Data-Driven Integration of Remote Sensing, Agro-Meteorology, and Wireless Sensor Networks for Crop Water Demand Estimation: Tools Towards Sustainable Irrigation in High-Value Fruit Crops

Abstract

Despite advances in precision irrigation, no systematic review has yet integrated the roles of remote sensing, agro-meteorological data, and wireless sensor networks in high-value, water-sensitive crops such as mango, avocado, and vineyards. Existing research often isolates technologies or crop types, overlooking their convergence and joint performance in the field. This review fills that gap by examining how these tools estimate crop water demand and support sustainable, site-specific irrigation under variable climate conditions. A structured search across major databases yielded 365 articles, of which 92 met the inclusion criteria. Studies were grouped into four categories: remote sensing, agro-meteorology, wireless sensor networks, and integrated approaches. Remote sensing techniques, including multispectral and thermal imaging, enable the spatial monitoring of vegetation indices and stress indicators, such as the Crop Water Stress Index. Agro-meteorological data feed evapotranspiration models using temperature, humidity, wind, and radiation inputs. Wireless sensor networks provide continuous, localized data on soil moisture and canopy temperature. Integrated approaches combine these sources to improve irrigation recommendations. Findings suggest that combining remote sensing, wireless sensor networks, and agro-meteorological inputs can reduce water use by up to 30% without yield loss. Challenges include sensor calibration, data integration complexity, and limited scalability. This review also compares methodologies and highlights future directions, including artificial intelligence systems, digital twins, and affordable Internet of Things platforms for irrigation optimization.

1. Introduction

Water management in agriculture is a critical challenge exacerbated by climate change, population growth, and increasing food demand [1,2,3]. Irrigated agriculture accounts for approximately 70% of global freshwater withdrawals; however, inefficient irrigation practices often lead to resource overexploitation, declining crop productivity, and environmental degradation [4,5,6]. These issues are particularly acute for high-value crops such as mango (Mangifera indica), avocado (Persea americana), and vineyards (Vitis vinifera), which are commonly grown in semi-arid and arid regions where water resources are scarce, and weather conditions are unpredictable [7,8,9]. Improving irrigation efficiency by precisely estimating crop water needs becomes a pressing necessity in this context.

Mango and avocado, as tropical and subtropical fruit crops, have substantial water requirements, particularly during the flowering and fruit development stages [7,10]. If subjected to water stress during these stages, avocados and mango trees can prematurely drop their fruits, reduce fruit size, and lower their market value [11,12]. Similarly, vineyards are highly sensitive to water stress, where inadequate irrigation can impact yield, grape quality, and wine characteristics [9,13]. For instance, variations in grape sugar content, phenolic compounds, and acidity are closely linked to water availability during key developmental stages [14,15]. These sensitivities underscore the importance of achieving a spatially accurate distribution of irrigation water for these crops, a task that remains insufficiently addressed by traditional irrigation scheduling methods. In this sense, fixed soil moisture thresholds fail to capture the dynamic nature of crop water needs, which vary across spatial and temporal scales due to differences in soil properties, plant physiology, and microclimatic conditions [16,17,18].

Emerging technologies, including remote sensing (RS), agro-meteorological stations, and wireless sensor networks (WSNs), provide new opportunities for improving irrigation management [19,20]. Through the application of advanced multispectral and thermal sensors, remote sensing plays a fundamental role in enabling comprehensive, large-scale monitoring of crop conditions and canopy health [21]. These tools can offer valuable insights into critical parameters, including vegetation indices, canopy temperature, and water stress indicators, such as the Crop Water Stress Index (CWSI) [22,23,24,25]. These insights facilitate improved decision-making for agricultural practices and enhanced resource management strategies. Agro-meteorological stations enhance these efforts by providing real-time, site-specific measurements of essential environmental variables, including critical factors such as temperature, relative humidity, wind speed, and solar radiation [26,27]. Each of these variables is crucial for accurately modeling evapotranspiration (ET) and estimating the water demand required by crops [28,29,30]. In parallel, WSNs enhance these systems by facilitating localized and continuous monitoring of various aspects, such as soil moisture levels, canopy temperature, and prevailing microclimatic conditions [31,32,33]. This advanced approach allows farmers to implement precise and site-specific irrigation management, significantly improving water use efficiency and overall crop health.

Nevertheless, while these advanced technologies have demonstrated significant utility in the agricultural sector, their isolated application often falls short of addressing the increasing complexity of agricultural water management. RS, for instance, provides essential spatial coverage and valuable insights into various environmental factors; however, it often lacks the temporal resolution necessary for making informed, daily irrigation decisions that can lead to more efficient water use [34,35,36]. Similarly, point-based sensors offer high temporal granularity but are limited in spatial coverage. Consequently, integrating these technologies with other systems could enhance their effectiveness in meeting specific agricultural needs. Therefore, an integrated approach that effectively combines the strengths of these various technologies is crucial for significantly improving irrigation efficiency and promoting resource sustainability.

Recent studies in this field have highlighted the substantial potential of such integration, demonstrating that coupling thermal imaging with comprehensive agro-meteorological data can enhance the accuracy of evapotranspiration models [37,38]. For instance, a study showed that an IoT-based automated irrigation system, guided by soil moisture sensor recommendations, achieved a 30% reduction in water usage while maintaining comparable yields and quality in tomato cultivation [39]. Another study [40] demonstrated that a WSN-based irrigation system, utilizing temperature, humidity, and soil moisture sensors, reduced water usage by up to 34%, enhancing sustainable irrigation practices. Furthermore, integrating sophisticated energy balance models with spatially distributed remote sensing data has dramatically advanced our understanding of the complex interactions between crop-water dynamics and environmental conditions. These integrated systems provide the technical foundation for developing next-generation irrigation strategies tailored to specific crop types, phenological stages, and local environmental conditions.

