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A two-year field experiment was conducted to integrate soil moisture stress with the Crop Water Stress Index (CWSI) for optimizing irrigation in winter wheat (Triticum aestivum L.) under varying irrigation regimes. The study took place at the Water Technology Centre (WTC-02) of ICAR-IARI, New Delhi, where the climate shows a blend of monsoon-influenced humid subtropical and semi-arid conditions. Using a randomized block design (RBD), five irrigation treatments were applied: full irrigation and deficit irrigation (DI) at 15 %, 30 %, 45 %, and 60 % levels. Canopy and ambient air temperature data, along with vapor pressure deficit (VPD), were recorded using a developed integrated sensing device to empirically determine the lower baseline equations and upper threshold for CWSI computation at pre-heading and post-heading stages. The slope (m), intercept (c) of the lower baseline equation, and upper threshold (UL) for pre-heading and post-heading were found: m: −1.94, c: −1.33, UL: 1.92°C and m: −1.30, c: −2.37, UL: 2.0°C, respectively. Results showed that increasing water deficit levels led to significant reductions in grain yield, biomass production, and harvest index. A strong negative correlation (R² = 0.95 and 0.93) between mean seasonal CWSI and yield attributes highlighted the utility of CWSI in yield prediction under varying irrigation regimes. It is recommended to schedule irrigation based on the CWSI approach when CWSI ≥0.35 for optimum wheat yields. Integrating CWSI with soil moisture stress provides valuable real-time insights into crop water status, enabling more precise and smart irrigation scheduling.

The availability of field experimental data plays a pivotal role in advancing agricultural research, particularly in the Mediterranean, where farmers face significant challenges due to water scarcity and changing climatic conditions. We present a multi-year homogenized dataset of agro-physiological traits collected on industrial tomatoes and focused on the effect of deficit irrigation (DI). The dataset has been compiled over nine years and comprises 100 experimental plots, where 32 DI strategies have been tested. Visual observations on tomato phenology and qualitative and quantitative production data have been collected in field and laboratory surveys, complemented with detailed information on pedo-climatic conditions and irrigation scheduling (timing and volume). Researchers can find in this dataset a rich source for calibrating and evaluating agro-physiological models and a reference basis to study the relationships between DI strategies, weather variability, and the performance of tomato growing systems. Agronomists from the public and private sectors can gain domain knowledge to support local farmers with the best DI strategies to achieve high yields while optimizing water use. Moreover, this dataset serves as ground truth for digital decision support systems, which need real-world data to enhance their accuracy in guiding farmers on efficient water use. This data source is intended to become a crucial asset for researchers, agronomists, and decision-makers in the Mediterranean as it bridges the gap between research and practice in an area where farmers are already striving with water scarcity for industrial tomato cultivation.

The main limitation of conventional methods generally used for monitoring the crop water stress lies in the expenditure of time and laboriousness (e.g., stem water potential, Ψstem). In this sense, infrared thermography can assist to identify the crop water status for precise irrigation purposes. However, this method requires high cost equipment and heavy systems for real-time analysis of the results. The aim of this research was to evaluate the reliability of a portable low-cost sensor (FLIR One Pro), connectable to a smartphone, for determining the water status of orange trees subjected to different treatments (i.e., full irrigation versus regulated deficit irrigation with or without soil mulching). The thermal information obtained from FLIR One Pro was compared with the data acquired with a professional thermal camera (Optris Xi 400) for two consecutive years (2021–2022). In addition, the reliability of the low-cost sensor was assessed, in respect to the loss of accuracy due to the sensor’s price reduction, in identifying the crop water stress index (CWSI) and the upper and lower baselines. A good agreement was obtained between the canopy temperature (Tc) and the references leaves temperatures (dry leaf, Td; and wet leaf, Tw) provided by both sensors, resulting in coefficient of determination (R2) of 0.89, 0.82 and 0.75, respectively. The CWSI comparing the two sensors provided a R2 of 0.75. No influence of the agrometeorological conditions and overheating of the low-cost sensor on the Tc was found, however it is advisable to keep the low-cost sensor under homogeneous weather conditions during the acquisition process. No correlation was found between the CWSI and the Ψstem, due to the isohydric behavior of citrus species. Finally, this study opens new insights for spreading the use of low-cost thermal sensors for speeding up the crop water status monitoring under field conditions and supporting the adoption of precision irrigation criteria.

