An infrared-based coefficient to screen plant environmental stress: concept, test and applications

2009 ◽  
Vol 36 (11) ◽  
pp. 990 ◽  
Author(s):  
Guo Yu Qiu ◽  
Kenji Omasa ◽  
Sadanori Sase
Keyword(s):  
Heat Stress ◽  
Water Stress ◽  
Environmental Stress ◽  
Air Temperature ◽  
Canopy Temperature ◽  
Water Shortage ◽  
Leaf Temperature ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Water Stress Index

By introducing a reference dry leaf (a leaf without transpiration), a formerly proposed plant transpiration transfer coefficient (hat) was applied to detect environmental stress caused by water shortage and high temperature on melon, tomato and lettuce plants under various conditions. Results showed that there were obvious differences between leaf temperature, dry reference leaf temperature and air temperature. The proposed coefficient hat could integrate the three temperatures and quantitatively evaluate the environmental stress of plants. Experimental results showed that the water stress of melon plants under two irrigation treatments was clearly distinguished by using the coefficient. The water stress of a tomato plant as the soil dried under a controlled environmental condition was sensitively detected by using hat. A linear relationship between hat and conventional crop water stress index was revealed with a regression determination coefficient R2 = 0.97. Further, hat was used to detect the heat stress of lettuce plants under high air temperature conditions (28.7°C) with three root temperature treatments (21.5, 25.9 and 29.5°C). The canopy temperature under these treatments was respectively 26.44, 27.15 and 27.46°C and the corresponding hat value was –1.11, –0.74 and –0.59. Heat stress was also sensitively detected using hat. The main advantage of hat is its simplicity for use in infrared applications.

Changes in the baseline of the Crop Water Stress Index for lucerne (Medicago sativa) over 3 years

1991 ◽  
Vol 116 (1) ◽  
pp. 63-66 ◽  
Author(s):  
E. A. Rechel ◽  
W. R. DeTar ◽  
D. Ballard
Keyword(s):  
Water Stress ◽  
Crop Production ◽  
Vapour Pressure Deficit ◽  
Water Shortage ◽  
Stress Index ◽  
First Year ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

SUMMARYThe ability to detect and measure water stress accurately is critical for optimizing crop production. The Crop Water Stress Index (CWSI), the linear relationship of the difference between foliage and air temperatures as a function of the air vapour pressure deficit, is one widely used method. Under well-watered conditions, a ‘baseline’ is derived that is crop specific and presumed fairly constant, despite differences in development and physiology. This study reports changes in the baseline of the CWSI for lucerne crops not subjected to water shortage over 3 years. Studies of lucerne in California from April 1986 to October 1988 used the CWSI to plan irrigations. It was necessary to re-establish the baseline periodically throughout the experiment. In the first year it was similar to that reported in the literature, but in the second year it had a statistically significant steeper slope and higher intercept. In the third year, the regression equation was similar to that in the first year. The changes in the baseline are thought to be a result of crop age rather than year-to-year weather fluctuations. The baseline needs to be determined periodically as the crop matures, to ensure accurate interpretation of plant water stress.

scholarly journals CROP WATER STRESS INDEX FOR PREDICTING YIELD LOSS IN COMMON BEAN

Irriga ◽  
2022 ◽  
Vol 1 (4) ◽  
pp. 687-695
Author(s):  
Carlos Quiloango-Chimarro ◽  
Rubens Duarte Coelho ◽  
Jéfferson de Oliveira Costa ◽  
Rafael Gomez-Arrieta
Keyword(s):  
Water Stress ◽  
Grain Yield ◽  
Common Bean ◽  
Yield Loss ◽  
Canopy Temperature ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

The crop water stress index (CWSI), an index derived from canopy temperature, has been widely studied as a physiological indicator of plant water status to optimize irrigation in common beans. However, it is not clear how this index could contribute to yield prediction as a decision support tool in irrigation management. This paper aimed to use the CWSI for predicting yield loss in common bean (Phaseolus vulgaris L.) subjected to water stress under drip irrigation. A rain shelter experiment was conducted using a completely randomized design with five replications. The indeterminate growth cultivar TAA Dama was subjected to three irrigation treatments: 100% of the field capacity (FC), 75 and 50% FC from 20 days after sowing (DAS) until the end of the crop cycle. Grain yield was reduced by 42% under 50% FC treatment. Furthermore, stomatal conductance was reduced under this treatment, whereas the CWSI and canopy temperature increased as irrigation levels decreased. The relationship between grain yield and CWSI (R2=0.76, RSME=2.35g) suggests that canopy temperature data could be used to forecast grain yield losses. In conclusion, farmers can have a low-cost, effective technique for making water management decisions in common bean.