This review aims to systematically analyze the state-of-the-art integration of RS, agro-meteorology, and WSN for crop water demand estimation in high-value fruit crops. By synthesizing current knowledge and critically evaluating methodologies, the paper seeks to establish a coherent framework applicable across diverse cropping systems, with examples drawn from mango, avocado, and vineyards as representative cases of water-sensitive, high-value crops. This approach offers a pathway toward more efficient and sustainable irrigation practices for high-value crops in a changing climate amid increasing resource scarcity.

2. Methodology: Systematizing Knowledge for the Review

The methodology for this review was designed to systematically explore and critically analyze the body of knowledge on integrating RS, agro-meteorology, and WSN to estimate crop water demand in mango, avocado, and grapevine. This process involved the structured selection of scientific literature, the thematic categorization of relevant studies, the analytical synthesis of their findings, and a rigorous assessment of their quality.

The selection of scientific literature was conducted using reputable databases, including Web of Science, Scopus, IEEE Xplore, and Google Scholar, to capture the breadth of research relevant to the topics of interest. The search strategy employed specific combinations of keywords, including “remote sensing for crop water demand,” “energy balance models,” “wireless sensor networks in agriculture,” “agro-meteorological data for irrigation management,” and “integrated irrigation platforms”. Boolean operators were applied to refine the results and ensure the inclusion of diverse methodologies and technologies. Publications from 2000 to 2024 were prioritized, with a focus on peer-reviewed articles written in English. The keyword combinations used were adapted to the indexing syntax of each database. They consistently included terms such as “remote sensing,” “crop water demand,” “energy balance models,” “wireless sensor networks,” “agro-meteorological data,” “irrigation management,” and the crop names “mango,” “avocado,” and “grapevine.” These combinations were joined using Boolean operators (AND, OR) to enhance precision and maximize thematic coverage across platforms. The inclusion criteria targeted studies investigating mango, avocado, or vineyards or presented methodologies applicable to these crops, specifically emphasizing RS, agro-meteorological data, or WSN for irrigation purposes. Conversely, studies with limited technical details, unrelated to irrigation practices, or not peer-reviewed were excluded. A detailed account of the identification, screening, eligibility assessment, and final inclusion of studies is summarized in the PRISMA [41] flow diagram (Figure 1), which outlines the number of records retrieved, excluded, and retained at each stage of the review process.

To organize the vast amount of information collected, the literature was categorized into four interconnected themes: RS, agro-meteorology, WSN, and integrated approaches. The remote sensing theme focused on multispectral and thermal imaging for monitoring vegetation indices, canopy temperature, and water stress indicators across spatially distributed agricultural systems. Agro-meteorology focuses on the role of environmental parameters such as temperature, humidity, wind speed, and solar radiation, which are critical inputs for evapotranspiration models and irrigation scheduling. Through sensor networks, WSN studies addressed continuous, localized soil moisture monitoring, canopy conditions, and microclimatic variables. The final theme, integrated approaches, explored studies that combined these technologies to create advanced platforms for irrigation management. This thematic categorization enabled a structured analysis of each technology, highlighting its synergies and complementarities. Due to the methodological heterogeneity of the selected studies, which span diverse crops, spatial scales, sensor types, and modeling approaches, a statistical meta-analysis was not feasible. Instead, a qualitative synthesis was employed to compare performance trends, technological maturity, and contextual applicability across the four thematic areas.

The findings were synthesized to construct a coherent narrative emphasizing the importance of integrated approaches for water management in high-value fruit crops. Methodologies were evaluated based on their ability to accurately capture spatial and temporal variations in crop water demand, particularly in heterogeneous cropping systems. Studies were critically assessed to identify gaps, such as the limited scalability of WSN, challenges in validating RS models under diverse environmental conditions, or the lack of integrated systems capable of real-time decision-making. The findings were synthesized to construct a coherent narrative emphasizing the importance of integrated approaches, combining the spatial precision of remote sensing with the temporal robustness of agro-meteorological data and the localized accuracy of WSN. This process emphasizes the dual focus of the review: spatial estimation of water requirements in tropical crops, such as mango and avocado, and continuous monitoring of water status in perennial crops, including vineyards.

The quality assessment of the reviewed studies ensured the reliability and scientific rigor of the synthesis, where methodological rigor was evaluated by examining experimental design, data acquisition techniques, and the robustness of analytical methods. Also, the relevance of each study to the specific crops and technologies under review was a critical consideration, ensuring alignment with the review’s focus. Scientific contributions were assessed based on their novelty, ability to address gaps, and implications for advancing irrigation management practices. Additionally, practical applicability was evaluated by considering the scalability, cost-effectiveness, and feasibility of implementing the studied methodologies in real-world agricultural contexts. Studies that demonstrated high reliability and practical relevance were emphasized in the synthesis, while those with significant limitations were critically noted.

Of the 365 records initially identified, 92 studies met the inclusion criteria and were subjected to qualitative synthesis. These 92 papers form the analytical core of the review, and their methodologies and findings were systematically compared across four thematic areas (RS, agro-meteorology, WSN, and integrated approaches). The remaining references included in the submission (totaling 248) were incorporated to provide contextual background, historical development, or methodological support (e.g., description of vegetation indices, technical details of sensors, or broader discussions on irrigation management). In this way, the paper distinguishes between the primary set of 92 systematically analyzed studies and the additional references used for broader context and literature support.

3. Technologies for Crop Water Demand Estimation

Section 3 reviews the three main technological pillars used to estimate crop water demand: RS, agro-meteorological systems, and WSN. Each technology is discussed in terms of its principles, applications, and limitations, providing the basis for understanding its complementary roles. A comparative summary of their main features is presented later in this section (Table 1), offering an overview of spatial and temporal resolution, scalability, advantages, and constraints. This structure ensures that the reader is first introduced to each technology individually before moving on to their integrated application in Section 4.

