Plant Water Status Monitoring Research Articles

Development of a microwave sensor for the non-invasive detection of plant responses to water stress: A practical application on maize (Zea mays L.)

In this study, a novel microwave sensing system, consisting of a microstrip self-resonant spiral coil inductively coupled to an external concentric planar probe loop, is presented and applied to the non-destructive detection of morpho-physiological plant responses to water stress. The optimised set-up of the proposed sensor ensures a highly sensitive spiral coil, which is a fundamental requirement to derive accurate information on plants' behavioural alterations related to water stress conditions. The proposed microwave sensor was tested it on two potted maize cultivars (Zea mays L.), namely “Cinquantino Bianchi” (CB) and “Scagliolo Frassine” (SF). For each cultivar, half of the samples were maintained at 100% (T100) field capacity while the other half was at 25% (T25) from 46 to 74 Days After Sowing (DAS). The frequency (fr) shift and the amplitude peaks variation of the real component of the external planar probe input impedance (ℜ(Zinput)) were obtained daily by positioning the sensor on the stem. These measured data were related to morpho-physiological parameters destructively acquired at four different growth stages. The resulting linear correlation between the stem's freshwater content (FWCstem) with both fr (r > −0.64) and the amplitude peaks (ℜ (Zinput)) (r > -0.70) provided evidence of the sensor's ability to identify stem dielectric properties' variations between the two water treatments. Concurrently, the sensor response demonstrated the capability to identify changes in the morphology and histology of the stem. Based on preliminary findings, the proposed sensor shows potential for employment in the real-time monitoring of plant water status, contributing to more economically and environmentally sustainable crop management practices. While the current correlations between plant water content and sensor measurements require further refinement to meet the rigorous industrial standards, nevertheless a large-scale adoption can be envisioned by leveraging IoT methodologies.

Spatio-temporal monitoring of plant water status using optical remote sensing data and in situ measurements

Water content as an important physiological status variable in plants, is closely linked to transpiration, photosynthesis, water stress, and biomass productivity. Obtaining plant water information that provides sufficient spatial and temporal resolution for such applications remains a challenge. In this study, the data distribution space of fractional vegetation cover (FVC) and shortwave infrared transformed reflectance (STR) with linear and nonlinear edges was used to monitor plant water content. Four indicators of plant water status, equivalent water thickness (EWT), leaf water content (LWC), relative water content (RWC), and leaf water potential (LWP), were measured in a 10-ha corn field at the Karkaj Agricultural Research Station of Tabriz University, Tabriz, Iran. Furthermore, the Sentinel-2 Level 2 prototype processor (SL2P) was utilized to estimate EWT and compare the results with FVC-STR space. The FVC-STR space with nonlinear edges (FSNLE) provided better estimation accuracy of plant water status than the FVC-STR space with linear edges (FSLE). The root mean square errors of the EWT, LWC, RWC and LWP estimates for FSLE were 0.00306 g/cm2, 4.03 %, 6.56 %, and 0.38 bar, respectively, while those for FSNLE were 0.00303 g/cm2, 3.75 %, 5.57 %, and 0.37 bar, respectively. In addition, the R2 value for FSNLE was higher than that for FSLE (0.43–0.70 vs. 0.39–0.64). The RMSE and R2 for SL2P were 0.0041 g/cm2 and 0.401, respectively. Among the four measured indicators, the highest and lowest estimation accuracies in both FSLE and FSNLE were obtained with EWT and RWC, respectively. It can be concluded that FVC-STR space model based on Sentinel-2 imagery data provide acceptable accuracy for estimating plant water content. The FVC-STR space with nonlinear edges provided a better estimation accuracy for plant water indicators than the FVC-STR space with linear edges.

Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies.

Best Procedures for Leaf and Stem Water Potential Measurements in Grapevine: Cultivar and Water Status Matter.

