Thermography to explore plant-environment interactions

Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome

Plant electrophysiology has unveiled the involvement of electrical signals in the physiology and behavior of plants. Spontaneously generated bioelectric activity can be altered in response to changes in environmental conditions, suggesting that a plant's electrome may possess a distinct signature associated with various stimuli. Analyzing electrical signals, particularly the electrome, in conjunction with Machine Learning (ML) techniques has emerged as a promising approach to classify characteristic electrical signals corresponding to each stimulus. This study aimed to characterize the electrome of common bean (Phaseolus vulgaris L.) cv. BRS-Expedito, subjected to different water availabilities, seeking patterns linked to these stimuli. For this purpose, bean plants in the vegetative stage were subjected to the following treatments: (I) distilled water; (II) half-strength Hoagland's nutrient solution; (III) -2 MPa PEG solution; and (IV) -2 MPa NaCl solution. Electrical signals were recorded within a Faraday's cage using the MP36 electronic system for data acquisition. Concurrently, plant water status was assessed by monitoring leaf turgor variation. Leaf temperature was additionally measured. Various analyses were conducted on the electrical time series data, including arithmetic average of voltage variation, skewness, kurtosis, Probability Density Function (PDF), autocorrelation, Power Spectral Density (PSD), Approximate Entropy (ApEn), Fast Fourier Transform (FFT), and Multiscale Approximate Entropy (ApEn(s)). Statistical analyses were performed on leaf temperature, voltage variation, skewness, kurtosis, PDF µ exponent, autocorrelation, PSD β exponent, and approximate entropy data. Machine Learning analyses were applied to identify classifiable patterns in the electrical time series. Characterization of the electrome of BRS-Expedito beans revealed stimulus-dependent profiles, even when alterations in water availability stimuli were similar in terms of quality and intensity. Additionally, it was observed that the bean electrome exhibits high levels of complexity, which are altered by different stimuli, with more intense and aversive stimuli leading to drastic reductions in complexity levels. Notably, one of the significant findings was the 100% accuracy of Small Vector Machine in detecting salt stress using electrome data. Furthermore, the study highlighted alterations in the plant electrome under low water potential before observable leaf turgor changes. This work demonstrates the potential use of the electrome as a physiological indicator of the water status in bean plants.

A Solanum lycopersicum polyamine oxidase contributes to the control of plant growth, xylem differentiation, and drought stress tolerance

Polyamines are involved in several plant physiological processes. In Arabidopsis thaliana, five FAD-dependent polyamine oxidases (AtPAO1 to AtPAO5) contribute to polyamine homeostasis. AtPAO5 catalyzes the back-conversion of thermospermine (T-Spm) to spermidine and plays a role in plant development, xylem differentiation, and abiotic stress tolerance. In the present study, to verify whether T-Spm metabolism can be exploited as a new route to improve stress tolerance in crops and to investigate the underlying mechanisms, tomato (Solanum lycopersicum) AtPAO5 homologs were identified (SlPAO2, SlPAO3, and SlPAO4) and CRISPR/Cas9-mediated loss-of-function slpao3 mutants were obtained. Morphological, molecular, and physiological analyses showed that slpao3 mutants display increased T-Spm levels and exhibit changes in growth parameters, number and size of xylem elements, and expression levels of auxin- and gibberellin-related genes compared to wild-type plants. The slpao3 mutants are also characterized by improved tolerance to drought stress, which can be attributed to a diminished xylem hydraulic conductivity that limits water loss, as well as to a reduced vulnerability to embolism. Altogether, this study evidences conservation, though with some significant variations, of the T-Spm-mediated regulatory mechanisms controlling plant growth and differentiation across different plant species and highlights the T-Spm role in improving stress tolerance while not constraining growth.

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.

Uncovering the wide protective responses in Coffea spp. leaves to single and superimposed exposure of warming and severe water deficit