3.1. Remote Sensing

The use of RS in agriculture began in the 1970s with the introduction of coarse-resolution satellite platforms, such as Landsat 1, which enabled early land classification and broad-scale vegetation monitoring. Also in this decade, indices like the Normalized Difference Vegetation Index (NDVI) were developed to quantify vegetation health and detect stress conditions [42]. Later, the 2000s saw the emergence of higher-resolution sensors and unmanned aerial vehicles (UAVs), which shifted the focus toward plot-level and site-specific crop monitoring. These developments laid the foundation for integrating multispectral, thermal, and hyperspectral data into real-time irrigation management systems.

Advances in RS technologies have significantly transformed the estimation of crop water demand, providing critical tools for precision irrigation management and sustainable agricultural practices [22,23,30,43]. By utilizing multispectral and thermal sensors deployed on airborne and satellite platforms, these technologies enable the monitoring of plant and environmental parameters with unprecedented spatial and temporal precision [44,45,46]. RS has become an indispensable component of water management strategies in high-value crops such as mangoes, avocados, and vineyards [47,48]. These advancements are exemplified in Figure 2, which presents an RS-based conceptual framework outlining how analytical outputs derived from airborne and satellite platforms can support the estimation of crop water demand and guide precision irrigation practices.

With the increasing availability of high-resolution RS data, attention has shifted toward understanding the specific capabilities of different sensor types, such as multispectral instruments, in detecting plant water stress and informing irrigation decisions. Multispectral sensors capture reflectance data across specific wavelengths of light, providing critical information about vegetation health that can be correlated with water stress levels in crops, commonly in the visible, near-infrared (NIR), and shortwave infrared (SWIR) ranges [44,49,50]. This data type is used to calculate vegetation indices, such as the NDVI, which provides insights into plant health, biomass, and canopy cover and aids in assessing crop water requirements under varying environmental conditions [51,52,53,54]. To complement this, Figure 3 illustrates the remote sensing-driven process by which vegetation indices and canopy temperature measurements contribute to assessing crop water stress and informing adaptive irrigation decision-making.

For most crops, multispectral imaging has proven effective in identifying spatial variability within orchards, enabling site-specific irrigation management [55,56,57,58]. In these systems, the assessment of crop water demand can be significantly enhanced by integrating RS data with ground-based measurements [59,60,61,62]. This integration improves the precision of estimations and facilitates the development of adaptive management strategies that respond to changing environmental conditions. Furthermore, it highlights the importance of interdisciplinary collaboration among agronomists, meteorologists, and data scientists in refining these methodologies.

However, multispectral sensors face limitations, particularly their inability to measure physiological water stress directly. Moreover, their reliance on reflectance data often requires complementary inputs, such as thermal imaging or agro-meteorological data, to gain a comprehensive understanding of crop water requirements [63,64,65].

Thermal sensors, in contrast, measure the emitted infrared radiation from plant canopies, providing accurate estimates of canopy temperature [66]. This temperature is a critical indicator of plant water status, reflecting the balance between transpiration-driven cooling and environmental heat load [67]. Therefore, thermal imaging has been widely adopted for calculating various thermal indices, resulting in a reliable tool for estimating plant water stress [68,69]. In vineyards, thermal sensors have played a crucial role in identifying water stress zones and informing irrigation strategies to maintain yield and grape quality [70,71,72]. Similarly, in mango and avocado orchards, thermal imaging enables the early detection of water stress, allowing for timely interventions to mitigate its effects on fruit development and market quality [56,73,74].

Despite their utility, thermal sensors face limitations, including potential inaccuracies resulting from mixed canopy and soil temperature signals or interference from atmospheric conditions. Moreover, spatial resolution can constrain their effectiveness, mainly when applied to large-scale agricultural systems [75,76,77].

These advantages and limitations are further influenced by the type of platform used to deploy thermal and multispectral sensors, as it determines both the spatial resolution and operational flexibility of data acquisition.

Airborne and satellite platforms facilitate the deployment of these sensors, each offering distinct advantages and disadvantages. For instance, drones with multispectral and thermal cameras provide high-resolution imagery at flexible temporal intervals, making them ideal for monitoring small to medium-sized fields and orchards [78,79,80]. Their ability to capture fine-scale variability enables precise irrigation management in heterogeneous systems; however, the operational limitations of drones, including battery life, flight regulations, and dependence on favorable weather conditions, can restrict their widespread adoption [81,82,83]. On the other hand, satellite platforms offer extensive spatial coverage and long-term monitoring capabilities, which are crucial for large-scale water management and trend analysis [59,84,85,86]. Satellites like Sentinel-2 and Landsat provide consistent multispectral data at regional and global scales, making them valuable tools for agricultural monitoring. However, the lower spatial resolution of satellite imagery can limit its utility in detecting fine-scale variability, a critical factor in managing water stress in heterogeneous crop systems. Additionally, satellite data is susceptible to temporal gaps caused by cloud cover and the fixed revisit schedules of orbital systems, which may impede timely decision-making during critical periods of crop development [87,88,89,90].

To synthesize the comparative characteristics of these technologies, Table 1 presents a comparative analysis that summarizes their key features, including spatial and temporal characteristics, scalability, implementation challenges, and strengths and limitations.

Table 1. Comparative overview of the leading technologies for estimating crop water demand, including their spatial and temporal characteristics, implementation cost, and key strengths and weaknesses.

