidative stress. In our study, PSII inhibiting herbicide metribuzin was applied to the leaf of the model plant species Chenopodium album. The temporally and spatially resolved cascade of defense responses was studied noninvasively at the leaf level by combining three imaging approaches: Raman spectroscopy as a principal method, corroborated by chlorophyll a fluorescence (ChlF) and infrared thermal imaging. ChlF imaging show time-dependent transport in acropetal direction through veins and increase of area affected by metribuzin and demonstrated the ability to distinguish between fast processes at the level of electron transport (1 - Vj) from slow processes at the level of non-photochemical energy dissipation (NPQ) or maximum efficiency of PSII photochemistry (Fv/Fm). The high-resolution resonance Raman images show zones of local increase of carotenoid signal 72 h after the herbicide application, surrounding the damaged tissue, which points to the activation of defense mechanisms. The shift in the carotenoid band indicates structural changes in carotenoids. Finally, the increase of leaf temperature in the region surrounding the spot of herbicide application and expanding in the direction to the leaf tip proves the metribuzin effect on slow stomata closure.

Thermal imaging as a noninvasive technique for analyzing circadian rhythms in plants

Endogenous (˜24 circadian) rhythms control an enormously diverse range of processes in plants and are, increasingly, the target of studies aimed at understanding plant performance. Although in the previous few decades most plant circadian research has focused on Arabidopsis, there is a pressing need for low-cost, high-throughput tools for analyzing rhythms in a wider variety of species. The present contribution investigates using circadian temperature oscillations as a novel marker for assaying plant circadian rhythms. A thermal imaging platform was set up to measure diel and circadian rhythms in different plant species, in wild-type and circadian mutant plants, and in leaves and flowers. Results from the thermal imaging technique were compared with those from other established circadian assay techniques. All of the dicot and monocot species examined showed robust circadian rhythms of leaf surface temperature; the effects of circadian mutations on thermocycles were similar to those reported using other techniques. In Petunia × atkinsiana plants circadian oscillations were observed in both leaves and flowers. Thermal imaging is an extremely useful technique for analyzing circadian rhythms in plants. It is predicted that the ability to make very high temporal resolution measurements may facilitate the discovery of novel aspects of circadian control.

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high-throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field-based plant phenomics can enable next-generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from 'Green Revolution' traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented.

Evaluation of the Phenotypic Repeatability of Canopy Temperature in Wheat Using Continuous-Terrestrial and Airborne Measurements

Infrared canopy temperature (CT) is a well-established surrogate measure of stomatal conductance. There is ample evidence showing that genotypic variation in stomatal conductance is associated with grain yield in wheat. Our goal was to determine when CT repeatability is greatest (throughout the season and within the day) to guide CT deployment for research and wheat breeding. CT was measured continuously with ArduCrop wireless infrared thermometers from post-tillering to physiological maturity, and with airborne thermography on cloudless days from manned helicopter at multiple times before and after flowering. Our experiments in wheat, across two years contrasting for water availability, showed that repeatability for CT was greatest later in the season, during grain-filling, and usually in the afternoon. This was supported by the observation that repeatability for ArduCrop, and more so for airborne CT, was significantly associated (P < 0.0001) with calculated clear-sky solar radiation and to a lesser degree, vapor pressure deficit. Adding vapor pressure deficit to a model comprising either clear-sky solar radiation or its determinants, day-of-year and hour-of-day, made little to no improvement to the coefficient of determination. Phenotypic correlations for airborne CT afternoon sampling events were consistently high between events in the same year, more so for the year when soil water was plentiful (r = 0.7 to 0.9) than the year where soil water was limiting (r = 0.4 to 0.9). Phenotypic correlations for afternoon airborne CT were moderate between years contrasting in soil water availability (r = 0.1 to 0.5) and notably greater on two separate days following irrigation or rain in the drier year, ranging from r = 0.39 to 0.53 (P < 0.0001) for the midday events. For ArduCrop CT the pattern of phenotypic correlations, within a given year, was similar for both years: phenotypic correlations were higher during the grain-filling months of October and November and for hours-of-day from 11 onwards. The lowest correlations comprised events from hours-of-day 8 and 9 across all months. The capacity for the airborne method to instantaneously sample CT on hundreds of plots is more suited to large field experiments than the static ArduCrop sensors which measure CT continuously on a single experimental plot at any given time. Our findings provide promising support for the reliable deployment of CT phenotyping for research and wheat breeding, whereby the high repeatability and high phenotypic correlations between afternoon sampling events during grain-filling could enable reliable screening of germplasm from only one or two sampling events.