The pressure chamber is the most used tool for plant water status monitoring. However, species/cultivar and seasonal effects on protocols for reliable water potential determination have not been properly tested. In four grapevine cultivars and two times of the season (early season, Es; late season, Ls, under moderate drought), we assessed the maximum sample storage time before leaf water potential (Ψleaf) measurements and the minimum equilibration time for stem water potential (Ψstem) determination, taking 24 h leaf cover as control. In 'Pinot gris', Ψleaf already decreased after 1 h leaf storage in both campaigns, dropping by 0.4/0.5 MPa after 3 h, while in 'Refosk', it decreased by 0.1 MPa after 1 and 2 h in Es and Ls, respectively. In 'Merlot' and 'Merlot Kanthus', even 3 h storage did not affect Ψleaf. In Es, the minimum Ψstem equilibration was 1 h for 'Refošk' and 10 min for 'Pinot gris' and 'Merlot'. In Ls, 'Merlot Kanthus' required more than 2 h equilibration, while 1 h to 10 min was sufficient for the other cultivars. The observed cultivar and seasonal differences indicate that the proposed tests should be routinely performed prior to experiments to define ad hoc procedures for water status determination.

Impact of a DANA Event on the Thermal Response of Nectarine Trees

This field experiment focuses on the effects of a heavy rainfall event (DANA, depresión aislada en niveles altos) that occurred on 12-14 September 2019 (DOY, Day of the year, 255-257), in southern Spain on plant water status and the thermal response of nectarine trees. Two irrigation treatments were applied during the summer-autumn postharvest period (DOY 158-329): full-irrigated (CTL) and non-irrigated (DRY). Volumetric soil water content (θv), air temperature (Ta) and canopy temperature (Tc) were monitored in real-time and the crop water stress index (CWSI) was calculated. The difference in Tc between the DRY and CTL treatments (Tc' - Tc) is proposed as a new thermal indicator. Stem water potential (Ψstem) and leaf gas exchange measurements were recorded on representative days. During the DANA event, only the Tc measured by the infrared radiometer sensors could be monitored. Therefore, the effects of the DANA forced the soil water content sensors to be switched off, which prevented Ψstem and leaf gas exchange determinations from DOY 255 to 275. Before the DANA event, withholding irrigation caused a gradual decrease in the soil and plant water status in the DRY treatment. Significant differences appeared between treatments in the studied thermal indexes. Moreover, Tc' - Tc was more sensitive than Tc - Ta in assessing nectarine water stress. The effects of the DANA reduced these differences, suggesting different baselines for the calculation of CWSI. In this respect, the relationship Tc - Ta vs. VPD improved the coefficient of determination after the DANA event in full-irrigated trees. Similar values of Ψstem and leaf gas exchange were found in both treatments after the DANA event, even though thermal indexes showed some significant differences. In addition, the strong relationship found between Tc - Ta and CWSI vs. Ψstem worsened after DANA occurred, revealing a lower sensitivity of Ψstem compared to canopy temperature to accurately assess nectarine water status in these saturated soil conditions. This research underlined the robustness of infrared thermography to continuously monitor plant water status under these extreme weather conditions.

Assessment of trunk microtensiometer as a novel biosensor to continuously monitor plant water status in nectarine trees.

The objective of this work was to validate the trunk water potential (Ψtrunk), using emerged microtensiometer devices, as a potential biosensor to ascertain plant water status in field-grown nectarine trees. During the summer of 2022, trees were subjected to different irrigation protocols based on maximum allowed depletion (MAD), automatically managed by real-time soil water content values measured by capacitance probes. Three percentages of depletion of available soil water (α) were imposed: (i) α=10% (MAD=27.5%); (ii) α=50% (MAD=21.5%); and (iii) α=100%, no-irrigation until Ψstem reached -2.0 MPa. Thereafter, irrigation was recovered to the maximum water requirement of the crop. Seasonal and diurnal patterns of indicators of water status in the soil-plant-atmosphere continuum (SPAC) were characterised, including air and soil water potentials, pressure chamber-derived stem (Ψstem) and leaf (Ψleaf) water potentials, and leaf gas exchange, together with Ψtrunk. Continuous measurements of Ψtrunk served as a promising indicator to determine plant water status. There was a strong linear relationship between Ψtrunk vs. Ψstem (R2 = 0.86, p<0.001), while it was not significant between Ψtrunk vs. Ψleaf (R2 = 0.37, p>0.05). A mean gradient of 0.3 and 1.8 MPa was observed between Ψtrunk vs.Ψstem and Ψleaf, respectively. In addition, Ψtrunk was the best matched to the soil matric potential. The main finding of this work points to the potential use of trunk microtensiometer as a valuable biosensor for monitoring the water status of nectarine trees. Also, trunk water potential agreed with the automated soil-based irrigation protocols implemented.