Technology	Measured Variables	Spatial Resolution	Temporal Resolution	Scalability	Key Advantages	Limitations	Key References
Remote Sensing (RS)	NDVI, LST, ET, canopy cover	Medium to High (10–30 m)	Low (weekly to monthly)	High	Large-scale coverage, historical archives	Low temporal resolution, cloud interference	[91,92,93]
Thermal Infrared Sensors	Canopy temperature	Variable (plant to drone)	Medium-High	Medium	Good indicator of crop water status	Needs calibration, affected by the sunlight angle	[94,95]
UAV with Multispectral Cameras	NDVI, NDRE, canopy reflectance	Very High (<5 cm)	Medium (per flight)	Medium	Flexible, high-res images	Requires pilots, image processing expertise	[96,97,98]
Agrometeorological Stations	Temperature, humidity, wind, radiation	Point-based	High (hourly/daily)	Medium	Reliable ground truth data	Sparse coverage, high maintenance	[99]
Wireless Sensor Networks (WSN)	Soil moisture, canopy temp, leaf wetness	Micro-scale (plant level)	Very High (real-time)	Low-Medium	Continuous monitoring, precision management	High deployment and calibration cost	[100,101]

Table 1. Comparative overview of the leading technologies for estimating crop water demand, including their spatial and temporal characteristics, implementation cost, and key strengths and weaknesses.

Technology	Measured Variables	Spatial Resolution	Temporal Resolution	Scalability	Key Advantages	Limitations	Key References
Remote Sensing (RS)	NDVI, LST, ET, canopy cover	Medium to High (10–30 m)	Low (weekly to monthly)	High	Large-scale coverage, historical archives	Low temporal resolution, cloud interference	[91,92,93]
Thermal Infrared Sensors	Canopy temperature	Variable (plant to drone)	Medium-High	Medium	Good indicator of crop water status	Needs calibration, affected by the sunlight angle	[94,95]
UAV with Multispectral Cameras	NDVI, NDRE, canopy reflectance	Very High (<5 cm)	Medium (per flight)	Medium	Flexible, high-res images	Requires pilots, image processing expertise	[96,97,98]
Agrometeorological Stations	Temperature, humidity, wind, radiation	Point-based	High (hourly/daily)	Medium	Reliable ground truth data	Sparse coverage, high maintenance	[99]
Wireless Sensor Networks (WSN)	Soil moisture, canopy temp, leaf wetness	Micro-scale (plant level)	Very High (real-time)	Low-Medium	Continuous monitoring, precision management	High deployment and calibration cost	[100,101]

The combined application of multispectral and thermal sensors, whether airborne or satellite-based, offers a powerful toolkit for assessing crop water demand. Nevertheless, effectively using these technologies often requires integration with other data sources, such as agro-meteorological stations and WSN. This integration can help overcome the inherent limitations of individual technologies, enabling comprehensive and dynamic water management strategies that address the complexity of agricultural systems. The subsequent sections will explore the potential of such integrated approaches to revolutionize water management in high-value cropping systems.

3.2. Agro-Meteorological Data

Agro-meteorological data form the backbone of precise irrigation management systems, providing real-time, site-specific measurements of environmental conditions that directly influence crop water demand [102]. They measure key variables, including temperature, relative humidity, wind speed, and solar radiation, which are crucial for estimating evapotranspiration (ET) and modeling crop water requirements [103,104]. These stations are often strategically positioned within agricultural fields to collect data representative of local microclimates, which can vary significantly across a single orchard or vineyard due to topography, vegetation, and other factors [105,106]. As shown in Figure 4, agro-meteorological stations measure core variables that influence evapotranspiration and vapor pressure deficit (VPD), two fundamental processes in estimating crop water demand.

Temperature is a fundamental variable that influences plant physiology, including transpiration and photosynthesis rates [107]. It also plays a critical role in calculating VPD, a measure of the atmospheric drying power, which is closely linked to plant water loss [108,109]. Regarding relative humidity, temperature determines the vapor pressure deficit (VPD), which in turn influences stomatal behavior and water uptake efficiency [110,111,112]. Aside from temperature and humidity, other environmental factors can also regulate plant water dynamics. Wind speed, for instance, influences the boundary layer around plant leaves, modulating transpiration rates by enhancing water vapor exchange between the leaf surface and the surrounding air [113]. Meanwhile, solar radiation provides the energy driving photosynthesis and evapotranspiration processes, making it a critical component of energy balance models [114,115,116].

Table 2. Key Agrometeorological Variables and Their Importance for Crop Water Demand Estimation.

Variable	Sensor Type	Unit	Relevance to Irrigation Management	Typical Sensor Examples
Air Temperature (T_air)	Thermometer/Thermistor	°C	Used in ET models (e.g., Penman-Monteith); heat stress	HOBO, Davis Vantage Pro
Relative Humidity (RH)	Hygrometer	%	Impacts evapotranspiration and plant transpiration	Sensirion SHT31, DHT22
Solar Radiation	Pyranometer/Quantum Sensor	W/m2 or µmol/m2s	Drives ET; used in radiation-based ET models	Apogee SP-110, LI-COR LI-200
Wind Speed	Cup/Ultrasonic Anemometer	m/s	Affects boundary layer conductance and ET rate	Davis, RM Young
Rainfall	Tipping Bucket Rain Gauge	mm/day	Direct input of water; essential for water balance	Texas Electronics, Decagon
Atmospheric Pressure	Barometer	hPa	Minor in ET models, but useful for evapotranspiration	Bosch BMP280, Vaisala PTB110
Dew Point Temperature	Calculated or measured	°C	Useful for estimating humidity deficit and dew formation	Derived from T_air and RH

summarizes these key agro-meteorological variables, detailing their units, types of sensors, relevance to irrigation modeling, and commonly used devices in the field.