Climate changes boosted the frequency and severity of drought and heat events, with aggravated when these stresses occur simultaneously, turning crucial to unveil the plant response mechanisms to such harsh conditions. Therefore, plant responses/resilience to single and combined exposure to severe water deficit (SWD) and heat were assessed in two cultivars of the main coffee-producing species: Coffea arabica cv. Icatu and C. canephora cv. Conilon Clone 153 (CL153). Well-watered plants (WW) were exposed to SWD under an adequate temperature of 25/20°C (day/night), and thereafter submitted to a gradual increase up to 42/30°C, and a 14-d recovery period (Rec14). Greater protective response was found to single SWD than to single 37/28°C and/or 42/30°C (except for HSP70) in both cultivars, but CL153-SWD plants showed the larger variations of leaf thermal imaging crop water stress index (CWSI, 85% rise at 37/28°C) and stomatal conductance index (IG, 66% decline at 25/20°C). Both cultivars revealed great resilience to SWD and/or 37/28°C, but a tolerance limit was surpassed at 42/30°C. Under stress combination, Icatu usually displayed lower impacts on membrane permeability, and PSII function, likely associated with various responses, usually mostly driven by drought (but often kept or even strengthened under SWD and 42/30°C). These included the photoprotective zeaxanthin and lutein, antioxidant enzymes (superoxide dismutase, Cu,Zn-SOD; ascorbate peroxidase, APX), HSP70, arabinose and mannitol (involving de novo sugar synthesis), contributing to constrain lipoperoxidation. Also, only Icatu showed a strong reinforcement of glutathione reductase activity under stress combination. In general, the activities of antioxidative enzymes declined at 42/30°C (except Cu,Zn-SOD in Icatu and CAT in CL153), but HSP70 and raffinose were maintained higher in Icatu, whereas mannitol and arabinose markedly increased in CL153. Overall, a great leaf plasticity was found, especially in Icatu that revealed greater responsiveness of coordinated protection under all experimental conditions, justifying low PIChr and absence of lipoperoxidation increase at 42/30°C. Despite a clear recovery by Rec14, some aftereffects persisted especially in SWD plants (e.g., membranes), relevant in terms of repeated stress exposure and full plant recovery to stresses.

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.

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.

Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses

Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.

In situ characterisation of whole-plant stomatal responses to VPD using leaf optical dendrometry

Vapour pressure deficit (VPD) plays a crucial role in regulating plant carbon and water fluxes due to its influence on stomatal behaviour and transpiration. Yet, characterising stomatal responses of the whole plant to VPD remains challenging due to methodological limitations. Here, we develop a novel method for in situ assessment of whole-plant stomatal responses (gc ) to VPD in the herbaceous plant Tanacetum cinerariifolium. To do this, we examine the relationship between daytime VPD and the corresponding soil-stem water potential gradient (ΔΨ) monitored using the optical dendrometry in well-hydrated plants under nonlimiting light in both glasshouse and field conditions. In glasshouse plants, ΔΨ increased proportionally with the VPD up to a threshold of 1.53 kPa, beyond which the slope decreased, suggesting a two-phase response in gc . This pattern aligned with corresponding gravimetrically measured gc behaviour, which also showed a decline when VPD exceeded a similar threshold. This response was then compared with that of field plants monitored using the optical dendrometry technique over a growing season under naturally variable VPD conditions and nonlimiting light and water supply. Field plants exhibited a similar threshold-type response to VPD but were more sensitive than glasshouse individuals with a VPD threshold of 0.74 kPa. The results showed that whole-plant gc responses to VPD can be characterised optically in T. cinerariifolium, introducing a new tool for the monitoring and characterisation of stomatal behaviour in situ.

Field phenotyping for African crops: overview and perspectives

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

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.

Defining durum wheat ideotypes adapted to Mediterranean environments through remote sensing traits

An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature.

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.

Use of Thermography to Evaluate Alternative Crops for Off-Season in the Cerrado Region

Future predictions due to climate change are of decreases in rainfall and longer drought periods. The search for new tolerant crops is an important strategy. The objective of this study was to evaluate the effect of water stress on the physiology and productivity of crops with potential for growing in the off-season period in the Cerrado, and evaluate correlations with the temperature of the canopy obtained by means of thermography. The experiment was conducted under field conditions, with experimental design in randomized blocks, in a split-plot scheme and four replications. The plots were: common bean (Phaseolus vulgaris); amaranth (Amaranthus cruentus); quinoa (Chenopodium quinoa); and buckwheat (Fagopyrum esculentum). The subplots were composed of four water regimes: maximum water regime (WR 535 mm), high-availability regime (WR 410 mm), off-season water regime (WR 304 mm) and severe water regime (WR 187 mm). Under WR 304 mm, the internal concentration of CO₂ and photosynthesis were reduced by less than 10% in amaranth. Common bean and buckwheat reduced 85% in photosynthesis. The reduction in water availability increased the canopy temperature in the four crops and, in general, common bean was the most sensitive species, while quinoa had the lowest canopy temperatures. Furthermore, canopy temperature correlated negatively with grain yield, biomass yield and gas exchange across all plant species, thus thermal imaging of the canopy represents a promising tool for monitoring crop productivity for farmers, For the identification of crops with high water use management for research.

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.

Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review

Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.