Comparing Different Light Use Efficiency Models to Estimate the Gross Primary Productivity of a Cork Oak Plantation in Northern China

Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model’s accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model’s performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23–52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49–65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-PEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future.

A LoRaWAN IoT System for Smart Agriculture for Vine Water Status Determination

In view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, monitoring of vine water status is done through expensive and time-consuming methods that do not allow continuous monitoring, which is especially inconvenient in places with difficult access. The aim of the developed work was to install three groups of sensors (Environmental, Plant and Soil) in a vineyard and connect them through LoRaWAN protocol for data transmission. The results demonstrate that the implemented system is capable of continuous data communication without data loss. The reduced cost and superior range of LoRaWAN compared to WiFi or Bluetooth is especially important for applications in remote areas where cellular networks have little coverage. Altogether, this methodology provides a remote, continuous and more effective method to monitor plant water status and is capable of supporting producers in more efficient management of their farms and water resources.

Spatial Prediction Model of Plant Water Status of an Olive Orchard (Olea europaea L.) cv. Arbequina Under Semiarid Conditions in the Central Valley of Chile

The Olive (Olea europaea L.) is a typical fruit tree of Mediterranean areas characterized by high-quality oil production and high tolerance to water deficit. Due to worldwide water scarcity in Mediterranean regions, it becomes indispensable to monitor plant water status, in example, through xylem water potential (&amp;Psi;x). Unfortunately, measurement is difficult to perform with high spatial resolution at field scale (&amp;gt; 50 measurements per hectare), due to the large amount of manpower required in the prosses which turned this technique into a high-cost solution. This situation drastically hinders its applicability in large production areas. Thus, the objective of this research is implementing a spatial prediction model of plant water status in an olive orchard, using a single &amp;Psi;x measurement performed in a reference site over the orchard. The experimental site was established in 2.2 hectares of commercial olive trees in the Pencahue valley located in the Maule region (Chile) during the 2013/14 growing season. Measurements of &amp;Psi;x were performed at key phenological stages of olive trees. The proposed methodology allowed to estimate the behavior of &amp;Psi;x in unsampled olive trees from reference site measurements, with an average spatial error less than &amp;plusmn;0.6 MPa and correlation of 0.8 (R2) ratifying the high spatial dependence between different sites sampled at field scale. Therefore, distribution of spatial variability would be adequate for the application of irrigation in homogeneous management zones, facilitating water management practices in clearly identified zones within the olive orchard under study.

Monitoring plant water status via static uniaxial compression of the leaf lamina.

Turgor pressure is an essential, but difficult to measure indicator of plant water status. Turgor has been quantified by localized compression of cells or tissues, but a simple method to perform these measurements is lacking. We hypothesized that changes in leaf turgidity can be monitored by uniaxially compressing the leaf lamina and measuring the mechanical stress under a constrained thickness (stress relaxation)and that changes in leaf water content can be monitored by measuring the leaf thickness under constant mechanical stress. Using a simple, custom-built leaf squeeze-flow rheometer, we performed different compression tests on leaves from 13 plant species. The mechanical stress measured during stress relaxation was correlated with leaf bulk turgor pressure (R2 > 0.95) and thus with balancing pressure (R2 > 0.94); the leaf thickness measured under constant mechanical stress was correlated with relative water content (R2 > 0.74). The coefficients of these relationships were related to the leaf bulk osmotic pressure at the turgor-loss point. An idealized average-cell model suggests that, under isothermal conditions, the stationary bulk modulus during compression is largely determined by the bulk osmotic pressure. Our study presents an inexpensive, accessibleand automatable method to monitor plant water status noninvasively.

