scholarly journals High-throughput phenotyping for drought tolerance in rice

2021 ◽  
Vol 12 (2) ◽  
pp. 379-391
Author(s):  
Abdourasmane Kadougoudiou Konate ◽  
Adama Zongo ◽  
Jean Rodrigue Sangaré ◽  
Audrey Dardou ◽  
Alain Audebert
Keyword(s):  
Infrared Thermography ◽  
Canopy Temperature ◽  
Stress Index ◽  
Plant Responses ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Activity Measurements ◽  
Field Phenotyping ◽  
High Throughput Phenotyping ◽  
The Impact

Most lowland rice in West Africa depends mainly on rainfall for water supply. Drought is consequently one of the major constraints on rice production, drastically affecting both plant growth and development. The objective of this work was to study the impact of water deficit both on canopy temperature and on chlorophyll fluorescence level, used as indicators of transpiration and photosynthetic activity. Measurements using infrared thermography and fluorimetry were taken on both 17 lines resulting from the cross IR64 X B6144F-MR-6-0-0 and their two parents plus one tolerant (APO) controls. These 20 lines were phenotyped after applying a drought constraint in a controlled laboratory environment in Montpellier (France) in 2013 and - 2014 and in field in the lowlands of Banfora and Farako-ba (INERA Burkina Faso) in 2014. Results showed that the drought stress sustained by the plants increased canopy temperature in all lines, entailing differential disturbance of the photosynthetic process, markedly depressed in susceptible lines. A classification of the lines with respect to their sensitivity to stress could be established by using the Drought Factor Index (DFI), and Crop Water Stress Index (CWSI) as was established a correlation between the phenotyping methods by infrared thermography and fluorimetry. This article propose an efficient application of combined imaging as a rapid and accurate phenotyping tool for crop yield improvement, in particular by monitoring the efficiency of plant responses to the fluctuating of environmental conditions. This study proved the efficiency of the method combining IR thermographie and fluorimetry as a field phenotyping tools for drought resistance.

Early detection of Psa infection in kiwifruit by means of infrared thermography at leaf and orchard scale

2014 ◽  
Vol 41 (12) ◽  
pp. 1207 ◽  
Author(s):  
Wouter H. Maes ◽  
Peter E. H. Minchin ◽  
William P. Snelgar ◽  
Kathy Steppe
Keyword(s):  
Early Detection ◽  
Infrared Thermography ◽  
Pseudomonas Syringae ◽  
Canopy Temperature ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Block Level ◽  
Leaf Level ◽  
Small Container

Pseudomonas syringae pv. actinidiae (Psa), the causal agent of bacterial canker of kiwifruit, has become a worldwide threat for the kiwifruit industry. In this work, the potential of infrared thermography for early detection of physiological symptoms related to Psa-infection at leaf and at orchard block scale was assessed. At the leaf level, thermal cold spots appeared shortly after Psa-infection, well before any visual symptoms. A few weeks after infection, thermal hot spots were observed, associated with, but not limited to, spots of visible leaf necrosis. At orchard block level, Psa-infected canes were significantly warmer in both blocks and on all measurement days. A novel wet reference surface, existing of a cluster of cotton imitation leaves with similar dimensions and orientation as real leaves and remaining wet through sucking up water from a small container, was used to estimate the crop water stress index (CWSI). CWSI showed stable values of infected and uninfected areas during the day and between following days. Crop temperature and CWSI were closely correlated with leaf stomatal conductance, which was lower in infected canes. A Psa-infection map based on canopy temperature revealed that Psa infects the outer canes rather than the central part of the canopy.

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.

scholarly journals Different methods for calculating Crop Water Stress Index using infrared thermography

2020 ◽  
Author(s):  
Borjan Ranilović ◽  
Alen Cukrov ◽  
Ivanka Boras ◽  
Srećko Švaić ◽  
Monika Zovko
Keyword(s):  
Water Stress ◽  
Infrared Thermography ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop 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.

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.

scholarly journals Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications

2019 ◽  
Vol 10 (1) ◽  
pp. 202 ◽  
Author(s):  
Jaime Giménez-Gallego ◽  
Juan D. González-Teruel ◽  
Manuel Jiménez-Buendía ◽  
Ana B. Toledo-Moreo ◽  
Fulgencio Soto-Valles ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Canopy Temperature ◽  
Stress Index ◽  
Support Vector ◽  
Data Preparation ◽  
Crop Water Stress Index ◽  
Weight Decay ◽  
Water Stress Index ◽  
Svm Model

The crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the calculation of the CWSI. To this end, two image-segmentation models based on support vector machine (SVM) and deep learning have been studied in this article. The models have been trained with different parameters (encoder depth, optimizer, learning rate, weight decay, validation frequency and validation patience), and several indicators (accuracy, precision, recall and F1 score/dice coefficient), as well as prediction, training and data preparation times are discussed. The results of the F1 score indicator are 83.11% for SVM and 86.27% for deep-learning models. More accurate results are expected for the deep-learning model by increasing the dataset, whereas the SVM model is worthwhile in terms of reduced data preparation times.

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 <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 < 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.

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