A Guide to Plant Intracellular Temperature Imaging using Fluorescent Thermometers

All aspects of plant physiology are influenced by temperature. Changes in environmental temperature alter the temperatures of plant tissues and cells, which then affect various cellular activities, such as gene expression, protein stability and enzyme activities. In turn, changes in cellular activities, which are associated with either exothermic or endothermic reactions, can change the local temperature in cells and tissues. In the past 10 years, a number of fluorescent probes that detect temperature and enable intracellular temperature imaging have been reported. Intracellular temperature imaging has revealed that there is a temperature difference >1°C inside cells and that the treatment of cells with mitochondrial uncoupler or ionomycin can cause more than a 1°C intracellular temperature increase in mammalian cultured cells. Thermogenesis mechanisms in brown adipocytes have been revealed with the aid of intracellular temperature imaging. While there have been no reports on plant intracellular temperature imaging thus far, intracellular temperature imaging is expected to provide a new way to analyze the mechanisms underlying the various activities of plant cells. In this review, I will first summarize the recent progress in the development of fluorescent thermometers and their biological applications. I will then discuss the selection of fluorescent thermometers and experimental setup for the adaptation of intracellular temperature imaging to plant cells. Finally, possible applications of intracellular temperature imaging to investigate plant cell functions will be discussed.

Spatiotemporal Winter Wheat Water Status Assessment Improvement Using a Water Deficit Index Derived from an Unmanned Aerial System in the North China Plain

Agricultural droughts cause a great reduction in winter wheat productivity; therefore, timely and precise irrigation recommendations are needed to alleviate the impact. This study aims to assess drought stress in winter wheat with the use of an unmanned aerial system (UAS) with multispectral and thermal sensors. High-resolution Water Deficit Index (WDI) maps were derived to assess crop drought stress and evaluate winter wheat actual evapotranspiration rate (ET_a). However, the estimation of WDI needs to be improved by using more appropriate vegetation indices as a proximate of the fraction of vegetation cover. The experiments involved six irrigation levels of winter wheat in the harvest years 2019 and 2020 at Luancheng, North China Plain on seasonal and diurnal timescales. Additionally, WDI derived from several vegetation indices (VIs) were compared: near-infrared-, red edge-, and RGB-based. The WDIs derived from different VIs were highly correlated with each other and had similar performances. The WDI had a consistently high correlation to stomatal conductance during the whole season (R² between 0.63-0.99) and the correlation was the highest in the middle of the growing season. On the contrary, the correlation between WDI and leaf water potential increased as the season progressed with R² up to 0.99. Additionally, WDI and ET_a had a strong connection to soil water status with R² up to 0.93 to the fraction of transpirable soil water and 0.94 to the soil water change at 2 m depth at the hourly rate. The results indicated that WDI derived from multispectral and thermal sensors was a reliable factor in assessing the water status of the crop for irrigation scheduling.

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.

High-throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress

Interannual and local fluctuations in wheat crop yield are mostly explained by abiotic constraints. Heatwaves and drought, which are among the top stressors, commonly co-occur, and their frequency is increasing with global climate change. High-throughput methods were optimized to phenotype wheat plants under controlled water deficit and high temperature, with the aim to identify phenotypic traits conferring adaptative stress responses. Wheat plants of 10 genotypes were grown in a fully automated plant facility under 25/18 °C day/night for 30 d, and then the temperature was increased for 7 d (38/31 °C day/night) while maintaining half of the plants well irrigated and half at 30% field capacity. Thermal and multispectral images and pot weights were registered twice daily. At the end of the experiment, key metabolites and enzyme activities from carbohydrate and antioxidant metabolism were quantified. Regression machine learning models were successfully established to predict plant biomass using image-extracted parameters. Evapotranspiration traits expressed significant genotype-environment interactions (G×E) when acclimatization to stress was continuously monitored. Consequently, transpiration efficiency was essential to maintain the balance between water-saving strategies and biomass production in wheat under water deficit and high temperature. Stress tolerance included changes in carbohydrate metabolism, particularly in the sucrolytic and glycolytic pathways, and in antioxidant metabolism. The observed genetic differences in sensitivity to high temperature and water deficit can be exploited in breeding programmes to improve wheat resilience to climate change.

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 (T_leaf ), yet T_leaf 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 T_leaf , 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 (T_rosette ) and artificial reference temperature (T_wet, T_dry ). T_rosette and several water stress indices were tested for their relation to E. Among the microclimatic growth conditions tested, RWSI₁ ([T_rosette  - T_wet ]/[T_dry  - T_wet ]) and RWSI₂ ([T_dry  - T_rosette ]/[T_dry  - T_wet ]) were well linearly-related to E, irrespective of the light environment, while the sole use of either T_wet or T_dry in different combinations with T_rosette returned less accurate results. This study provides evidence that selected combinations of T_rosette , T_dry , and T_wet can be utilized to assess E under water stress irrespective of the light environment.

Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane

Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder’s equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.

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.

Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits

Advances in sugarcane breeding have contributed significantly to improvements in agronomic traits and crop yield. However, the growing global demand for sugar and biofuel in the context of climate change requires further improvements in cane and sugar yields. Attempts to achieve the desired rates of genetic gain in sugarcane by conventional breeding means are difficult as many agronomic traits are genetically complex and polygenic, with each gene exerting small effects. Unlike those of many other crops, the sugarcane genome is highly heterozygous due to its autopolyploid nature, which further hinders the development of a comprehensive genetic map. Despite these limitations, many superior agronomic traits/genes for higher cane yield, sugar production, and disease/pest resistance have been identified through the mapping of quantitative trait loci, genome-wide association studies, and transcriptome approaches. Improvements in traits controlled by one or two loci are relatively easy to achieve; however, this is not the case for traits governed by many genes. Many desirable phenotypic traits are controlled by quantitative trait nucleotides (QTNs) with small and variable effects. Assembling these desired QTNs by conventional breeding methods is time consuming and inefficient due to genetic drift. However, recent developments in genomics selection (GS) have allowed sugarcane researchers to select and accumulate desirable alleles imparting superior traits as GS is based on genomic estimated breeding values, which substantially increases the selection efficiency and genetic gain in sugarcane breeding programs. Next-generation sequencing techniques coupled with genome-editing technologies have provided new vistas in harnessing the sugarcane genome to look for desirable agronomic traits such as erect canopy, leaf angle, prolonged greening, high biomass, deep root system, and the non-flowering nature of the crop. Many desirable cane-yielding traits, such as single cane weight, numbers of tillers, numbers of millable canes, as well as cane quality traits, such as sucrose and sugar yield, have been explored using these recent biotechnological tools. This review will focus on the recent advances in sugarcane genomics related to genetic gain and the identification of favorable alleles for superior agronomic traits for further utilization in sugarcane breeding programs.

The role of silicon in regulating physiological and biochemical mechanisms of contrasting bread wheat cultivars under terminal drought and heat stress environments

The individual and cumulative effects of drought stress (DS) and heat stress (HS) are the primary cause of grain yield (GY) reduction in a rainfed agricultural system. Crop failures due to DS and HS are predicted to increase in the coming years due to increasingly severe weather events. Plant available silicon (Si, H4SiO4) has been widely reported for its beneficial effects on plant development, productivity, and attenuating physiological and biochemical impairments caused by various abiotic stresses. The current study investigated the impact of pre-sowing Si treatment on six contrasting wheat cultivars (four drought and heat stress-tolerant and two drought and heat stress-susceptible) under individual and combined effects of drought and heat stress at an early grain-filling stage. DS, HS, and drought-heat combined stress (DHS) significantly (p < 0.05) altered morpho-physiological and biochemical attributes in susceptible and tolerant wheat cultivars. However, results showed that Si treatment significantly improved various stress-affected morpho-physiological and biochemical traits, including GY (>40%) and yield components. Si treatment significantly (p < 0.001) increased the reactive oxygen species (ROS) scavenging antioxidant activities at the cellular level, which is linked with higher abiotic stress tolerance in wheat. With Si treatment, osmolytes concentration increased significantly by >50% in tolerant and susceptible wheat cultivars. Similarly, computational water stress indices (canopy temperature, crop water stress index, and canopy temperature depression) also improved with Si treatment under DS, HS, and DHS in susceptible and tolerant wheat cultivars. The study concludes that Si treatment has the potential to mitigate the detrimental effects of individual and combined stress of DS, HS, and DHS at an early grain-filling stage in susceptible and tolerant wheat cultivars in a controlled environment. These findings also provide a foundation for future research to investigate Si-induced tolerance mechanisms in susceptible and tolerant wheat cultivars at the molecular level.

Physio-morphological traits and osmoregulation strategies of hybrid maize (Zea mays) at the seedling stage in response to water-deficit stress

Drought has been identified as a major factor restricting maize productivity worldwide, especially in the rainfed areas. The objective of the present study was to investigate the physiological adaptation strategies and sugar-related gene expression levels in three maize (Zea mays L.) genotypes with different drought tolerance abilities (Suwan4452, drought tolerant as a positive check; S7328, drought susceptible as a negative check; Pac339, drought susceptible) at the seedling stage. Ten-day old seedlings of maize genotypes were subjected to (i) well-watered (WW) or control and (ii) water-deficit (WD) conditions. Leaf osmotic potential of cv. S7328 under WD was significantly decreased by 1.35-1.45 folds compared with cv. Pac339 under WW, whereas it was retained in cv. Suwan4452, which utilized total soluble sugars as the major osmolytes for maintaining leaf greenness, F_v/F_m, Φ_PSII, and stomatal function (P_n, net photosynthetic rate; g_s, stomatal conductance; and E, transpiration rate). Interestingly, sucrose degradation (65% over the control) in cv. Pac339 under WD was evident in relation to the downregulation of the ZmSPS1 level, whereas glucose enrichment (1.65 folds over the control) was observed in relation to the upregulation of ZmSPS1 and ZmSUS1. Moreover, CWSI (crop water stress index), calculated from leaf temperature of stressed plants, was negatively correlated with E, g_s, and P_n. Overall, growth characteristics, aboveground and belowground parts, in the drought-susceptible cv. Pac339 and cv. S7328, were significantly decreased (> 25% over the control), whereas these parameters in the drought-tolerant cv. Suwan4452 were unaffected. The study validates the use of leaf temperature, CWSI, P_n, g_s, and E as sensitive parameters and overall growth characters as effective indices for drought tolerance screening in maize genotypes at the seedling stage. However, further experiments are required to validate the results observed in this study under field conditions.

Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images

In some applications of thermography, spatial orientation of the thermal infrared information can be desirable. By the photogrammetric processing of thermal infrared (TIR) images, it is possible to create 2D and 3D results augmented by thermal infrared information. On the augmented 2D and 3D results, it is possible to locate thermal occurrences in the coordinate system and to determine their scale, length, area or volume. However, photogrammetric processing of TIR images is difficult due to negative factors which are caused by the natural character of TIR images. Among the negative factors are the lower resolution of TIR images compared to RGB images and lack of visible features on the TIR images. To eliminate these negative factors, two methods of photogrammetric co-processing of TIR and RGB images were designed. Both methods require a fixed system of TIR and RGB cameras and for each TIR image a corresponding RGB image must be captured. One of the methods was termed sharpening and the result of this method is mainly an augmented orthophoto, and an augmented texture of the 3D model. The second method was termed reprojection and the result of this method is a point cloud augmented by thermal infrared information. The details of the designed methods, as well as the experiments related to the methods, are presented in this article.

Chemical Approaches for Improving Plant Water Use

Agricultural productivity in rain-fed crops has been threatened in recent decades due to increased instances of drought and diminishing freshwater resources. This has led to the development of novel chemical and genetic approaches for improving plant water use efficiency. Agrochemical water-banking with the aid of synthetic mimics of phytohormone abscisic acid (ABA) is one such approach, whereby plant transpiration can be chemically tuned to ensure water availability during critical stages of growth. Here, we describe the use of infrared thermography, a noninvasive quantitative technique to evaluate antitranspirant efficacy of existing ABA receptor agonists in crops such as wheat and tomato.

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO₂ . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

Kaolin Film Increases Gas Exchange Parameters of Coffee Seedlings During Transference From Nursery to Full Sunlight

Increases in water use efficiency (WUE) and the reduction of negative impacts of high temperatures associated with high solar radiation are being achieved with the application of fine particle film of calcined and purified kaolin (KF) on the leaves and fruits of various plant species. KF was applied on young Coffea arabica and Coffea canephora plants before their transition from nursery to full sunlight during autumn and summer. The effects of KF were evaluated through the responses of leaf temperature (T_leaf), net CO₂ assimilation rate (A), stomatal conductance (g _s), transpiration (E), WUE, crop water stress index (CWSI), index of relative stomatal conductance (I_g), initial fluorescence (F₀), and photosynthetic index (PI) in the first 2-3 weeks after the plant transitions to the full sun. All measurements were performed at midday. In Coffea plants, KF decreased the T_leaf up to 6.7°C/5.6°C and reduced the CWSI. The plants that were not protected with KF showed lower A, g _s, E, and I_g than those protected with KF. C. canephora plants protected with KF achieved higher WUE compared with those not protected by 11.23% in autumn and 95.58% in summer. In both Coffea sp., KF application reduced F₀, indicating reduced physical dissociation of the PSII reaction centers from the light-harvesting system, which was supported with increased PI. The use of KF can be recommended as a management strategy in the transition of Coffea seedlings from the nursery shade to the full sunlight, to protect leaves against the excessive solar radiation and high temperatures, especially in C. canephora during the summer.

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

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

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.

Sensing stress responses in potato with whole-plant redox imaging

Environmental stresses are among the major factors that limit crop productivity and plant growth. Various nondestructive approaches for monitoring plant stress states have been developed. However, early sensing of the initial biochemical events during stress responses remains a significant challenge. In this work, we established whole-plant redox imaging using potato (Solanum tuberosum) plants expressing a chloroplast-targeted redox-sensitive green fluorescence protein 2 (roGFP2), which reports the glutathione redox potential (EGSH). Ratiometric imaging analysis demonstrated the probe response to redox perturbations induced by H2O2, DTT, or a GSH biosynthesis inhibitor. We mapped alterations in the chloroplast EGSH under several stress conditions including, high-light (HL), cold, and drought. An extremely high increase in chloroplast EGSH was observed under the combination of HL and low temperatures, conditions that specifically induce PSI photoinhibition. Intriguingly, we noted a higher reduced state in newly developed compared with mature leaves under steady-state and stress conditions, suggesting a graded stress sensitivity as part of the plant strategies for coping with stress. The presented observations suggest that whole-plant redox imaging can serve as a powerful tool for the basic understanding of plant stress responses and applied agricultural research, such as toward improving phenotyping capabilities in breeding programs and early detection of stress responses in the field.

