Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications

Abstract. While infrared thermometry and thermal imagery have potential to detect crop water stress and quantify evapotranspiration, both valuable in irrigation scheduling, it is often difficult to isolate plant canopy temperature from background temperatures. In this study, we demonstrate a simple technique that uses a homogeneous background temperature that contrasts with canopy temperature, thereby allowing the canopy temperature itself to be isolated in a thermal image. Analysis of pixel temperatures and their associated statistics demonstrate the potential of this method to measure small (ca. < 0.5°C) and rapid (ca. < 1 s) fluctuations in leaf energy balance. This technique has broad applicability in greenhouse, growth chamber, and other small-scale experiments where real time response of individual leaves or canopies is required. Keywords: Thermal imaging, Crop water stress, Infrared thermometry, Background masking, Deficit irrigation, Greenhouse.

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Identification of Infiltration Features and Hydraulic Properties of Soils Based on Crop Water Stress Derived from Remotely Sensed Data

Remote Sensing ◽

10.3390/rs13204127 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4127

Author(s):

Jakub Brom ◽

Renata Duffková ◽

Jan Haberle ◽

Antonín Zajíček ◽

Václav Nedbal ◽

...

Keyword(s):

Water Stress ◽

Spatial Variability ◽

Precision Agriculture ◽

Hydraulic Properties ◽

Support Vector ◽

Crop Water ◽

Soil Hydraulic Properties ◽

Crop Water Stress Index ◽

Soil Hydraulic ◽

Crop Water Stress

Knowledge of the spatial variability of soil hydraulic properties is important for many reasons, e.g., for soil erosion protection, or the assessment of surface and subsurface runoff. Nowadays, precision agriculture is gaining importance for which knowledge of soil hydraulic properties is essential, especially when it comes to the optimization of nitrogen fertilization. The present work aimed to exploit the ability of vegetation cover to identify the spatial variability of soil hydraulic properties through the expression of water stress. The assessment of the spatial distribution of saturated soil hydraulic conductivity (Ks) and field water capacity (FWC) was based on a combination of ground-based measurements and thermal and hyperspectral airborne imaging data. The crop water stress index (CWSI) was used as an indicator of crop water stress to assess the hydraulic properties of the soil. Supplementary vegetation indices were used. The support vector regression (SVR) method was used to estimate soil hydraulic properties from aerial data. Data analysis showed that the approach estimated Ks with good results (R2 = 0.77) for stands with developed crop water stress. The regression coefficient values for estimation of FWC for topsoil (0–0.3 m) ranged from R2 = 0.38 to R2 = 0.99. The differences within the study sites of the FWC estimations were higher for the subsoil layer (0.3–0.6 m). R2 values ranged from 0.12 to 0.99. Several factors affect the quality of the soil hydraulic features estimation, such as crop water stress development, condition of the crops, period and time of imaging, etc. The above approach is useful for practical applications for its relative simplicity, especially in precision agriculture.

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Water Stress Regime of Irrigated Crops Based on Remote Sensing and Ground-Based Data

Agronomy ◽

10.3390/agronomy11061117 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1117

Author(s):

Anatoly Mikhailovich Zeyliger ◽

Olga Sergeevna Ermolaeva

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Crop Yield ◽

Satellite Monitoring ◽

Agricultural Crop ◽

Crop Water ◽

Stress Regime ◽

Irrigation Water Use ◽

Crop Water Stress ◽

Crop Irrigation

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.

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