Agro-meteorological data are crucial in energy balance models, which are widely used to estimate crop evapotranspiration (ET) and assess plant water status. These models rely on accurate measurements of environmental variables to quantify the energy fluxes involved in soil and plant water dynamics [117,118]. For instance, the Penman-Monteith equation, a standard model for estimating ET, requires temperature, humidity, wind speed, and solar radiation inputs to calculate the energy balance at the leaf or canopy level [119,120,121]. In vineyards, energy balance models have been utilized to correlate environmental conditions with grape water status, enabling precise irrigation scheduling that minimizes stress while preserving yield and quality [13,122,123]. Similarly, in mango and avocado orchards, these models provide insights into the spatial and temporal variability of water requirements, enabling efficient resource allocation and improved crop performance [48,124,125,126]. Figure 5 illustrates how agro-meteorological data, energy balance models, and WSNs can be integrated into digital platforms to support adaptive irrigation scheduling.

To enhance the effectiveness of these models, agro-meteorological data is increasingly being integrated with sensor networks and digital platforms. WSNs enhance the spatial resolution of environmental data by deploying multiple nodes across a field, each capable of collecting localized measurements [127,128]. When combined with agro-meteorological stations, WSNs form robust systems that capture macro- and microclimatic conditions [20]. Digital platforms serve as the interface for these systems, aggregating and visualizing data from various sources, including agro-meteorological stations, WSNs, and RS products, to provide actionable insights through real-time analytics and decision-support tools. Farmers and researchers can visualize spatial and temporal crop water demand by integrating energy balance models into these platforms, automating irrigation recommendations, and implementing adaptive management strategies tailored to specific field conditions. This convergence of data enhances precision agriculture, optimizing water use and improving overall crop resilience.

Despite their significant contributions, the effective use of agro-meteorological data in agriculture faces several challenges. For instance, establishing and maintaining agro-meteorological stations can be prohibitive for small and medium-sized farms, limiting their widespread adoption. Additionally, sensor calibration issues, equipment degradation, and environmental interferences can affect data accuracy, requiring frequent maintenance and validation [129,130,131]. Nevertheless, technological advances and open-access digital platforms are progressively overcoming these barriers and making agro-meteorological data more accessible. As a critical component of integrated water management systems, agro-meteorological data bridges the gap between local observations and large-scale agricultural planning. When combined with RS and WSN, it provides a comprehensive foundation for developing innovative irrigation solutions that respond to the complex realities of modern agriculture. Building on this foundational role, the following sections delve deeper into how agro-meteorological data, combined with sensing technologies and remote tools, enable transformative irrigation strategies in mango, avocado, and vineyard production systems.

3.3. Wireless Sensor Networks

WSNs have emerged as a transformative technology in precision agriculture, offering localized and continuous monitoring of critical environmental and crop conditions [132]. These networks comprise distributed sensor nodes that monitor critical environmental variables, including air temperature, relative humidity, wind speed, and canopy temperature. The collected data is wirelessly transmitted to a central hub for analysis, providing real-time insights into the microclimatic conditions that influence crop water demand. WSNs are particularly effective in heterogeneous agricultural systems, where spatial variability within fields requires high-resolution data to optimize irrigation and management decisions [19,24,33]. Figure 6 illustrates the components and data flow within WSNs, emphasizing how environmental variables are sensed, transmitted, and processed to support real-time irrigation optimization.

To ensure long-term performance in remote deployments, techniques such as duty cycling (alternating between active and sleep states) and solar-powered units are commonly adopted to enhance energy efficiency [133]. WSNs also enable the continuous monitoring of environmental variables critical for understanding and managing crop water requirements. Among these, air temperature and relative humidity are fundamental in calculating the vapor pressure deficit (VPD), an indicator of the atmospheric demand for water by plants [134]. Wind speed influences transpiration rates by modifying the boundary layer around leaves, while canopy temperature, measured using infrared sensors, directly indicates plant water status [113]. This real-time monitoring capability is especially valuable in dynamic agricultural environments with complex microclimates, such as orchards and vineyards [135]. Figure 7 illustrates the application of WSNs in high-value crops, such as mango, avocado, and vineyards, and how their integration with remote sensing and agrometeorological data supports sustainable water management.

For example, in vineyards, sensors are often placed on opposite sides of vine rows (north- and south-facing sides in the case of the Northern Hemisphere) to capture differences in canopy microclimate caused by sunlight exposure. This positioning allows measurement of vapor pressure deficit (VPD) and canopy temperature variability associated with row orientation and solar incidence, rather than slope effects [71,136]. In mango and avocado orchards, WSNs simultaneously monitor soil moisture and atmospheric conditions, allowing for the detection of water stress zones and facilitating efficient irrigation scheduling [137,138].

These applications are summarized in

Table 3. Summary of key applications, benefits, challenges, and representative studies involving Wireless Sensor Networks (WSN) for crop water demand estimation.

Application/Feature	Integrated Sensor Types	Benefits for Water Management	Challenges/ Limitations	Key References
Soil Moisture Monitoring	Tensiometers, capacitive, and FDR sensors	Enables site-specific irrigation based on real-time data	Sensor calibration, soil heterogeneity	[139,140,141,142]
Canopy Temperature Monitoring (CWSI)	Infrared thermometers (e.g., MLX90614)	Detects plant stress before visual symptoms appear	Affected by ambient conditions, cost of deployment	[143,144,145]
Leaf Wetness and Microclimate Data	Leaf wetness, RH, temperature sensors	Supports disease prediction and irrigation timing	Sensor placement critical, prone to failure in the field	[146,147]
Energy-Efficient WSN Protocols	LoRa, ZigBee, Bluetooth Low Energy	Long-range data transmission with low power consumption	Network reliability in dense canopies	[148,149,150]
Autonomous Power Supply	Solar-powered nodes	Enables long-term deployment without external power	Limited by solar exposure and energy storage	[151,152,153]
Decision Support System Integration	WSN + IoT Platforms	Real-time irrigation scheduling and predictive modeling	Interoperability and data fusion	[154,155,156]

, which presents the main WSN-based practices, their sensor types, irrigation benefits, challenges, and supporting literature.