scholarly journals Linking plant and soil indices for water stress management in black gram

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Afshin Khorsand ◽  
Vahid Rezaverdinejad ◽  
Hossein Asgarzadeh ◽  
Abolfazl Majnooni-Heris ◽  
Amir Rahimi ◽  
...  
Keyword(s):  
Water Stress ◽  
Canopy Temperature ◽  
Growth Period ◽  
Irrigation Scheduling ◽  
Stress Index ◽  
Black Gram ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Relative Water ◽  
Soil Indices

AbstractMeasurement of plant and soil indices as well as their combinations are generally used for irrigation scheduling and water stress management of crops and horticulture. Rapid and accurate determination of irrigation time is one of the most important issues of sustainable water management in order to prevent plant water stress. The objectives of this study are to develop baselines and provide irrigation scheduling relationships during different stages of black gram growth, determine the critical limits of plant and soil indices, and also determine the relationships between plant physiology and soil indices. This study was conducted in a randomized complete block design at the four irrigation levels 50 (I1), 75 (I2), 100 (I3 or non-stress treatment) and 125 (I4) percent of crop’s water requirement with three replications in Urmia region in Iran in order to irrigation scheduling of black gram using indices such as canopy temperature (Tc), crop water stress index (CWSI), relative water content (RWC), leaf water potential (LWP), soil water (SW) and penetration resistance (Q) of soil under one-row drip irrigation. The plant irrigation scheduling was performed by using the experimental crop water stress index (CWSI) method. The upper and lower baseline equations as well as CWSI were calculated for the three treatments of I1, I2 and I3 during the plant growth period. Using the extracted baselines, the mean CWSI values for the three treatments of I1, I2 and I3 were calculated to be 0.37, 0.23 and 0.15, respectively, during the growth season. Finally, using CWSI, the necessary equations were provided to determine the irrigation schedule for the four growing stages of black gram, i.e. floral induction-flowering, pod formation, seed and pod filling and physiological maturity, as (Tc − Ta)c = 1.9498 − 0.1579(AVPD), (Tc − Ta)c = 4.4395 − 0.1585(AVPD), (Tc − Ta)c = 2.4676 − 0.0578(AVPD) and (Tc − Ta)c = 5.7532 − 0.1462(AVPD), respectively. In this study, soil and crop indices, which were measured simultaneously at maximum stress time, were used as a complementary index to remove CWSI constraints. It should be noted that in Urmia, the critical difference between the canopy temperature and air temperature (Tc − Ta), soil penetration resistance (Q), soil water (SW) and relative water content (RWC) for the whole growth period of black gram were − 0.036 °C, 10.43 MPa and 0.14 cm3 cm−3 and 0.76, respectively. Ideal point error (IPE) was also used to estimate RWC, (Tc − Ta) and LWP as well as to select the best regression model. According to the results, black gram would reduce its RWC less through reducing its transpiration and water management. Therefore, it can be used as a low-water-consuming crop. Furthermore, in light of available facilities, the farmer can use the regression equations between the obtained soil and plant indices and the critical boundaries for the irrigation scheduling of the field.

Estimation of Stomatal Conductance using Crop Water Stress Index based on the Thermal Image at a Leaf Scale

2020 ◽  
Author(s):  
Hoejeong Jeong ◽  
Jae-Hyun Ryu ◽  
Sang-il Na ◽  
Jaeil Cho
Keyword(s):  
Water Stress ◽  
Stomatal Conductance ◽  
Thermal Image ◽  
Crop Growth ◽  
Leaf Temperature ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