On-line monitoring of plant water status: Validation of a novel sensor based on photon attenuation of radiation through the leaf

Non-destructive real-time monitoring of leaf water status is important for precision irrigation practice to increase water productivity and reduce its use. To this end, we tested and validated a novel leaf sensor (Leaf Water Meter, LWM), based on the photon attenuation during the passage of the light through the leaf, to monitor plant water status. Four woody species were subjected to multiple cycles of dehydration and re-hydration, and the signals recorded by the LWM were compared with classical measurements of plant water relations (relative water content and water potential). A good agreement between the signals recorded by LWM sensor and the destructive measurements, throughout the repeated water stress and rewatering cycles, was found across all species. These results demonstrate that LWM sensor is a sensitive, non-destructive and easy-to-handle device to reliably monitor in continuous fashion leaf water status. In conclusion, this sensor may be considered a promising tool for smart irrigation scheduling in precision agriculture context to decrease water wastage in light of global change and increasing conflicts over water demand.

Remote Sensing for Plant Water Content Monitoring: A Review

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

Monitoring water status in apple trees using a sensitive morning crop water stress index*

AbstractThe crop water stress index (CWSI) has shown to be a good indicator of water status in fruit trees. Conventional CWSI measured over solar noon is widely used to monitor plant water status. This study compared the theoretical CWSI averaged over morning hours, CWSImo, and over solar noon hours, CWSImd, of apple trees. This study also assessed their sensitivity to the changes in soil water status (soil water deficit [SWD, %] and soil water potential [SWP, kPa]) at different root zone depths. Four different types of commercial and experimental apple orchards with different characteristics growing under semi‐arid conditions in Washington State, United States, were chosen to assess CWSImo against CWSImd sensitivity to soil water status. In some of these locations, soil water status ranged from fully watered to severely stressed (low water deficit to high water deficit). Ground‐based thermal and microclimate measurements were recorded continuously at 10‐s intervals and acquired for the 15‐min average on a daily basis throughout the study period. The linear relationship between SWD and CWSImo at the effective root zone depth (average of 0–150 mm and 0–460 mm) resulted in a slightly higher correlation (R2 = 0.42–0.64, p &lt; .001) compared to the traditional CWSImd (R2 = 0.32–0.60, p &lt; 0.001). The correlation between CWSImo and SWP (R2 = 0.38–0.69, p &lt; 0.001) was found to be larger than the correlation between CWSImo and SWD, while SWP was less correlated with CWSImd (R2 = 0.33–0.55, p &lt; 0.001). Interestingly, a better relationship between soil water status and both CWSImo and CWSImd was observed using a nadir view orientation thermal measurement. The CWSImo showed higher sensitivity to SWP than SWD.

Evapotranspiration of Urban Landscape Trees and Turfgrass in an Arid Environment: Potential Trade-offs in the Landscape

Irrigation in arid urban landscapes can use significant amounts of water. Water conservation must be based on plant species and the ability to meet plant water requirements while minimizing overirrigation. However, actual evapotranspiration (ET) estimates for landscape trees and turfgrass in arid environments are poorly documented, especially direct comparisons to assess potential trade-offs. We conducted research to quantify ET of 10 common landscape tree species grown in southern Nevada and compared these values with the ET of both a warm season and cool season turfgrass species. The trees were grown in a plot with a high-density planting (256 trees/ha). A complete morphological assessment was made on each tree, and monitoring of plant water status was conducted monthly. ET was quantified with a hydrologic balance approach, irrigating based on the previous week’s ET to eliminate a drainage component. Transpiration was estimated with sap-flow sensors, and evaporation was estimated by difference. Although ET in liters revealed no statistical difference based on species, there were many significant differences in tree morphological parameters (P &lt; 0.05), such as found with basal canopy area. When ET was converted to centimeters based on standardizing the ET on a basal canopy area basis, statistically higher ET values (P &lt; 0.05) were generated for three of the trees (Lagerstroemia indica, Gleditsia tricanthos, and Fraxinus velutina ‘Modesto’). A clear separation of all tree ET values (lower ET) with turfgrass ET occurred (P &lt; 0.001), with the exception of L. indica. Backward regression analysis revealed that all morphological and physiological parameters were eliminated with the exception of percent cover in predicting ET (cm, R2 = 0.88, P &lt; 0.001). In addition, a highly curvilinear relationship existed between decreasing percent tree cover and ET on a basal canopy area basis (R2 = 0.96, P &lt; 0.001), revealing that smaller trees located within the plot had significantly higher ET (centimeters). Tree-to-grass water use ratios demonstrated that all species except L. indica had ratios significantly below 1.0, indicating that on the basis of this study, landscapes dominated by mature trees irrigated at ET would have lower water use rates than similar areas planted to turfgrass, with the exception of the smaller L. indica. The results suggest that the smaller trees within the higher planting density plot were partially released from a negative feedback on transpiration that occurred in the larger trees based on reduced canopy atmospheric coupling.