Despite these advantages, several implementation challenges remain: (i) high initial investment costs may limit adoption among small-scale farmers, (ii) sensor degradation, environmental interference, and signal instability in dense or uneven terrains can compromise data reliability, and (iii) regular maintenance and calibration are essential to maintain system performance [134].

Nevertheless, advances in low-cost sensors, energy harvesting, and robust wireless protocols mitigate many of these issues. As these barriers are overcome, WSNs become increasingly viable for many producers. Integrating WSNs with remote sensing and agro-meteorological data amplifies their potential, offering a comprehensive and scalable framework for precision irrigation. These integrated systems bridge the gap between micro-scale sensing and macro-level water management, equipping producers with tools to face the challenges of climate variability, resource constraints, and sustainability.

The following section will explore how these integrated systems are translated into practical applications, transforming water management in mango, avocado, and vineyard production.

4. Models and Integrated Approaches

Energy balance models provide a robust framework for estimating crop water demand and monitoring water stress, enabling precise irrigation management in high-value crops such as mango, avocado, and vineyards [123,137]. These models rely on partitioning energy fluxes within the plant-environment system, combining environmental measurements and physiological responses to quantify water use and stress. Among these, the CWSI is a reliable indicator of plant water status and irrigation requirements [157].

4.1. Energy Balance Models

The CWSI is a key energy balance modeling tool widely used to assess plant water stress [158]. It is based on canopy temperature measurements, which reflect the balance between transpiration-driven cooling and heat absorption from solar radiation. Transpiration is high when a plant is well-watered, resulting in lower canopy temperatures. Conversely, under water stress, transpiration decreases, causing canopy temperature to rise, which serves as a physiological signal for irrigation needs. The CWSI quantifies these variations by incorporating reference temperatures representing transpiring and non-transpiring conditions, making it a robust comparative index under varying environmental scenarios [159,160].

Beyond its value as a physiological indicator, canopy temperature also plays a central role in physical models of surface energy balance, which provide a more detailed and process-based estimation of plant water use.

Energy balance models partition the net radiation into latent heat, sensible heat, and soil heat flux. The latent heat flux, which is directly related to evapotranspiration, is estimated using models such as the Penman-Monteith or other surface energy balance approaches [161,162,163]. In these models, canopy temperature, measured with thermal sensors, is critical in assessing the sensible heat component and, subsequently, the CWSI.

The CWSI is calculated as a normalized ratio between the observed canopy temperature (Tc), the temperature of a well-watered plant (Tll), and the temperature of a non-transpiring plant (Tul) [160]. This formulation yields a dimensionless value representing the degree of water stress, where values close to zero indicate no stress, and values close to one indicate severe stress.

As illustrated in Figure 8, this approach integrates canopy temperature and reference thermal thresholds to assess plant water stress and is validated by field data. It is further integrated with remote sensing and Wireless Sensor Networks (WSN).

Applying energy balance models and the CWSI in mango, avocado, and vineyards has demonstrated substantial potential for improving water management practices. In mango orchards, where water stress reduces fruit set and size, using CWSI has optimized irrigation schedules, thereby improving yield and fruit quality [137]. In avocado, energy balance models have supported precise water use during critical phenological stages, reducing unnecessary applications while maintaining productivity [164]. In vineyards, the integration of CWSI with thermal imagery and agro-meteorological data has enabled the identification of stress zones, guiding irrigation decisions that preserve both grape quality and yield [70,165,166].

Validation and calibration are crucial for ensuring the reliability of these models across various crops and environments. This process often involves comparing model outputs with in-field measurements such as soil moisture content, stem water potential, or stomatal conductance. For example, in mango and avocado, CWSI models have been calibrated using data from pressure chambers and sap flow sensors, ensuring their physiological relevance. In vineyards, calibration efforts often include data from thermal sensors placed on multiple sides of the canopy (north, south, and nadir) to account for spatial variability in microclimate [167].

While energy balance models offer strong theoretical foundations and have demonstrated effectiveness, their implementation poses challenges. For instance, the accuracy of predictions depends heavily on the quality of input data, particularly canopy temperature and environmental parameters, which require high-performance sensors and standardized data acquisition protocols [168,169]. Additionally, site-specific calibration is often necessary to account for crop-specific and regional variability, increasing model complexity and deployment time. Nonetheless, recent advances in thermal sensing, remote monitoring technologies, and computational modeling have made these tools more accessible.

Integrating energy balance models with remote sensing, agro-meteorological data, and wireless sensor networks represents a promising step toward developing holistic, scalable systems for real-time irrigation management. These integrated models enhance the precision of crop water assessments and provide a pathway to more sustainable and resilient agricultural practices, especially in high-value and water-sensitive crops such as mango, avocado, and vineyards. To fully realize their potential, these models must be integrated into digital platforms that combine multiple data sources, enabling the real-time integration of sensing technologies for site-specific irrigation decisions.

4.2. Integration of Remote Sensing, Agro-Meteorology, and Sensor Networks

Integrating remote sensing, agro-meteorological data, and WSN creates a robust decision-support ecosystem for irrigation management (Figure 9). This integration combines the spatial granularity of RS, the temporal accuracy of weather stations, and the site-specific detail provided by WSNs, producing datasets that can drive predictive models for irrigation scheduling [170,171,172].

These systems use advanced data fusion techniques, including machine learning, geospatial modeling, and dynamic simulations. RS contributes vegetation indices, canopy temperature, and stress maps, while agro-meteorological stations provide high-frequency data on solar radiation, wind, temperature, and humidity. WSNs offer real-time measurements of soil moisture and canopy conditions. These datasets are integrated using GIS tools or cloud platforms, enabling the modeling of predictive water demand and stress.