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers.

Stem canker caused by Phomopsis spp. Induces changes in polyamine levels and chlorophyll fluorescence parameters in pecan leaves

Pecan plants are attacked by the fungus Phomopsis spp. that causes stem canker, a serious and emerging disease in commercial orchards. Stem canker, which has been reported in several countries, negatively affects tree canopy health, eventually leading to production losses. The purpose of this study was to inquire into the physiology of pecan plants under stem canker attack by Phomopsis spp. To this end, pecan plants were inoculated with an isolate of Phomopsis spp. and several parameters, such as polyamines, proline, sugars, starch, chlorophyll fluorescence and canopy temperature were analysed. Under artificial inoculation, a high disease incidence was observed with symptoms similar to those in plants showing stem canker under field conditions. Furthermore, the infected stem showed dead tissue with brown necrotic discolouration in the xylem tissue. The free polyamines putrescine, spermidine, and spermine were detected and their levels decreased as leaves aged in the infected plants with respect to the controls. Chlorophyll fluorescence parameters, such as Sm, ψEO, and QbRC decreased under plant infection and therefore the K-band increased. Canopy temperature and proline content increased in the infected plants with respect to the controls while sugar content decreased. These data suggest that stem canker caused by Phomopsis spp. induces physiological changes that are similar to those observed in plants under drought stress. To our knowledge, this is the first study that documents the physiological and biochemical effects derived from pecan-Phomopsis interaction.

Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.

High night temperature effects on wheat and rice: Current status and way forward

Rapid increases in minimum night temperature than in maximum day temperature is predicted to continue, posing significant challenges to crop productivity. Rice and wheat are two major staples that are sensitive to high night-temperature (HNT) stress. This review aims to (i) systematically compare the grain yield responses of rice and wheat exposed to HNT stress across scales, and (ii) understand the physiological and biochemical responses that affect grain yield and quality. To achieve this, we combined a synthesis of current literature on HNT effects on rice and wheat with information from a series of independent experiments we conducted across scales, using a common set of genetic materials to avoid confounding our findings with differences in genetic background. In addition, we explored HNT-induced alterations in physiological mechanisms including carbon balance, source-sink metabolite changes and reactive oxygen species. Impacts of HNT on grain developmental dynamics focused on grain-filling duration, post-flowering senescence, changes in grain starch and protein composition, starch metabolism enzymes and chalk formation in rice grains are summarized. Finally, we highlight the need for high-throughput field-based phenotyping facilities for improved assessment of large-diversity panels and mapping populations to aid breeding for increased resilience to HNT in crops.

Imaging canopy temperature: shedding (thermal) light on ecosystem processes

Canopy temperature T_can is a key driver of plant function that emerges as a result of interacting biotic and abiotic processes and properties. However, understanding controls on T_can and forecasting canopy responses to weather extremes and climate change are difficult due to sparse measurements of T_can at appropriate spatial and temporal scales. Burgeoning observations of T_can from thermal cameras enable evaluation of energy budget theory and better understanding of how environmental controls, leaf traits and canopy structure influence temperature patterns. The canopy scale is relevant for connecting to remote sensing and testing biosphere model predictions. We anticipate that future breakthroughs in understanding of ecosystem responses to climate change will result from multiscale observations of T_can across a range of ecosystems.

Photoprotection and optimization of sucrose usage contribute to faster recovery of photosynthesis after water deficit at high temperatures in wheat

Plants are increasingly exposed to events of elevated temperature and water deficit, which threaten crop productivity. Understanding the ability to rapidly recover from abiotic stress, restoring carbon assimilation and biomass production, is important to unravel crop climate resilience. This study compared the photosynthetic performance of two Triticum aestivum L. cultivars, Sokoll and Paragon, adapted to the climate of Mexico and UK, respectively, exposed to 1-week water deficit and high temperatures, in isolation or combination. Measurements included photosynthetic assimilation rate, stomatal conductance, in vitro activities of Rubisco (EC 4.1.1.39) and invertase (INV, EC 3.2.1.26), antioxidant capacity and chlorophyll a fluorescence. In both genotypes, under elevated temperatures and water deficit (WD38°C), the photosynthetic limitations were mainly due to stomatal restrictions and to a decrease in the electron transport rate. Chlorophyll a fluorescence parameters clearly indicate differences between the two genotypes in the photoprotection when subjected to WD38°C and showed faster recovery of Paragon after stress relief. The activity of the cytosolic invertase (CytINV) under these stress conditions was strongly related to the fast photosynthesis recovery of Paragon. Taken together, the results suggest that optimal sucrose export/utilization and increased photoprotection of the electron transport machinery are important components to limit yield fluctuations due to water shortage and elevated temperatures.

