Detection Of Plant Water Stress Research Articles

Development and evaluation of a decision support mobile application for cotton irrigation management

Irrigation decision support tools are a means by which crop producers can improve the efficiency of water they apply. However, the available tools generally have poor adoption rates due to high costs, requirement for technical background and skills, need for extensive input data, lack of economic analysis support, and/or low accuracy. Here, we aimed to develop an inexpensive and easy-to-use decision support mobile app called Irrigation Decision-support for Conserving Resources and Optimizing Production (idCROP), to aide cotton producers in the Texas Rolling Plains and High Plains regions to increase water-use efficiency while maintaining higher crop yields. The app was built based on the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), and a newly developed economic model to provide real-time irrigation schedules and projected economic outcomes based on water use and production goals. The app integrates real-time management information from users with historic, real-time, and forecasted short-term and seasonal weather data. This integration enables output of efficient and situationally relevant irrigation schedules and forecasts of associated cotton yield and economic returns. This enables users to choose an irrigation strategy that best suits their irrigation capacities and expected returns. The irrigation schedules can be improved by optional remote detection of plant water stress using a sensor platform mounted on a pivot irrigation system, but the app can be used without the sensors in conjunction with any type of irrigation system. The crop model-based irrigation scheduling in idCROP was validated at the Texas A&M AgriLife Chillicothe Research Station in the Texas Rolling Plains region and acceptable results were obtained. The idCROP app provides producers with a simple but powerful tool that takes the guess work out of irrigation management, helping them apply limited irrigation water more efficiently.

An augmented attention-based lightweight CNN model for plant water stress detection

An augmented attention-based lightweight CNN model for plant water stress detection

Active hyperspectral mid-infrared imaging based on a widely tunable quantum cascade laser for early detection of plant water stress

Plant water stress has been extensively studied using hyperspectral visible- and near-infrared systems. Thermal imaging and the recent availability of widely tunable infrared quantum cascade laser (QCL) allow us to propose an active hyperspectral imaging system operating in the mid-infrared (MIR) band, where the system output consists of a series of narrowband subimages arranged across the reflectance spectrum of the sample, forming a hypercube data acquired by “staring” acquisition technique. To evaluate more precisely the capabilities of the active hyperspectral imaging, we propose a system composed of four powerful tunable QCL covering the 3.9- to 4.7-μm and 7.5- to 11-μm wavelengths ranges. Two cameras are used for detection: an InSb cooled camera ranging from 3 to 5 μm and a bolometer from 7.5 to 13 μm range. This system is validated by applying to growing plants for early water stress detection. Finally, we present and discuss results using partial least squares discriminant analysis classification technique to characterize water status of different plants, separated in two classes: control subjects were maintained at 80% of the amount of water to soil saturation ratio and stressed subjects at 20%. Initial discrimination results have shown the efficiency of the proposed system.

Early Detection of Plant Water Stress

Early Detection of Plant Water Stress

Detection of plant water stress using leaf spectral responses in three poplar hybrids prior to the onset of physiological effects

ABSTRACT The negative impact of water stress on forest and tree plantation productivity has been the focus of many investigations. However, moderate water stress that can decrease productivity and is difficult to detect, has received less attention. Therefore, we designed a greenhouse experiment where the main objective was to test the efficacy of published biochemical and water stress indices along with physiological traits in detecting moderate water stress at leaf scale. Potted saplings of three hybrid poplars ((Populus × canadensis) × P. maximowiczii, Populus × canadensis and Populus × generosa ‘Boelare’) were subjected to moderate water stress. During the experiment we recorded (i) the biomass accumulation and allocation to leaves, stem, and roots; (ii) Net CO2 assimilation rate (A) and stomatal conductance (g s) and (iii) Spectral biochemical and water indices. Results indicate that moderate water deficit had a negative impact on biomass accumulation, but no change was detected in physiological traits. Nitrogen and chlorophyll contents also remained unaffected. Spectral biochemical indices failed to detect differences between treatments, whereas water stress indices succeeded. We also have identified unique wavelengths in the shortwave infrared region of the spectrum that proved sufficiently sensitive on their own to detect moderate water deficit. The results were not influenced by a genotype effect, suggesting that these unique wavelengths could be responding to a general rather than a species-specific leaf feature, thus widening opportunities for the early detection of plant stress, particularly moderate water deficit.

UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivity and atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.

Plant water stress detection based on aerial and terrestrial infrared thermography: a study case from vineyard and olive orchard

Irrigation scheduling is critical for vineyards and olive orchards, since it directly affects yield and fruit composition. Regulated deficit irrigation (RDI) strategies have been applied on both crops with positive results in the past. However, to successfully regulate stress levels, it is necessary to have accurate measurements of plant water status, which is usually achieved using a pressure chamber. In this regard, canopy temperature (Tc) has been shown to be an accurate indicator of plant water stress. Therefore, the objective of this study was to evaluate the accuracy of water stress detection based on aerial and terrestrial infrared thermography for a vineyard and an olive orchard. Lateral infrared thermal images were obtained using a handheld infrared camera and nadir-view infrared thermal images were obtained using an unmanned aerial vehicle (UAV). In addition, measurements of midday stem water potential (Φstem) from olives trees (Olea europaea L. 'eArbequina'f) and grapevines (Vitis vinifera L. 'eCarmenere'f) were taken under different RDI strategies during the 2013- 2014 growing season. The image analysis was performed using a customized code written in MathlabR defining thresholds to exclude non-plant elements. Results showed that the use of infrared thermal images aided in recognizing the differences in the water availability for irrigation treatments when the plants were under stress conditions.

Detection of Plant Water Stress Using Leaf Temperature and Microclimatic Measurements in Almond, Walnut, and Grape Crops

Abstract. A mobile sensor suite was developed and evaluated to predict plant water status by measuring the leaf temperature and microclimatic variables in nut crop trees and grapevines. The sensor suite consists of an infrared thermometer to measure leaf temperature along with other relevant sensors to measure microclimatic variables. The sensor suite was successfully evaluated in commercial orchards in the Sacramento Valley of California on three orchard crops, i.e., almond (Prunus dulcis), walnut (Juglans regia), and grape (Vitis vinifera), for both sunlit and shaded leaves. Stepwise linear regression models developed for shaded leaf temperature yielded coefficient of multiple determination values of 0.90, 0.86, and 0.86 for almond, walnut, and grape crops, respectively. Stem water potential (SWP) was found to be a significant variable in all models. The regression models were used to classify trees into water stressed and unstressed categories. Critical misclassification errors (classifying a water stressed tree as unstressed) for sunlit and shaded leaf models were 8.8% and 5.2% for almond, 5.4% and 6.9% for walnut, and 12.9% and 8.1% for grapevine, respectively. Canonical discriminant analyses were also conducted using the sensor suite data to classify trees into water stressed and unstressed trees with critical misclassification errors for sunlit and shaded leaves of 9.3% and 7.8% for almond, 2.0% and 4.1% for walnut, and 9.6% and 1.6% for grapevine, respectively. These results show the feasibility of the sensor suite to determine plant water status for irrigation management of nut and vineyard crops.

Thermographic measurement of canopy temperature is a useful tool for predicting water deficit effects on fruit weight in citrus trees

Alternative and more practical methods for plant water stress detection than stem water potential (ψs) and stomatal conductance (gs) are needed when regulated deficit irrigation (RDI) strategies are applied. The aim of this experiment was to compare sap flow and canopy temperature (Tc) measurements with more classical methods like ψs or gs to predict the effect of deficit irrigation on fresh fruit weight in citrus trees. The experiment was performed during the summer of 2011 in a “Clementina de Nules” orchard undergoing RDI. Sap flow was determined by means of the compensation heat pulse method in well-watered and RDI trees. Tc was measured continuously with infrared thermometers (IRTs) mounted over the canopies and also weekly with an infrared hand-operated thermographic camera taking frontal images of the sunlit side of tree crowns. Concurrently, ψs and gs were also measured on all trees. Results showed that the evolution of the relative transpiration obtained with the sap flow sensors was in agreement with the plant water stress experienced. The values of Tc obtained with the fixed IRTs, normalized by air temperature (Tc−Ta) were in general poorly related with ψs and gs. However, when Tc was obtained from thermal imaging, there was a good correlation with ψs in days of relatively high water stress (i.e. when ψs differences among treatments were >1.0MPa). The average fruit weight at harvest was significantly correlated with all the stress indicators, and the best correlation was that of thermographic Tc followed by ψs and gs. Overall, results showed that in citrus trees Tc measurement obtained from thermal imaging is a good tool to predict the effect of water deficit on fresh fruit weight.