<p>  In 1980s, Crop Water Stress Index (CWSI) is suggested to indicate the water stress of crops. CWSI is based on the leaf energy balance, which is closely related to leaf temperature. To calculate CWSI, meteorological factors such as air temperature and vapor pressure deficit should be measured besides leaf temperature. As recent technology has been developed, leaf temperature can be easily observed by thermal camera or infrared thermometer. Stomatal conductance (g<sub>s</sub>, mmol m<sup>-2</sup> s<sup>-1</sup>) is one of the critical factors to understand crop photosynthesis and water demand. In addition, the behaviors of g<sub>s</sub> can represent the biotic and abiotic plant stresses. In abnormal condition, such as drought, insects or disease, g<sub>s</sub> getting lower. The observation of g<sub>s</sub> will make better to evaluate and predict crop growth and conditions. Therefore, the time series data of g<sub>s</sub> is useful for the monitoring of crop growth and the quick detection of abnormal crop condition in smart-farming system but there are some limitations to measure g<sub>s</sub> continuously and easily.</p><p>  We assume that there is some relationship between CWSI and g<sub>s</sub> because both has strong relation to leaf temperature. Thus, the aim of this study is to investigate possibility of estimation of g<sub>s</sub> using CWSI which is derived from thermal image. Through the data collected from literatures, negative correlations between CWSI and g<sub>s</sub> were revealed. The slope of correlation was changed according to crop types. In addition, as a result of simulation, there is almost linear negative relationship between CWSI and g<sub>s</sub>, and the slope was determined by maximum stomatal conductance (g<sub>s_max</sub>). Field measurement in this study was also demonstrated to identify such correlation. Further, various methods to measure CWSI were tested. This relationship will contribute to not only monitoring of crop stress for irrigation scheduling in smart farm system but also estimating evapotranspiration, photosynthesis, and crop yield.</p>

scholarly journals Evaluation of the Crop Water Stress Index as an Indicator for the Diagnosis of Grapevine Water Deficiency in Greenhouses

Horticulturae ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 86
Author(s):  
Chen Ru ◽  
Xiaotao Hu ◽  
Wene Wang ◽  
Hui Ran ◽  
Tianyuan Song ◽  
...  
Keyword(s):  
Water Stress ◽  
Water Status ◽  
Leaf Temperature ◽  
Stress Index ◽  
Plant Water ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
The Relationship ◽  
Crop Water Stress

Precise irrigation management of grapevines in greenhouses requires a reliable method to easily quantify and monitor the grapevine water status to enable effective manipulation of the water stress of the plants. This study evaluated the applicability of crop water stress index (CWSI) based on the leaf temperature for diagnosing the grapevine water status. The experiment was conducted at Yuhe Farm (northwest China), with drip-irrigated grapevines under three irrigation treatments. Meteorological factors, soil moisture contents, leaf temperature, growth indicators including canopy coverage and fruit diameter, and physiological indicators including SPAD (relative chlorophyll content), stem water potential (φs), stomatal conductance (gs), and transpiration rate (E) were studied during the growing season. The results show that the relationship between the leaf-air temperature difference (Tc-Ta) and the plant water status indicators (φs, gs, E) were significant (P < 0.05), and the relationship between gs, E and Tc-Ta was the closest, with R2 values ranging from 0.530–0.604 and from 0.545–0.623, respectively. CWSI values are more easily observed on sunny days, and it was determined that 14:00 BJS is the best observation time for the CWSI value under different non-water-stressed baselines. There is a reliable linear correlation between the CWSI value and the soil moisture at 0–40 cm (P < 0.05), which could provide a reference when using the CWSI to diagnose the water status of plants. Compared with the Tc-Ta value, the CWSI could more accurately monitor the plant water status, and above the considered indictors, gs has the greatest correlation with the CWSI.

scholarly journals Assessment of Canopy Porosity in Avocado Trees as a Surrogate for Restricted Transpiration Emanating from Phytophthora Root Rot

2019 ◽  
Vol 11 (24) ◽  
pp. 2972
Author(s):  
Arachchige Surantha Ashan Salgadoe ◽  
Andrew James Robson ◽  
David William Lamb ◽  
Elizabeth Kathryn Dann
Keyword(s):  
Water Stress ◽  
Root Rot ◽  
Human Error ◽  
Management Practices ◽  
Canopy Temperature ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Thermal Imagery ◽  
Phytophthora Root Rot ◽  
Water Stress Index