The production of fruits and vegetables in a protected environment (e.g., greenhouse) and open field mainly depends on a substantial water supply. Water stress is the most common plant stress, significantly affecting the quality and yield of crops, especially in organic plant cultivation and arid regions. An automated and well-scheduled irrigation system that corresponds to the actual water demand helps reduce the consumption of this limited resource without negatively affecting crop productivity. This study outlines the design, construction, and functionality of an autonomous multi-sensor monitoring system for real-time water stress detection and precisely targeted irrigation called PLANTSENS. The developed prototype combines the absorption-based and the temperature-based measuring method by using three optical sensors operating in the near-infrared, shortwave, and longwave (thermal) infrared spectrum. The camera modules are mounted on a moving rail-based platform with an integrated positioning system. Appropriate networking and cloud environment with a central server for storing, analyzing, and visualizing the acquired data was created. PLANTSENS monitoring system has been successfully implemented and trained on tomato plants in a greenhouse. The algorithm for scheduling irrigation time has been developed by merging two water stress detection algorithms, namely the Water Index (WI) and the Crop Water Stress Index (CWSI), thereby improving the system's capability and accuracy. WI determines the plant stress from the reflection differences at 1450 and 1300 nm, where water absorbs strongly and weakly, respectively. CWSI, on the other hand, uses the leaf temperature, which tends to increase during water stress due to the lower transpiration rate caused by closed stomata. This work presents the performance of investigated measuring methods and developed algorithms based on the system approach from July 2020 through August 2020. The obtained irrigation time has been verified in accordance with observed changes in the plant's physiological response, such as vapor conductivity, validating this approach as promising and confirming water stress as a significant indicator in irrigation scheduling.

The goal of irrigation for wine grape grown in arid or semiarid regions is to sustain vine survival and to optimize berry attributes for quality wine production. Precision irrigation of wine grape is impeded by the lack of a smart, decision support system (DSS) to remotely monitor vine water status. The objectives of this study were to: develop and field test an Internet of Things (IoT) DSS system for precision irrigation of wine grape. The IoT system was comprised of a suite of in situ sensors used to monitor real-time weather conditions, grapevine canopy temperature, soil moisture, and irrigation amount. Sensor data were collected and stored on a field deployed data logger that calculated a daily thermal Crop Water Stress Index (CWSI) for grapevine using a neural network model with real-time sensor data model inputs. The data logger also hosted, via a cellular modem, webpages showing a running, 12-day history of daily CWSI, fraction of available soil moisture (fASW), irrigation amount, and other sensor data. The webpages were accessible to vineyard managers via cell phone or computer. The CWSI based IoT DDS system was installed at two small acreage, commercial estate vineyards in southwestern Idaho USA over four growing seasons. At each vineyard site, the DSS was used daily by the vineyard irrigation manager to schedule irrigation events. Neither vineyard manager used any other quantitative vine water status monitoring tool for irrigation management decisions. The midday leaf water potential (LWP) of grapevines was routinely measured by research project personnel. Data collected over the study period at each vineyard showed a significant (p < 0.001) correlation with LWP and fASW, providing evidence that, under the conditions of this study, the daily CWSI based IoT provided automated, remote monitoring of vine water status. Both vineyard managers reported daily use of the DSS for irrigation scheduling decisions. Over the four-year study, each vineyard manager was able to maintain consistent seasonal average CWSI daily values and irrigation application amounts, despite yearly differences in climatic conditions. The results of this study demonstrate that a CWSI based IoT DSS can be used for precision irrigation of wine grape in a commercial vineyard under semiarid growing conditions. The CWSI based IoT DSS used a unique combination of neural network modeling, edge computing, and IoT for real-time vine water stress monitoring for precision irrigation.

Plant-based measurements such as leaf water potential (LWP) are widely used for irrigation scheduling because they are accurate at indicating when irrigation is needed. Despite being a good indicator, scheduling irrigation with LWP is time consuming and scale-limited. The work reported in this study explored the potential of using thermal remote sensing to estimate cotton crop water status in the humid southeastern U.S.A. The study was conducted over two growing seasons (2018 and 2020) in southwestern Georgia, U.S.A using a complete randomized block design plot scheme with three irrigation treatments (0% ETc (crop evapotranspiration; rainfed), 100% ETc (well-irrigated), and 125% ETc (over-irrigated). To monitor the irrigation treatment effects on cotton physiological responses, predawn LWP (LWPPD), stomatal conductance (gs) and leaf area index (LAI) data were collected in both growing seasons. UAV-based images collected in the thermal infrared waveband were used to calculate crop water stress index (CWSI) based on three different methodologies and evaluated as predictors of LWPPD. Results in this study suggest that LWPPD values above -0.45 MPa indicate a non-stressed crop. No negative effects in leaf stomatal conductance and crop growth were observed in 2018. In 2020, the less and more irregular precipitation led to significant differences in LAI and gs, as well as in LWPPD. A moderate to strong relationship was observed for all dates in 2020, with the CWSI based on the Monteith approach (CWSIMonteith) showing the two highest R2 values among the 3 dates (0.65 and 0.58) with low RMSE values of 0.02 and 0.04 MPa, respectively. Overall, the results showed that there is potential of using an affordable UAV-based thermal system to produce predicted LWP maps that are representative of the current field water status.