Democratizing IoT for Smart Irrigation: A Cost-Effective DIY Solution Proposal Evaluated in an Actinidia Orchard

Abstract

Proper management of water resources in agriculture is of utmost importance for sustainable productivity, especially under the current context of climate change. However, many smart agriculture systems, including for managing irrigation, involve costly, complex tools for most farmers, especially small/medium-scale producers, despite the availability of user-friendly and community-accessible tools supported by well-established providers (e.g., Google). Hence, this paper proposes an irrigation management system integrating low-cost Internet of Things (IoT) sensors with community-accessible cloud-based data management tools. Specifically, it resorts to sensors managed by an ESP32 development board to monitor several agroclimatic parameters and employs Google Sheets for data handling, visualization, and decision support, assisting operators in carrying out proper irrigation procedures. To ensure reproducibility for both digital experts but mainly non-technical professionals, a comprehensive set of guidelines is provided for the assembly and configuration of the proposed irrigation management system, aiming to promote a democratized dissemination of key technical knowledge within a do-it-yourself (DIY) paradigm. As part of this contribution, a market survey identified numerous e-commerce platforms that offer the required components at competitive prices, enabling the system to be affordably replicated. Furthermore, an irrigation management prototype was tested in a real production environment, consisting of a 2.4-hectare yellow kiwi orchard managed by an association of producers from July to September 2021. Significant resource reductions were achieved by using low-cost IoT devices for data acquisition and the capabilities of accessible online tools like Google Sheets. Specifically, for this study, irrigation periods were reduced by 62.50% without causing water deficits detrimental to the crops’ development.

1. Introduction

Several factors pose challenges to the agricultural sector, including climate change [1], population growth [2], and even policies within the industry itself [3]. Together, these factors undermine sustainability, revealing vulnerabilities during events like COVID-19 [4], which triggered an unprecedented food crisis driven by restrictions imposed at a governmental level over food importation, thus rising inflation [5].

In the context of climate change, long-term consequences such as the desertification of previously arable lands and alterations to the water cycle are becoming increasingly evident [6]. Such water cycle alterations raise risks for potable water sources due to contamination-inducing factors (e.g., through the leaching process) and scarcity (e.g., drought-induced), significantly impacting water-dependent sectors. In turn, these impacts threaten the entire agricultural supply chain, as water provision and safety for human consumption and work-related activities—considering both the quantity (hydrology) and the quality of water (type of use, transport, and contaminant loads through soils, forests, and urban water infrastructures)—are compromised. These issues are also inherently associated with socioeconomic and public health risks [7].

At the global scale, climate change has resulted in an average surface temperature increase of 1.1 °C since the pre-industrial period (1850–1900), with the past 50 years marking the most rapid warming phase in the last two millennia. This warming has also had significant implications for sea levels, which have risen from 2.6 mm/year in the early 1990s to over 4.2 mm/year between 2007 and 2022, driven by widespread glacial retreat, ocean heat accumulation, and changes in atmospheric humidity and precipitation patterns. These changes have increased the frequency and intensity of extreme events, including floods, heatwaves, and droughts [8]. In India, for example, farmers reported crop losses of up to 40% due to drought events occurring with greater regularity. Moreover, the lack of adequate administrative mitigation measures to support them remains a persistent challenge [9]. Another example is the 2022 floods in Pakistan, which inundated approximately 2.67 million acres of cropland in Punjab province alone, demonstrating how sudden changes in precipitation patterns can devastate agricultural systems [10]. Both cases clearly illustrate the severe threat that climate change poses to food security on a massive scale, reinforcing the critical need for climate-resilient strategies, particularly those focused on water management.

Within a more focused geography, the Iberian Peninsula is among the European regions most vulnerable to climate change. Observations indicate an increase in average surface temperature ranging from 0.75 °C to 1.5 °C since the pre-industrial era. Per decade, the number of hot days has risen by up to 6, while heatwaves have increased by up to 10. Conversely, the frequency of cold days has been declining. Precipitation extremes have also intensified, despite an overall reduction in total rainfall (−44.6 mm per decade), with more rain concentrated in fewer yet more intense events [11].