Phytophthora root rot (PRR) disease is a major threat in avocado orchards, causing extensive production loss and tree death if left unmanaged. Regular assessment of tree health is required to enable implementation of the best agronomic management practices. Visual canopy appraisal methods such as the scoring of defoliation are subjective and subject to human error and inconsistency. Quantifying canopy porosity using red, green and blue (RGB) colour imagery offers an objective alternative. However, canopy defoliation, and porosity is considered a ‘lag indicator’ of PRR disease, which, through root damage, incurs water stress. Restricted transpiration is considered a ‘lead indicator’, and this study sought to compare measured canopy porosity with the restricted transpiration resulting from PRR disease, as indicated by canopy temperature. Canopy porosity was calculated from RGB imagery acquired by a smartphone and the restricted transpiration was estimated using thermal imagery acquired by a FLIR B250 hand-held thermal camera. A sample of 85 randomly selected trees were used to obtain RGB imagery from the shaded side of the canopy and thermal imagery from both shaded and sunlit segments of the canopy; the latter were used to derive the differential values of mean canopy temperature (Δ Tmean), crop water stress index (Δ CWSI), and stomatal conductance index (Δ Ig). Canopy porosity was observed to be exponentially, inversely correlated with Δ CWSI and Δ Ig (R2 > 90%). The nature of the relationship also points to the use of canopy porosity at early stages of canopy decline, where defoliation has only just commenced and detection is often beyond the capability of subjective human assessment.

scholarly journals Simulating Canopy Temperature Using a Random Forest Model to Calculate the Crop Water Stress Index of Chinese Brassica

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2244
Author(s):  
Mingxin Yang ◽  
Peng Gao ◽  
Ping Zhou ◽  
Jiaxing Xie ◽  
Daozong Sun ◽  
...  
Keyword(s):  
Water Stress ◽  
Random Forest ◽  
Water Status ◽  
Canopy Temperature ◽  
Random Forest Model ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Forest Model ◽  
Water Stress Index

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

Leaf temperature of maize and Crop Water Stress Index with variable irrigation and nitrogen supply

2017 ◽  
Vol 35 (6) ◽  
pp. 549-560 ◽  
Author(s):  
David A. Carroll ◽  
Neil C. Hansen ◽  
Bryan G. Hopkins ◽  
Kendall C. DeJonge
Keyword(s):  
Water Stress ◽  
Leaf Temperature ◽  
Nitrogen Supply ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

scholarly journals Data-Driven Models for Canopy Temperature-Based Irrigation Scheduling

2020 ◽  
Vol 63 (5) ◽  
pp. 1579-1592
Author(s):  
Bradely A. King ◽  
Krista C. Shellie ◽  
David D. Tarkalson ◽  
Alexander D. Levin ◽  
Vivek Sharma ◽  
...  
Keyword(s):  
Water Stress ◽  
Canopy Temperature ◽  
Irrigation Scheduling ◽  
Data Driven ◽  
Stress Index ◽  
Wine Grape ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

HighlightsArtificial neural network modeling was used to predict crop water stress index lower reference canopy temperature.Root mean square error of predicted lower reference temperatures was &lt;1.1°C for sugarbeet and Pinot noir wine grape.Energy balance model was used to dynamically predict crop water stress index upper reference canopy temperature.Crop water stress index for sugarbeet was well correlated with irrigation and soil water status.Crop water stress idex was well correlated with midday leaf water potential of wine grape.Abstract. Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adoption in production agriculture. Development of generalized crop-specific upper and lower reference temperatures is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data-driven models for predicting the reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed from measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over five years under full and severe deficit irrigation. Lower reference temperatures (TLL) were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1°C. The relationship between TLL minus ambient air temperature and vapor pressure deficit was represented with a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures that were nearly double the values reported in previous studies. A daily CWSI, calculated as the average of 15 min CWSI values between 13:00 and 16:00 MDT for sugarbeet and between 13:00 and 15:00 local time for wine grape, were well correlated with irrigation events and amounts. There was a significant (p &lt; 0.001) linear relationship between the daily CWSI and midday leaf water potential of Malbec and Syrah wine grapes, with an R2 of 0.53. The data-driven models developed in this study to estimate reference temperatures enable automated calculation of the CWSI for effective assessment of crop water stress. However, measurements taken under conditions of wet canopy or low solar radiation should be disregarded as they can result in irrational values of the CWSI. Keywords: Canopy temperature, Crop water stress index, Irrigation scheduling, Leaf water potential, Sugarbeet, Wine grape.

Sign in / Sign up

Export Citation Format

Share Document