Thermal Imaging Reliability for Estimating Grain Yield and Carbon Isotope Discrimination in Wheat Genotypes: Importance of the Environmental Conditions

Canopy temperature (Tc) by thermal imaging is a useful tool to study plant water status and estimate other crop traits. This work seeks to estimate grain yield (GY) and carbon discrimination (Δ13C) from stress degree day (SDD = Tc - air temperature, Ta), considering the effect of a number of environmental variables such as the averages of the maximum vapor pressure deficit (VPDmax) and the ambient temperature (Tmax), and the soil water content (SWC). For this, a set of 384 and a subset of 16 genotypes of spring bread wheat were evaluated in two Mediterranean-climate sites under water stress (WS) and full irrigation (FI) conditions, in 2011 and 2012, and 2014 and 2015, respectively. The relationship between the GY of the 384 wheat genotypes and SDD was negative and highly significant in 2011 (r2 = 0.52 to 0.68), but not significant in 2012 (r2 = 0.03 to 0.12). Under WS, the average GY, Δ13C, and SDD of wheat genotypes growing in ten environments were more associated with changes in VPDmax and Tmax than with the SWC. Therefore, the amount of water available to the plant is not enough information to assume that a particular genotype is experiencing a stress condition.

Nanobiotechnology approaches for engineering smart plant sensors

Nanobiotechnology has the potential to enable smart plant sensors that communicate with and actuate electronic devices for improving plant productivity, optimize and automate water and agrochemical allocation, and enable high-throughput plant chemical phenotyping. Reducing crop loss due to environmental and pathogen-related stresses, improving resource use efficiency and selecting optimal plant traits are major challenges in plant agriculture industries worldwide. New technologies are required to accurately monitor, in real time and with high spatial and temporal resolution, plant physiological and developmental responses to their microenvironment. Nanomaterials are allowing the translation of plant chemical signals into digital information that can be monitored by standoff electronic devices. Herein, we discuss the design and interfacing of smart nanobiotechnology-based sensors that report plant signalling molecules associated with health status to agricultural and phenotyping devices via optical, wireless or electrical signals. We describe how nanomaterial-mediated delivery of genetically encoded sensors can act as tools for research and development of smart plant sensors. We assess performance parameters of smart nanobiotechnology-based sensors in plants (for example, resolution, sensitivity, accuracy and durability) including in vivo optical nanosensors and wearable nanoelectronic sensors. To conclude, we present an integrated and prospective vision on how nanotechnology could enable smart plant sensors that communicate with and actuate electronic devices for monitoring and optimizing individual plant productivity and resource use.

Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment

In spite of the significant progress made in recent years, the use of thermography to derive biologically relevant traits remains a challenge under fluctuating conditions. The aim of this study was to rethink the current method to process thermograms and derive temporal responses of stomatal conductance (gsw) using dynamic energy balance equations. Time-series thermograms provided the basis for a spatial and temporal characterization of gsw responses in wheat (Triticum aestivum). A leaf replica with a known conductance was used to validate the approach and to test the ability of our model to be used with any material and under any environmental conditions. The results highlighted the importance of the co-ordinated stomatal responses that run parallel to the leaf blade despite their patchy distribution. The diversity and asymmetry of the temporal response of gsw observed after a step increase and step decrease in light intensity can be interpreted as a strategy to maximize photosynthesis per unit of water loss and avoid heat stress in response to light flecks in a natural environment. This study removes a major bottleneck for plant phenotyping platforms and will pave the way to further developments in our understanding of stomatal behaviour.