Leaf water status monitoring by scattering effects at terahertz frequencies.

The applications of terahertz (THz) radiation for plant water status monitoring require systematic studies on interaction of THz wave and plants. Here, we present theoretical investigations on scattering behavior of THz waves reflected by and transmitting through a plant leaf under different water content. A theoretical model combining integral equation and radiative transfer theory is presented to fit the measured data. Good agreement confirms the availability of the model for water status evaluation when variation of leaf thickness and surface roughness is considered. We investigate the applicability of THz waves for water status monitoring in reflection and transmission geometries under different temperatures, salinities and polarizations.

Feasibility of Low-Cost Thermal Imaging for Monitoring Water Stress in Young and Mature Sweet Cherry Trees

Infrared thermography has been introduced as an affordable tool for plant water status monitoring, especially in regions where water availability is the main limiting factor in agricultural production. This paper outlines the potential applications of low-cost thermal imaging devices to evaluate the water status of young and mature sweet cherry trees (Prunus avium L.) submitted to water stress. Two treatments per plot were assayed: (i) a control treatment irrigated to ensure non-limiting soil water conditions; and (ii) a water-stress treatment. The seasonal evolution of the temperature of the canopy (Tc) and the difference between Tc and air temperature (ΔT) were compared and three thermal indices were calculated: crop water stress index (CWSI), degrees above control treatment (DAC) and degrees above non-water-stressed baseline (DANS). Midday stem water potential (Ψstem) was used as the reference indicator of water stress and linear relationships of Tc, ΔT, CWSI, DAC and DANS with Ψstem were discussed in order to assess their sensitivity to quantify water stress. CWSI and DANS exhibited strong relationships with Ψstem and two regression lines to young and mature trees were found. The promising results obtained highlight that using low-cost infrared thermal devices can be used to determine the plant water status in sweet cherry trees.

Integral water capacity (IWC) and least limiting water range (LLWR): prediction using plant growth indices and soil properties.

Soil water availability is an important field of study in soil water and plant relationship. Least limiting water range (LLWR) and integral water capacity (IWC) are two important concepts which are used for water availability to plant. LLWR is determined from four moisture coefficients (θ AFP, θ FC, θ SR, θ PWP) that are the soil water contents 10% air-filled porosity (AFP), at field water capacity (FC), 2MPa penetration resistance (SR), and permanent wilting point (PWP), respectively. The computation is dependent on critical values, so IWC was introduced to avoid using the critical limits that sharply rises in a cut-off from 0 to 1 at the wet end of water release curve or sharply falls from 1 to 0 at the dry side in the previous concepts of water availability for plant. IWC is the integral of differential water capacity function (C(h)) in the amplitude of 0 to infinity soil matric potential (h) multiplied by some weighting functions (ω i (h)) each considering the effect of various soil limitations on water availability to plants. Up to now, the effect of different soil attributes and the tillage treatments have been reviewed on LLWR. The effect of soil various physical and chemical limitations such as soil hydraulic conductivity (K(h)), aeration, SR, and salinity has been considered on IWC computation. LLWR and especially IWC have been seldom studied using plant real response. Results of few studies about LLWR and IWC using stomatal conductance and canopy temperature showed that their values were considerably different with those computed based on previously introduced critical limits for LLWR and weighting functions for IWC. These differences indicate that the critical limits proposed by da Silva et al. (Soil Sci Soc Am J 58:1775-1781, 1994) and weighting functions by Groenevelt et al. (Aust J Soil Res 39:577-598, 2001) may not be applied indiscriminately for all plants and should to be modified according to plant response. Physiological characteristics like transpiration and photosynthesis rate, chlorophyll index, leaf water potential, and relative water content also could be appropriate indices for monitoring plant water status and computation the real value of LLWR and IWC in the field or greenhouse for various types of plants.