Plant water-stress detection by digital imaging using the entropy concept: application to menthe plant (Mentha viridis)

The purpose of this work is to demonstrate the capability of digital imaging within the visible range of the electromagnetic radiation to detect early signs of water stress in plants. The approach is based on colour information and on using the entropy concept. The method is composed of two main steps. The first one classifies pixels of the image that contains plant and soil into plant and background pixels. The classification is performed by a hierarchical analysis method by using a two-dimensional (2D) histogram algorithm. The second one introduces a non-destructive water stress indicator using the entropy concept. The method is applied to detect the extent of modification in menthe plants occurring under water-stress conditions. A physiological study of the water status of the plant is undertaken in order to compare the results obtained by the proposed approach. The results show that the proposed methodology is able to detect the signs of water stress in the menthe plant.

Usefulness of thermography for plant water stress detection in citrus and persimmon trees

The feasibility of using canopy temperature (Tc) measured with a hand-operated infrared thermographic camera as a water stress indicator was evaluated in the field during two seasons on citrus and persimmon trees subjected to different levels of deficit irrigation. In both species, which differ in leaf anatomy and stomatal response to environmental conditions, Tc was compared with midday stem water potential (Ψs) measurements. In persimmon trees, leaf stomatal conductance (gs) was also measured. In 2009, images were taken from the sunlit and shady sides of the canopies. Based on the results obtained, during the second experimental season images were taken from the sunlit side of the trees and also from above the canopy. In persimmon, trees under deficit irrigation had lower Ψs and gs what resulted in a clear increase in Tc regardless of the position from where the pictures were taken. The maximum Tc difference between deficit-irrigated and control trees observed was of 4.4°C, which occurred when the stressed trees had Ψs values 1.1MPa lower than the control ones. In persimmon trees, Tc was the most sensitive indicator of plant water status particularly due to the lower tree-to-tree variability as compared to Ψs and gs. On the other hand, in citrus trees Tc was not always affected by plant water stress. Only in the second experimental season, when air vapour pressure deficit values were below 2.7kPa and images were also taken from above the canopies, deficit-irrigated trees had higher Tc than the control ones, this difference being at most 1.7°C. Overall, the results show that hand-operated thermographic cameras can be used to detect plant water stress in both fruit tree species. Nevertheless, the use of Tc measurements to detect plant water stress appears to be more precise in persimmon than in orange citrus. This might be because persimmon trees have larger leaf size which determines higher canopy resistance allowing for higher increases in canopy temperature in response to water stress via stomatal closure.

Can heat-pulse sap flow measurements be used as continuous water stress indicators of citrus trees?

Transpiration of well-watered and regulated deficit irrigated (RDI) citrus trees was determined by sap flow (SF) measurements using the compensation heat-pulse method. Its potential for detection of plant water stress was evaluated in comparison with measurements of midday stem water potential (wstem). The study was carried out during 2 years in two commercial groves of Clementina de Nules (CN) and Navel Lane Late (NLL). SF measurements were taken in two trees per treatment instrumented with two identical gauges per tree in NLL and two different types of gauges (type 1 shorter than type 2) in CN. The absolute SF values underestimated the tree water use. Averaged over the entire period of water restrictions, a reduction of about 50 % in water application in the RDI trees of both species decreased tree transpiration compared to the control trees only by a 15 %. Both the nocturnal-to- diurnal SF ratio and the relative transpiration were in good agreement with differences in wstem. Overall, results sug- gest that SF measurements should be preferentially used in relative terms. Sap flow sensors are useful for detecting plant water stress, but they also highlight some of the problems for accurately measuring transpiration.