For example, an integrated model may utilize satellite-based NDVI and thermal imagery, real-time temperature and humidity data from weather stations, and plant-level canopy temperatures from WSNs to generate tailored irrigation prescriptions [173,174]. The combination enables daily or hourly irrigation decisions, which are crucial in rapidly changing field conditions.

The advantages of these integrated systems lie in their ability to overcome the limitations of individual technologies. RS alone lacks temporal resolution; weather data may be too generalized for large fields; and WSNs, while precise, are limited in spatial scope [175]. Integration bridges these gaps, providing a comprehensive understanding of plant-water dynamics across multiple scales. This results in better identification of stressed areas, optimized irrigation planning, and reduced water waste—key components of sustainable agriculture.

A central feature of these integrated systems is the online platform, which serves as the operational layer, aggregating incoming data and producing user-friendly dashboards. Through cloud access, users can visualize water status, receive irrigation alerts, and simulate future scenarios based on climate forecasts [176]. Additionally, predictive analytics and modeling tools are integrated into the platform to automate decision-making and design systems that can accommodate additional sensors and crops over time, ensuring scalability [177].

The effectiveness of these platforms depends on data interoperability, intuitive interfaces, and affordable deployment [178]. Compatibility with multiple sensor types, consistent calibration, and data standardization are essential. Platforms must also be accessible to end-users without requiring technical expertise, especially in regions where digital literacy may vary [23].

5. Practical Applications and Case Studies

The practical application of advanced agricultural water management technologies demonstrates their potential to enhance efficiency, sustainability, and productivity in high-value crops. Focusing on mango, avocado, and vineyard systems, this section examines how integrating spatial and temporal data, through multispectral and thermal sensors, WSNs, and energy balance models, has been implemented to address water stress. It also presents a comparative analysis of these approaches in terms of cost, precision, and long-term sustainability, highlighting the benefits and limitations in real-world contexts.

Mango and avocado orchards present unique challenges and opportunities for precision irrigation due to their sensitivity to water stress and the variability of microclimatic conditions [12,179]. Multispectral sensors deployed via drones or satellite platforms provide spatial data on vegetation indices such as the NDVI, which correlates with canopy vigor and leaf area [83]. These indices allow the identification of suboptimal zones within orchards. Thermal sensors, mainly when used to calculate the CWSI, complement this information by measuring canopy temperature, a direct indicator of water stress [180,181]. Together, these tools enable the estimation of spatial water demand, allowing for targeted irrigation that optimizes water use without compromising fruit yield or quality.

Early stress detection using these technologies has helped prevent quality loss and optimize irrigation scheduling in avocado orchards [137,182]. However, adoption can be limited by high equipment costs and the need for technical expertise, emphasizing the importance of training and support programs.

In contrast, vineyards represent a mature use case for continuous monitoring via WSNs and energy balance models [13,122,123]. Sensor networks deployed in vineyards provide high-frequency data on canopy temperature, air temperature, relative humidity, and soil moisture. When integrated with agro-meteorological data, these measurements feed energy balance models that calculate real-time evapotranspiration (ET) and water stress indices [118,167,183]. Strategic sensor placement—on the north and south sides of vine rows and nadir positions—captures microclimatic variability, supporting precision irrigation tailored to specific zones [167].

Integrating WSNs with energy balance modeling in Chilean vineyards has resulted in water savings of up to 25% while maintaining key grape quality parameters, such as sugar content and acidity [70]. These systems have also proven effective in adapting irrigation to changing climatic conditions, contributing to resilience and productivity. Nonetheless, their success hinges on sensor calibration, maintenance, and reliable data transmission, particularly in hilly or remote areas [27,149,153].

When comparing technologies across mango, avocado, and vineyard systems, notable operational differences emerge in terms of cost, precision, and sustainability (Figure 10). RS systems, including UAVs and satellites, provide valuable spatial data but are limited by episodic acquisition and trade-offs in resolution and coverage [78]. WSN, by contrast, offers continuous, localized measurements with lower operational costs over time but requires initial infrastructure investment and ongoing maintenance [33,148,150].

Regarding precision, thermal sensors and energy balance models provide direct physiological indicators of plant stress, which NDVI alone cannot achieve. This is particularly advantageous in vineyards, where slight variations in water status can significantly affect grape quality. However, in larger orchards, such as those for mangoes and avocados, the broader spatial coverage of remote sensing may be more beneficial, as it enables the identification of overall stress patterns and the optimization of water distribution at scale.

Sustainability considerations also differ. RS systems have a lower environmental footprint, requiring minimal on-site infrastructure, though their intermittent nature may miss rapid stress fluctuations [78]. When combined with either sensing method, energy balance models offer a sustainable analytical framework for reducing water use and enhancing decision-making across all crop types. Despite these promising outcomes, real-world implementations have also revealed critical challenges that must be addressed to ensure the long-term viability, equity, and scalability of these technologies.

6. Challenges and Future Perspectives

Integrating advanced technologies, such as remote sensing, agro-meteorological systems, and WSNs, has demonstrated immense potential in transforming water management in agriculture. However, their widespread adoption and long-term impact remain limited by technical constraints, scalability issues, and the complex interplay between technological innovation and socio-environmental objectives such as water sustainability and food security [184,185,186]. Addressing these barriers requires a critical evaluation of current limitations and a forward-looking strategy to ensure effective deployment across diverse agricultural contexts.

Technical and operational limitations continue to hinder the effectiveness of these tools. Although valuable for spatial analysis, RS systems often suffer from low temporal resolution [187]. For instance, satellite-based platforms like Landsat or Sentinel-2 deliver imagery every few days, which may not coincide with rapid changes in crop water status during key phenological stages [188,189]. Drone-based systems provide higher resolution and greater flexibility, but face constraints related to flight regulations, battery life, and the need for skilled operators. Additionally, environmental factors such as cloud cover or atmospheric interference may compromise data quality, particularly for optical or thermal sensors [80,190,191]. This issue is summarized in Figure 11, which outlines the key technical limitations of remote sensing and WSN technologies and their impact on system effectiveness.