Explorative Frequency Analysis of Leaf Temperature Behavior of Maize (Zea mays subsp. mays) at Water Deficit

In this study, different standard frequency analysis (FA) methods are applied to measured leaf temperature data of maize plants (developmental stages EC13-15). These FA methods are used to identify specific behaviors, regularities, and sudden changes in frequencies/amplitudes of data, e.g., in control engineering. The thorough application of different FA methods in plant studies is novel. The aim of this paper is to analyze features of the measured data and to explore the explanatory power of different methods for the detection of plant dynamic behavioral changes. The basic assumption is an expected relation between plant water stress and resulting changes in leaf temperature oscillations caused by stress-induced changes in stomatal behavior. Therefore, an irrigation experiment (laboratory; controlled environmental conditions) was implemented to compare leaf temperature behavior of stressed and unstressed plants. Leaf temperature time series are processed and the results are compared as functions of time showing the behavioral changes in terms of the different methods applied. The analysis of results is explained; conclusions, which can be made based on different methods, are given. The study confirms the applicability of FA methods and provides new insights into leaf temperature behavioral patterns. Results are discussed regarding the hypothesized incipience of leaf temperature oscillations due to water stress.

Can UAV-Based Infrared Thermography Be Used to Study Plant-Parasite Interactions between Mistletoe and Eucalypt Trees?

Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that the mistletoe infection rate was relatively high, with 69% of the Eucalyptus fibrosa and 75% of the E. moluccana trees being infected. Next, to study the potential consequences of the infection for the trees, canopy temperatures of mistletoe plants and of infected and uninfected trees were analyzed using thermal imagery acquired during 10 flights with an unmanned aerial vehicle (UAV) in two consecutive summer seasons. Throughout all flight campaigns, mistletoe canopy temperature was 0.3–2 K lower than the temperature of the eucalypt canopy it was growing in, suggesting higher transpiration rates. Differences in canopy temperature between infected eucalypt foliage and mistletoe were particularly large when incoming radiation peaked. In these conditions, eucalypt foliage from infected trees also had significantly higher canopy temperatures (and likely lower transpiration rates) compared to that of uninfected trees of the same species. The study demonstrates the potential of using UAV-based infrared thermography for studying plant-water relations of mistletoe and its hosts.

Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle

Characterizing the land surface temperature (LST) and its diurnal cycle is important in understanding a range of surface properties, including soil moisture status, evaporative response, vegetation stress and ground heat flux. While remote-sensing platforms present a number of options to retrieve this variable, there are inevitable compromises between the resolvable spatial and temporal resolution. For instance, the spatial resolution of geostationary satellites, which can provide sub-hourly LST, is often too coarse (3 km) for many applications. On the other hand, higher-resolution polar orbiting satellites are generally infrequent in time, with return intervals on the order of weeks, limiting their capacity to capture surface dynamics. With recent developments in the application of unmanned aerial vehicles (UAVs), there is now the opportunity to collect LST measurements on demand and at ultra-high spatial resolution. Here, we detail the collection and analysis of a UAV-based LST dataset, with the purpose of examining the diurnal surface temperature response: something that has not been possible from traditional satellite platforms at these scales. Two separate campaigns were conducted over a bare desert surface in combination with either Rhodes grass or a recently harvested maize field. In both cases, thermal imagery was collected between 0800 and 1700 local solar time. The UAV-based diurnal cycle was consistent with ground-based measurements, with a mean correlation coefficient and root mean square error (RMSE) of 0.99 and 0.68 °C, respectively. LST retrieved over the grass surface presented the best results, with an RMSE of 0.45 °C compared to 0.67 °C for the single desert site and 1.28 °C for the recently harvested maize surface. Even considering the orders of magnitude difference in scale, an exploratory analysis comparing retrievals of the UAV-based diurnal cycle with METEOSAT geostationary data yielded pleasing results (R = 0.98; RMSE = 1.23 °C). Overall, our analysis revealed a diurnal range over the desert and maize surfaces of ~20 °C and ~17 °C respectively, while the grass showed a reduced amplitude of ~12 °C. Considerable heterogeneity was observed over the grass surface at the peak of the diurnal cycle, which was likely indicative of the varying crop water status. To our knowledge, this study presents the first spatially varying analysis of the diurnal LST captured at ultra-high resolution, from any remote platform. Our findings highlight the considerable potential to utilize UAV-based retrievals to enhance investigations across multi-disciplinary studies in agriculture, hydrology and land-atmosphere investigations.

