Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

&#160; 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 (gs, mmol m-2 s-1) is one of the critical factors to understand crop photosynthesis and water demand. In addition, the behaviors of gs can represent the biotic and abiotic plant stresses. In abnormal condition, such as drought, insects or disease, gs getting lower. The observation of gs will make better to evaluate and predict crop growth and conditions. Therefore, the time series data of gs 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 gs continuously and easily.&#160; We assume that there is some relationship between CWSI and gs because both has strong relation to leaf temperature. Thus, the aim of this study is to investigate possibility of estimation of gs using CWSI which is derived from thermal image. Through the data collected from literatures, negative correlations between CWSI and gs 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 gs, and the slope was determined by maximum stomatal conductance (gs_max). 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.

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

Horticulturae ◽

10.3390/horticulturae6040086 ◽

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.

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

Irrigation Science ◽

10.1007/s00271-017-0558-4 ◽

2017 ◽

Vol 35 (6) ◽

pp. 549-560 ◽

Cited By ~ 10

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

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Data-Driven Models for Canopy Temperature-Based Irrigation Scheduling

Transactions of the ASABE ◽

10.13031/trans.13901 ◽

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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