While WSNs enable continuous and localized monitoring, they face critical issues. These include sensor degradation due to harsh field conditions (e.g., high humidity, heat, pests), signal transmission failures in dense vegetation or hilly terrain, and energy supply limitations [192,193]. Maintaining these networks over time requires robust communication protocols, reliable energy sources, and routine maintenance. Moreover, interoperability with RS and agrometeorological data remains a significant hurdle, primarily due to the lack of standardized data formats and integration platforms [194].

Scalability is another primary concern, especially in regions with diverse agricultural systems and limited infrastructure. Estimating energy balance models typically requires site-specific field data, increasing labor demands and costs [195]. In addition, smallholder farmers often face greater barriers, including limited internet access, fewer technical resources, and a lack of access to affordable equipment and services [33]. Figure 12 illustrates the relationship between the benefits and constraints of current technologies and the systemic factors that influence scalability and sustainability.

From a broader perspective, the potential impact of these technologies on water sustainability and food security is both promising and complex. Additionally, integrated irrigation systems can achieve significant water savings and improve yield stability, contributing to climate resilience and food security. On the other hand, uneven access to digital tools may exacerbate existing inequalities between large-scale and smallholder farmers. This risk-benefit balance is summarized in

Table 4. Current Challenges and Future Perspectives in Crop Water Demand Monitoring Technologies.

Challenge/ Perspective	Affected Domain(s)	Description/Implication	Opportunity/ Emerging Solutions	Key References
Fragmented Data Sources	Sensor integration, modeling	Difficulty in merging WSN, RS, and weather data due to format and temporal mismatches	Standardized data protocols and interoperable platforms	[196,197]
Limited Adoption in Smallholder Contexts	Socioeconomic scalability	High-tech solutions are often inaccessible to small farms	Open-source tools, low-cost sensors, and targeted training	[198,199,200]
Calibration and Validation Complexity	Model reliability, replicability	Field conditions vary widely; sensor-based models require site-specific tuning	Development of adaptive or self-calibrating systems	[201,202,203]
Energy and Connectivity Constraints	Remote field deployment	Power and network infrastructure limit long-term use of WSNs	Solar microgrids, LoRa WAN, and delay-tolerant data systems	[150,204,205]
Data Literacy and Human Capital	Capacity building, adoption	Users struggle to interpret data or apply it to decisions	Intuitive interfaces, gamification, and continuous training	[206,207]
Climate Uncertainty	Model robustness, risk management	Increased variability makes predictions less reliable	Integration of AI/ML with real-time data streams	[200,208,209]
Future: Digital Twins and Edge Computing	Predictive analytics, automation	Simulate crop behavior under scenarios; process data closer to the source	Real-time crop stress forecasting and automated irrigation	[210,211,212,213]

, which outlines key challenges and future opportunities for scaling precision irrigation technologies. These include technical and economic aspects, as well as broader concerns that extend into environmental sustainability, cybersecurity, and digital dependency. Building on these considerations, further risks emerge from the environmental footprint of hardware production, energy consumption, and long-term data governance. Overdependence on digital platforms and automated systems may increase vulnerability to system failures, obsolescence, or data loss, particularly in under-resourced settings.

Additional concerns arise from the environmental footprint and cybersecurity risks of these systems, where overdependence on digital platforms, sensors, and automated tools may introduce vulnerabilities such as data loss, system failure, or technology obsolescence. Furthermore, the production, distribution, and disposal of hardware (e.g., sensors, drones, and batteries) raise sustainability questions aligned with circular economy principles.

Looking ahead, innovation must be guided by affordability, inclusivity, and collaboration. Technological development should prioritize resilient, low-cost, and interoperable solutions that can function under diverse environmental and infrastructural conditions. Machine learning and AI tools can help simplify model calibration, reduce dependency on manual inputs, and support scalable predictive analytics. Edge computing and decentralized data processing can also help mitigate dependence on internet connectivity in rural or remote regions.

Inclusivity is key to unlocking the full potential of smart irrigation. Public–private partnerships, government subsidies, and inclusive training programs must ensure that smallholder farmers and under-resourced communities can access, use, and benefit from these technologies. Networks of trained users, such as farmer cooperatives or local technician associations, can support knowledge transfer and long-term adoption. Collaborative frameworks, such as international research initiatives, will also be essential for scaling innovation and sharing best practices globally.

7. Conclusions

This review systematically examined the integration of RS, agro-meteorology, and WSNs as complementary tools for estimating crop water demand in high-value fruit crops. By analyzing 92 selected studies, we identified key contributions of each technology and highlighted how their combined use provides a more robust and scalable framework for irrigation management.

RS enables spatial monitoring at the canopy to the regional scales. Agro-meteorological data provide continuous insights into the atmospheric drivers of evapotranspiration, and WSNs deliver localized, real-time field information. Their integration reduces uncertainty, improves water allocation decisions, and supports precision irrigation in water-scarce environments.

The primary challenges to adoption include cost, technical complexity, and the requirement for interoperable platforms. However, advances in low-cost sensors, cloud-based analytics, and open-source tools offer promising pathways to overcome these barriers. Future research should focus on harmonizing data streams, developing standardized protocols, and strengthening links between science and practice.

Ultimately, the integration of these technologies can foster sustainable irrigation practices, improving water-use efficiency and resilience of high-value fruit crops. For this potential to be realized, it will be essential to align technical innovation with socioeconomic realities, particularly in developing countries where affordability and accessibility are critical for large-scale adoption.