Effect of micro climatic indices on the accuracy of thermographic plant water status monitoring, case study of a semi-arid area

ABSTRACT Plant water stress can be remotely monitored by means of thermography due to its increasing effect on plant temperature. For a more accurate estimation of plant water status, its temperature should be normalised against environmental conditions. In this paper, the aim was to investigate a recently proposed method of normalisation using artificial reference surfaces, different measurement times in a day and different capturing directions regarding to solar trail. At three different times of the day, frontal images were captured from the sunlit side of the olive canopies in two azimuth directions. Two different stress indices of crop water stress index (CWSI) and stomatal conductance index (IG) were calculated using Tc and the temperatures of wet and dry reference surfaces. Trees in different stress levels discriminated better via mean canopy temperature (Tc) of morning measurements. At this time, Tc also provided higher correlated indices with stomatal conductance (SC) rather than those indices calculated based on Tc measured at noon and afternoon. Comparing two measurement directions, the first direction provided Tc with higher correlated indices at morning but the second direction at afternoon. In the most of the measurement conditions, stress indices calculated with air temperature plus five degrees as dry reference temperature had higher correlation with SC.

Learned features of leaf phenotype to monitor maize water status in the fields

Water stress significantly influences normal maize growth. Fast and effective maize water stress detection is of great help to monitor the plant status and provide scientific guidance for crop irrigation. Most of the methods are based on manual measurements of soil water content, or laboratory imaging techniques, such as hyperspectral and thermal images at plant level. With the collection of 656 original maize plant images under natural environment, a novel maize leaf image dataset with different water stress levels (well-watered, reduced-watered and drought-stressed) was constructed. This paper considers maize water status detection as a fine-grained classification problem using local leaf images. Inspired by deep learning, a convolutional neural network (CNN) is applied for the first time to maize water stress recognition. In the designed CNN architecture, feature maps from different convolutional layers are merged. Through visualization and importance analysis of the multi-scale feature maps, several specific feature maps are selected as learned features, which provide a strong discrimination ability. An SVM classifier is finally trained using the feature representation as inputs. Compared with existing techniques, the proposed method achieves the satisfying classification performance with an accuracy of 88.41%. This study also provides a quantitative measure of water stress degree using a regression model. Experimental results demonstrate that the learned features perform better than hand-crafted features to detect water stress and quantify stress severity. The proposed framework can be deployed in practical applications for a non-destructive, near real-time, and automatic monitoring of plant water status in fields.

A Cultivar-Sensitive Approach for the Continuous Monitoring of Olive (Olea europaea L.) Tree Water Status by Fruit and Leaf Sensing

Sustainable irrigation is crucial to reduce water use and management costs in modern orchard systems. Continuous plant-based sensing is an innovative approach for the continuous monitoring of plant water status. Olive (Olea europaea L.) genotypes can respond to drought using different leaf and fruit physiological and morphological mechanisms. This study aimed to identify whether fruit and leaf water dynamics of two different olive cultivars were differently affected by water deficit and their response to changes of midday stem water potential (Ψstem), the most common indicator of plant water status. Plant water status indicators such as leaf stomatal conductance (gs) and Ψstem were measured in the Sicilian olive cultivars Nocellara del Belice (NB) and Olivo di Mandanici (MN), in stage II and III of fruit development. Fruit gauges and leaf patch clamp pressure probes were mounted on trees and their raw data were converted in relative rates of fruit diameter change (RRfruit) and leaf pressure change (RRleaf), sensitive indicators of tissue water exchanges. The analysis of diel, diurnal and nocturnal fluctuations of RRfruit and RRleaf highlighted differences, often opposite, between the two cultivars under water deficit. A combination of statistical parameters extrapolated from RRfruit and RRleaf diurnal and nocturnal curves were successfully used to obtain significant multiple linear models for the estimation of midday Ψstem. Fruit and leaf water exchanges suggest that olive cultivar can either privilege fruit or leaf water status, with MN likely preserving leaf water status and NB increasing fruit tissue elasticity under severe water deficit. The results highlight the advantages of the integration of fruit and leaf water dynamics to estimate plant water status and the need for genotype-specific models in olive.