Remote sensing of plant-water relations: An overview and future perspectives

Vegetation is a highly dynamic component of the Earth surface and substantially alters the water cycle. Particularly the process of oxygenic plant photosynthesis determines vegetation connecting the water and carbon cycle and causing various interactions and feedbacks across Earth spheres. While vegetation impacts the water cycle, it reacts to changing water availability via functional, biochemical and structural responses. Unravelling the resulting complex feedbacks and interactions between the plant-water system and environmental change is essential for any modelling approaches and predictions, but still insufficiently understood due to currently missing observations. We hypothesize that an appropriate cross-scale monitoring of plant-water relations can be achieved by combined observational and modelling approaches. This paper reviews suitable remote sensing approaches to assess plant-water relations ranging from pure observational to combined observational-modelling approaches. We use a combined energy balance and radiative transfer model to assess the explanatory power of pure observational approaches focussing on plant parameters to estimate plant-water relations, followed by an outline for a more effective use of remote sensing by their integration into soil-plant-atmosphere continuum (SPAC) models. We apply a mechanistic model simulating water movement in the SPAC to reveal insight into the complexity of relations between soil, plant and atmospheric parameters, and thus plant-water relations. We conclude that future research should focus on strategies combining observations and mechanistic modelling to advance our knowledge on the interplay between the plant-water system and environmental change, e.g. through plant transpiration.

Measuring the dynamic photosynthome

Background

Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The 'dynamic' changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield.

Scope

It is known that photosynthesis does not respond in a timely manner, and even a small temporal 'mismatch' between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered.

Conclusions

There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of 'phenomics' which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the 'photosynthome'. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved.

A novel QTL associated with rice canopy temperature difference affects stomatal conductance and leaf photosynthesis

Canopy temperature can be a good indicator of stomatal conductance. To understand the genetic basis of phenotypic differences in stomatal conductance between average and high-yielding rice (Oryza sativa L.) cultivars, we conducted a quantitative trait locus (QTL) analysis of canopy temperature. We developed reciprocal series of backcross inbred lines (BC1F6) derived from a cross between the average-yielding japonica cultivar 'Koshihikari' and the high-yielding indica cultivar 'Takanari'. A stable QTL, qCTd11 (QTL for canopy temperature difference on chromosome 11) on the short arm of chromosome 11, accounted for 10.4 and 19.8% of the total phenotypic variance in the two lines; the 'Takanari' allele decreased the canopy temperature difference value. A chromosome segment substitution line carrying the Takanari qCTd11 showed a greater reduction in canopy temperature than 'Koshihikari', and had higher stomatal conductance and photosynthetic rate. These results suggest that qCTd11 is not only involved in canopy temperature, but is also involved in both stomatal conductance and photosynthetic rate.