The genetic basis of water-use efficiency and yield in lettuce

BACKGROUND

Water supply limits agricultural productivity of many crops including lettuce. Identifying cultivars within crop species that can maintain productivity with reduced water supply is a significant challenge, but central to developing resilient crops for future water-limited climates. We investigated traits known to be related to water-use efficiency (WUE) and yield in lettuce, a globally important leafy salad crop, in a recombinant inbred line (RIL) lettuce mapping population, produced from a cross between the cultivated Lactuca sativa L. cv. Salinas and its wild progenitor L. serriola L.

RESULTS

Wild and cultivated lettuce differed in their WUE and we observed transgressive segregation in yield and water-use traits in the RILs. Quantitative trait loci (QTL) analysis identified genomic regions controlling these traits under well-watered and droughted conditions. QTL were detected for carbon isotope discrimination, transpiration, stomatal conductance, leaf temperature and yield, controlling 4-23 % of the phenotypic variation. A QTL hotspot was identified on chromosome 8 that controlled carbon isotope discrimination, stomatal conductance and yield under drought. Several promising candidate genes in this region were associated with WUE, including aquaporins, late embryogenesis abundant proteins, an abscisic acid-responsive element binding protein and glutathione S-transferases involved in redox homeostasis following drought stress were also identified.

CONCLUSIONS

For the first time, we have characterised the genetic basis of WUE of lettuce, a commercially important and water demanding crop. We have identified promising candidate genomic regions determining WUE and yield under well-watered and water-limiting conditions, providing important pre-breeding data for future lettuce selection and breeding where water productivity will be a key target.

Intrinsic non-stomatal resilience to drought of the photosynthetic apparatus in Coffea spp. is strengthened by elevated air [CO2]

Growing water restrictions associated with climate changes constitute daunting challenges to crop performance. This study unveils the impacts of moderate (MWD) or severe (SWD) water deficit, and their interaction with air [CO2], on the photosynthetic apparatus of Coffea canephora Pierre ex A. Froehner cv. Conilon Clone 153 (CL153) and Coffea arabica L. cv. Icatu. Seven year-old potted plants grown under 380 (aCO2) or 700 μl l -1 (eCO2) [CO2] gradually reached predawn water potentials between -1.6 and -2.1 MPa (MWD), and below -3.5 MPa (SWD). Under drought, stomata closure was chiefly related to abscisic acid (ABA) rise. Increasing drought severity progressively affected gas exchange and fluorescence parameters in both genotypes, with non-stomatal limitations becoming gradually dominating, especially regarding the photochemical and biochemical components of CL153 SWD plants. In contrast, Icatu plants were highly tolerant to SWD, with minor, if any, negative impacts on the potential photosynthetic functioning and components (e.g., Amax, Fv/Fm, electron carriers, photosystems (PSs) and ribulose-1,5-bisphosphate carboxylase oxygenase (RuBisCO) activities). Besides, drought-stressed Icatu plants displayed increased abundance of a large set of proteins associated with the photosynthetic apparatus (PSs, light-harvesting complexes, cyclic electron flow, RuBisCO activase) regardless of [CO2]. Single eCO2 did not promote stomatal and photosynthetic down-regulation in both genotypes. Instead, eCO2 increased photosynthetic performance, moderately reinforced photochemical (PSs activity, electron carriers) and biochemical (RuBisCO, ribulose-5-phosphate kinase) components, whereas photoprotective mechanisms and protein abundance remained mostly unaffected. In both genotypes, under MWD, eCO2 superimposition delayed stress severity and promoted photosynthetic functioning with lower energy dissipation and PSII impacts, whereas stomatal closure was decoupled from increases in ABA. In SWD plants, most impacts on the photosynthetic performance were reduced by eCO2, especially in the moderately drought affected CL153 genotype, although maintaining RuBisCO as the most sensitive component, deserving special breeder's attention to improve coffee sustainability under future climate scenarios.

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.