Evapotranspiration over a camelina crop at Maricopa, Arizona

Evapotranspiration (ET) over an oilseed crop, Camelina sativa, was evaluated for an experimental plot in Maricopa, Arizona between December 2006 and April 2007. Camelina (cv. Robinson) was grown in a 1.3-ha field in a randomized design containing 32 plots replicated for four levels of water depletion: 40, 55, 65, and 75%. Six supplemental plots evaluated water stress with 85% soil water depletion. A surface energy balance model, utilizing meteorological and radiometric observations within the plots, was implemented to estimate latent heat fluxes from the camelina canopy at 15-min intervals during most of the growing season. The latent heat fluxes were then summed to produce daily estimates of ET. A distinct aspect of the model was the incorporation of canopy thermal infrared observations at 15 different locations, which allow plant water stress detection. The resulting ET values were compared with independent observations of soil water depletion, obtained from soil neutron probe profiles. Agreement on a plot-by-plot basis between modeled and observed ET values was very good, where root mean squared errors (RMSE) were usually less than 0.8 mm d −1, R 2 > 0.78 , and bias < 0.76 mm d −1. Average yield for the camelina crop was 1000 ± 310 kg ha −1. Average total oil content was 41.4 ± 3.8 % by weight. Oil content was predicted by yield with fair accuracy where R 2 was 0.425 and RMSE was 2.36%. Correlation between resultant camelina yield and total ET was weak; the four main water depletion treatment plots showed no dependence of yield upon cumulative ET. The secondary water stress treatment plots, however, did show dependence, where a 20% reduction in cumulative ET resulted in a corresponding 24% reduction in yield. Hence seasonal camelina water minimally required 333–423 mm. The ET results showed that the surface energy balance is a feasible and valuable technique for monitoring crop water requirement over this potential oil seed crop. Further work is needed to characterize the relation between camelina yield and ET, including tests of different varieties and levels of fertilization.

Comparison of Plant Water Stress Detection Ability of Color and Gray-Level Texture in Sunagoke Moss

Imaging is emerging as a novel technique for real-time and non-invasive stress diagnosis in plants. In this study, water stress detection ability of six texture features extracted using color co-occurrence matrix (CCM) and gray-level co-occurrence matrix (GLCM) methods were compared. Multi-layer perceptron neural network (MLPNN) models were developed and used to predict or classify water stress in five samples of Sunagoke moss (Rhacomitrium canescens) using the texture features. Hue-saturation-intensity (HSI) texture features had an average water stress prediction mean square error (MSE) of 0.0043. The average water stress prediction MSE of GLCM texture features was 0.0053. The CCM and GLCM texture features showed average water stress misclassification errors of 3.05% and 1.78%, respectively. Texture features extracted from HSI color space showed a higher ability and reliability to predict and classify water stress in the plant. By discarding the intensity component, the HSI texture features can be used to detect water stress in Sunagoke moss under natural lighting conditions. Use of hyperspectral reflectance imaging can enable this method to detect specific stress signatures for particular plants and make it a powerful biophysical, non-invasive, and pre-visual stress diagnosis method for a wide variety of stresses in plants.

ESTABLISHING CROP WATER STRESS INDEX (CWSI) THRESHOLD VALUES FOR EARLY, NON–CONTACT DETECTION OF PLANT WATER STRESS

Early, noncontact, nondestructive, and quantitative detection of plant water stress with the application ofinfrared thermometry using a crop water stress index (CWSI) was established. A CWSI model for plants grown undercontrolled environments was developed using thermodynamic principles and energy balance of the plant. CWSI thresholdvalues were established with a parametric approach. The effectiveness of the sensing technique was evaluated using timingof the stress detection by a grower. The CWSIbased technique was able to detect the stress one to two days prior to the timeof stress detection by visual observation. Overall results of this study suggested that previsual and noncontact detectionof plant water stress with infrared thermometry application using CWSI is feasible.

Machine vision extracted plant movement for early detection of plant water stress.