Application of thermography for monitoring stomatal conductance of Coffea arabica under different shading systems

Stomatal regulation is a key process in the physiology of Coffea arabica (C. arabica). Intrinsically linked to photosynthesis and water relations, it provides insights into the plant's adaptive capacity, survival and growth. The ability to rapidly quantify this parameter for C. arabica under different agroecological systems would be an indispensable tool. Using a Flir E6 MIR Camera, an index that is equivalent to stomatal conductance (I_g) was compared with stomatal conductance measurements (g_s) in a mature coffee plantation. In order to account for varying meteorological conditions between days, the methods were also compared under stable meteorological conditions in a laboratory and I_g was also converted to absolute stomatal conductance values (g₁). In contrast to typical plant-thermography methods which measure indices once per day over an extended time period, we used high resolution hourly measurements over daily time series with 9 sun and 9 shade replicates. Eight daily time series showed a strong correlation between methods, while the remaining 10 were not significant. Including several other meteorological parameters in the calculation of g₁ did not contribute to any stronger correlation between methods. Total pooled data (combined daily series) resulted in a correlation of ρ=0.66 (P≤2.2e-16), indicating that our approach is particularly useful for situations where absolute values of stomatal conductance are not required, such as for comparative purposes, screening or trend analysis. We use the findings to advance the protocol for a more accurate methodology which may assist in quantifying advantageous microenvironment designs for coffee, considering the current and future climates of coffee growing regions.

Comparison of infrared canopy temperature in a rubber plantation and tropical rain forest

Canopy temperature is a result of the canopy energy balance and is driven by climate conditions, plant architecture, and plant-controlled transpiration. Here, we evaluated canopy temperature in a rubber plantation (RP) and tropical rainforest (TR) in Xishuangbanna, southwestern China. An infrared temperature sensor was installed at each site to measure canopy temperature. In the dry season, the maximum differences (T_c - T_a) between canopy temperature (T_c) and air temperature (T_a) in the RP and TR were 2.6 and 0.1 K, respectively. In the rainy season, the maximum (T_c - T_a) values in the RP and TR were 1.0 and -1.1 K, respectively. There were consistent differences between the two forests, with the RP having higher (T_c - T_a) than the TR throughout the entire year. Infrared measurements of T_c can be used to calculate canopy stomatal conductance in both forests. The difference in (T_c - T_a) at three g_c levels with increasing direct radiation in the RP was larger than in the TR, indicating that change in (T_c - T_a) in the RP was relatively sensitive to the degree of stomatal closure.

Stomatal function, density and pattern, and CO2 assimilation in Arabidopsis thaliana tmm1 and sdd1-1 mutants

Stomata modulate the exchange of water and CO₂ between plant and atmosphere. Although stomatal density is known to affect CO₂ diffusion into the leaf and thus photosynthetic rate, the effect of stomatal density and patterning on CO₂ assimilation is not fully understood. We used wild types Col-0 and C24 and stomatal mutants sdd1-1 and tmm1 of Arabidopsis thaliana, differing in stomatal density and pattern, to study the effects of these variations on both stomatal and mesophyll conductance and CO₂ assimilation rate. Anatomical parameters of stomata, leaf temperature and carbon isotope discrimination were also assessed. Our results indicate that increased stomatal density enhanced stomatal conductance in sdd1-1 plants, with no effect on photosynthesis, due to both unchanged photosynthetic capacity and decreased mesophyll conductance. Clustering (abnormal patterning formed by clusters of two or more stomata) and a highly unequal distribution of stomata between the adaxial and abaxial leaf sides in tmm1 mutants also had no effect on photosynthesis. Except at very high stomatal densities, stomatal conductance and water loss were proportional to stomatal density. Stomatal formation in clusters reduced stomatal dynamics and their operational range as well as the efficiency of CO₂ transport.

Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography

Lower canopy temperature (CT), resulting from increased stomatal conductance, has been associated with increased yield in wheat. Historically, CT has been measured with hand-held infrared thermometers. Using the hand-held CT method on large field trials is problematic, mostly because measurements are confounded by temporal weather changes during the time required to measure all plots. The hand-held CT method is laborious and yet the resulting heritability low, thereby reducing confidence in selection in large scale breeding endeavors. We have developed a reliable and scalable crop phenotyping method for assessing CT in large field experiments. The method involves airborne thermography from a manned helicopter using a radiometrically-calibrated thermal camera. Thermal image data is acquired from large experiments in the order of seconds, thereby enabling simultaneous measurement of CT on potentially 1000s of plots. Effects of temporal weather variation when phenotyping large experiments using hand-held infrared thermometers are therefore reduced. The method is designed for cost-effective and large-scale use by the non-technical user and includes custom-developed software for data processing to obtain CT data on a single-plot basis for analysis. Broad-sense heritability was routinely >0.50, and as high as 0.79, for airborne thermography CT measured near anthesis on a wheat experiment comprising 768 plots of size 2 × 6 m. Image analysis based on the frequency distribution of temperature pixels to remove the possible influence of background soil did not improve broad-sense heritability. Total image acquisition and processing time was ca. 25 min and required only one person (excluding the helicopter pilot). The results indicate the potential to phenotype CT on large populations in genetics studies or for selection within a plant breeding program.

Thermal imaging of soybean response to drought stress: the effect of Ascophyllum nodosum seaweed extract

Previous experiments have demonstrated positive effect of Acadian(®) extract of Ascophyllum nodosum on plant stress-resistance, however the mode of action is not fully understood. The aim of this study was to understand the physiological effect of Acadian(®) seaweed extract on the plant response to drought stress. Leaf temperature and leaf angle were measured as early-stage indicators of plant stress with thermal imaging "in situ" over a 5-day stress-recovery trial. The early stress-response of control became visible on the third day as a rapid wilting of leaves, accompanied with the asymptotic increase of leaf temperature on 4-5 °C to the thermal equilibrium with ambient air temperature. At the same time Acadian(®) treated plants still maintained turgor, accompanied with the linear increase in leaf temperature, which indicated better control of stomatal closure. Re-watering on the fifth day showed better survival of treated plants compared to control. This study demonstrated the ability of Acadian(®) seaweed extract to improve resistance of soybean plants to water stress.

Assessing Drought Responses Using Thermal Infrared Imaging

Canopy temperature, a surrogate for stomatal conductance, is shown to be a good indicator of plant water status and a potential tool for phenotyping and irrigation scheduling. Measurement of stomatal conductance and leaf temperature has traditionally been done by using porometers or gas exchange analyzers and fine-wire thermocouples attached to the leaves, which are labor intensive and point measurements. The advent of remote or proximal thermal sensing technologies has provided the potential for scaling up to leaves, plants, and canopies. Thermal cameras with a temperature resolution of <0.1 K now allow one to study the temperature variation within and between plants. This chapter discusses some applications of infrared thermography for assessing drought and other abiotic and biotic stress and outlines some of the main factors that need to be considered when applying this to the study of leaf or canopy temperature whether in controlled environments or in the field.

High resolution imaging of maize (Zea mays) leaf temperature in the field: the key role of the regions of interest

The use of remote sensors (thermometers and cameras) to analyse crop water status in field conditions is fraught with several difficulties. In particular, average canopy temperature measurements are affected by the mixture of soil and green regions, the mutual shading of leaves and the variability of absorbed radiation. The aim of the study was to analyse how the selection of different 'regions of interest' (ROI) in canopy images affect the variability of the resulting temperature averages. Using automated image segmentation techniques we computed the average temperature in four nested ROI of decreasing size, from the whole image down to the sunlit fraction of a leaf located in the upper part of the canopy. The study was conducted on maize (Zea mays L.) at the flowering stage, for its large leaves and well structured canopy. Our results suggest that, under these conditions, the ROI comprising the sunlit fraction of a leaf located in the upper part of the canopy should be analogous to the single leaf approach (in controlled conditions) that allows the estimation of stomatal conductance or plant water potential.