Integration of Visible and Thermal Imagery with an Artificial Neural Network Approach for Robust Forecasting of Canopy Water Content in Rice

A total of 120 rice plant samples were scanned by visible and thermal proximal sensing systems under different water stress levels to evaluate the canopy water content (CWC). The oven-drying method was employed for assessing the canopy’s water state. This CWC is of great importance for irrigation management decisions. The proposed framework is to integrate visible and thermal imaging data using an artificial neural network as a valuable promising implement for accurately estimating the water content of the plant. The RGB-based features included 20 color vegetation indices (VI) and 6 gray level co-occurrence matrix-based texture features (GLCMF). The thermal imaging features were two thermal indicators (T), namely normalized relative canopy temperature (NRCT) and the crop water stress index (CWSI), that were deliberated by plant temperatures. These features were applied with a back-propagation neural network (BPNN) for training the samples with minimal loss on a cross-validation set. Model behavior was affected by filtering high-level features and optimizing hyperparameters of the model. The results indicated that feature-based modeling from both visible and thermal images achieved better performance than features from the individual visible or thermal image. The supreme prediction variables were 21 features: 14VI, 5GLCMF, and 2T. The fusion of color–texture–thermal features greatly improved the precision of water content evaluation (99.40%). Its determination coefficient (R2 = 0.983) was the most satisfied with an RMSE of 0.599. Overall, the methodology of this work can support decision makers and water managers to take effective and timely actions and achieve agricultural water sustainability.

Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests

Reducing agricultural losses is an effective way to sustainably increase agricultural output efficiency to meet our present and future needs for food, fiber, fodder, and fuel. Our ever-improving understanding of the ways in which plants respond to stress, biotic and abiotic, has led to the development of innovative sensing technologies for detecting crop stresses/stressors and deploying efficient measures. This article aims to present the current state of the methodologies applied in the field of agriculture towards the detection of biotic stress in crops. Key sensing methodologies for plant pathogen (or phytopathogen), as well as herbivorous insects/pests are presented, where the working principles are described, and key recent works discussed. The detection methods overviewed for phytopathogen-related stress identification include nucleic acid-based methods, immunological methods, imaging-based techniques, spectroscopic methods, phytohormone biosensing methods, monitoring methods for plant volatiles, and active remote sensing technologies. Whereas the pest-related sensing techniques include machine-vision-based methods, pest acoustic-emission sensors, and volatile organic compound-based stress monitoring methods. Additionally, Comparisons have been made between different sensing techniques as well as recently reported works, where the strengths and limitations are identified. Finally, the prospective future directions for monitoring biotic stress in crops are discussed.

Water-Stressed Plants Do Not Cool: Leaf Surface Temperature of Living Wall Plants under Drought Stress

Urban green infrastructures offer thermal regulation to mitigate urban heat island effects. To gain a better understanding of the cooling ability of transpiring plants at the leaf level, we developed a method to measure the time series of thermal data with a miniaturized, uncalibrated thermal infrared camera. We examined the canopy temperature of four characteristic living wall plants (Heuchera x cultorum, Bergenia cordifolia, Geranium sanguineum, and Brunnera macrophylla) under increasing drought stress and compared them with a well-watered control group. The method proved suitable to evaluate differences in canopy temperature between the different treatments. Leaf temperatures of water-stressed plants were 6 to 8 °C higher than those well-watered, with differences among species. In order to cool through transpiration, vegetation in green infrastructures must be sufficiently supplied with water. Thermal cameras were found to be useful to monitor vertical greening because leaf surface temperature is closely related to drought stress. The usage of thermal cameras mounted on unmanned aerial vehicles could be a rapid and easy monitoring system to cover large façades.

High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement

Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.

The effects of putrescine pre-treatment on osmotic stress responses in drought-tolerant and drought-sensitive wheat seedlings

Recent studies have demonstrated that exogenous polyamines have protective effects under various stress condition. A broader understanding of the role of the polyamine pool fine regulation and the alterations of polyamine-related physiological processes could be obtained by comparing the stress effects in different genotypes. In this study, the impact of pre-treatment with putrescine in response to osmotic stress was investigated in the drought-tolerant Katya and drought-sensitive Zora wheat (Triticum aestivum) cultivars. Photosynthetic performance, in vivo thermoluminescence emission from leaves, leaf temperature, polyamine and salicylic acid levels, contents of osmoprotectants, and activities of antioxidant enzymes in the leaves were investigated not only to reveal differences in the physiological processes associated to drought tolerance, but to highlight the modulating strategies of polyamine metabolism between a drought-tolerant and a drought-sensitive wheat genotype. Results showed that the tolerance of Katya under osmotic stress conditions was characterized by higher photosynthetic ability, stable charge separation across the thylakoid membrane in photosystem II, higher proline accumulation and antioxidant activity. Thermoluminescence also revealed differences between the two varieties - a downshift of the B band and an increase of the afterglow band under osmotic stress in Zora, providing original complementary information to leaf photosynthesis. Katya variety exhibited higher constitutive levels of the signaling molecules putrescine and salicylic acid compared to the sensitive Zora. However, responses to exogenous putrescine were more advantageous for the sensitive variety under PEG treatment, which may be in relation with the decreased catabolism of polyamines, suggesting the increased need for polyamine under stress conditions.

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.