A methodology was established for early, non-contact, and quantitative detection of plant water stress with machine vision extracted plant features. Top-projected canopy area (TPCA) of the plants was extracted from plant images using image-processing techniques. Water stress induced plant movement was decoupled from plant diurnal movement and plant growth using coefficient of relative variation of TPCA (CRV[TPCA)] and was found to be an effective marker for water stress detection. Threshold value of CRV(TPCA) as an indicator of water stress was determined by a parametric approach. The effectiveness of the sensing technique was evaluated against the timing of stress detection by an operator. Results of this study suggested that plant water stress detection using projected canopy area based features of the plants was feasible.

The Effect of Water Shortage on Potted Peach Trees in Relation to Ecophysiological Parameters and Infra-red Thermometry

Potted peach trees grown outdoors during the 1997 season were subjected to drought and subsequent rewatering to evaluate their dynamic response to soil water content. The investigation was primarily focused on the early detection of plant water stress to prevent negative effects on the growth. Leaf chlorophyll fluorescence and canopy temperature estimates (by infra-red thermometry) were conducted. Drought effect on physiological processes were detected through by estimates of canopy development rate, leaf gas-exchange measurements; while leaf water potential was measured to characterize plant water status. A decrease in the canopy's development rate was found 1 week after irrigation was stopped, which also coincided with a more-negative leaf water potential, whereas a decrease of the gas-exchange activities occurred several days later. No significant differences between the stressed and control plants were recorded by the chlorophyll fluorescence parameters (Fo, Fm, Fv and the ratio Fv/Fm), whereas the infra-red estimates of canopy temperature detected a slight increase of the canopy surface temperature (connected to the change of leaf energy balance and in relation to partial stomatal closure) on the non-irrigated plants 1 week after the beginning of the trial. The use of infra-red thermometry for early detection of water shortage is discussed.

Infrared measurement of canopy temperature and detection of plant water stress

Leaf temperature is directly determined by leaf energy and water balance. If diminished water availability decreases latent heat flux at the leaf surface, a complementary increase of sensible heat will occur and create a larger temperature difference between foliage and air. Radiation, air temperature, humidity and wind speed modify leaf temperature and may mask indications of water stress. The position, inclination and orientation of leaves within the canopy also produce considerable variation of leaf temperature. These factors were incorporated in a linear transpiration model, using physical and physiological characteristics of cotton. Water stress was simulated by imposing a limit value for stomatal conductance. The energy balance equation was solved as a function of angle between leaf and solar beam, to determine leaf temperature frequency distribution. The results show that stress induced temperature rise occurs over a small percentage of the total leaf area. Detection of moderate stress requires a normalizing procedure which takes into account meteorological conditions. The leaf temperature distribution is a better indicator of stress than the average value.

Detection of changes in leaf water content using Near- and Middle-Infrared reflectances

Detection of plant water stress by remote sensing has been proposed using indices of Near-Infrared (NIR, 0.7–1.3 μm) and Middle-Infrared (MIR, 1.3–2.5 μm) wavelengths. The first objective of this study was to test the ability of the Leaf Water Content Index (LWCI) to determine leaf Relative Water content (RWC) of different species with different leaf morphologies. The second objective was to determine how the Moisture Stress Index (MSI; MIR / NIR) varies with RWC and the Equivalent Water Thickness (EWT). Reflectance factors at 0.82 μm and 1.6 μm were measured on leaves of Quercus agrifolia (sclerophyllous leaves), Liquidambar styraciflua (hardwood deciduous tree leaves), Picea rubens and Picea pungens (conifer needles), and Glycine max (herbaceous dicot leaves) as they dried on a laboratory bench. RWC and EWT were measured concurrently with the reflectance measurements. The results showed that LWCI was equal to RWC for the species tested. However, the results of a sensitivity analysis indicated the reflectances at 1.6 μm for two different RWC must be known for accurate prediction of unknown RWC; thus the LWCI is impractical for field applications. MSI was linearly correlated to RWCwith each species having a different regression equation and to log 10 EWT with data of all species falling on the same regression line. Because EWT is correlated with leaf area index, MSI should also be correlated with leaf area index. Assuming that the linear regression equation of MSI to EWT can be applied to canopies, then the minimum significant change of RWC that can be detected is 52%. For most plants, the natural variation in RWC from water stress is only about 20%, so that we conclude that indices derived from NIR and MIR reflectances cannot be used to remotely-sense water stress.