In Portugal, these regional patterns are reflected in prolonged dry periods, temperature anomalies, and a growing mismatch between water availability and agricultural demand. In particular, spring and summer months have become drier, increasing the likelihood of wildfires and reducing crop resilience. Moreover, in recent years, an increase in the frequency of climate anomalies has been witnessed, especially regarding drought duration and interannual variability in rainfall [12].

Aware of the urgent need to adopt a pragmatic approach integrating rational water use within the context of climate change, international treaties such as the Paris Agreement—consisting of a set of legally binding guidelines towards the accomplishment of the Sustainable Development Goals (SDG) [13]—have been established and broadly discussed, heavily relying on water resources. In contrast, work groups such as the United Nations Intergovernmental Panel on Climate Change (IPCC) have proposed climate change mitigation strategies that require considering water’s role more adequately [6].

That said, eco-conscious water management in crop irrigation activities ensures continuous and sustainable worldwide access to this vital resource with impact in global food security. Traditional irrigation through conventional management practices is likely to lead to an inadequate use of water in crops, causing various issues, from water stress to soil leaching, with short and long-term impacts on productivity and sustainability. While some farmers resort to deficit irrigation techniques to save up 13% of water, poorly performed procedures can reduce crop yields by up to 25%. Conversely, smart irrigation has been tackling these issues through the use of a panoply of sensors to measure—for example, soil moisture, evapotranspiration, and precipitation, among others—which can lead to water savings between 20% and 92% while maintaining crop quality and sustainable growth [14].

Commonly embedded in the Internet of Things (IoT) paradigm, sensors enable real-time data transmission for monitoring and control purposes that not only facilitate farmers’ access to detailed information about their soil and crops [15] but also foster other relevant aspects such as the evaluation of circular economy performance [16]. Regarding IoT specifically applied to agriculture, decision support can be provided through integrative information systems, allowing farmers to follow crop-related data and adjust irrigation, fertilization, and pesticide application according to specific crop needs and site characteristics, independent of property size, while ensuring the continuous monitoring of plant and related agronomic indicators. Implementing and disseminating such strategies has increased agricultural productivity and optimized resource management, particularly water [17]. In addition, while forecasting and strategic decision-making assistance through alerts or advisory messages aimed at the farmer are also implementable features when processing readings from IoT sensors [18], big data analytics has been highlighted as an operative field with significant impacts on productivity improvements, optimizing the efficiency and sustainability of agricultural practices [16]. Moreover, IoT systems enriched with crop simulation models [19] have the potential to bridge data-driven short-term predictions with long-term projections, providing an extended strategic perspective for agriculture—supporting, for example, the development of climate change-resilient water management policies. Such integration may be truly transformative for farming practices, particularly in a context where extreme weather events are becoming increasingly difficult to predict. Focusing primarily on IoT applications particularly, though not exclusively, in smart irrigation combined with community-accessible, cloud-based tools for data centralization, management, and processing—specifically supported by Google—, the next section addresses a set of related solutions found in the literature.

1.1. IoT and Google Cloud-Based Data Management in Literature

In the literature, some works combining IoT and more traditional data handling/management applicable to farming contexts can be found and aligned with the scope of this paper. For example, the approach proposed in [20] aimed to explore the use of IoT in monitoring soil water content in rice fields. To that end, an IoT device connected with sensors for air temperature, relative humidity, and soil water content was assembled and set up for transmitting real-time data for a Google Sheets instance. Despite the promising results reported, some details for reproducibility are missing, such as the chosen type of soil water content sensor, the total area where the test was conducted, or how the irrigation management was performed based on the obtained data—which are exhibited in tabular format, limiting intuitive interpretability. Additionally, instructions to replicate the system are not detailed. Kibria et al. [21] proposed an IoT-based prototype to monitor temperature, relative humidity, and soil water content, which integrates Google Sheets as a data repository. Additionally, resorting to a Blynk-based software, they developed a water spraying system to irrigate soil and plants, working in articulation with relevant data under monitoring. This system offers the capability to trigger irrigation remotely through a mobile app purposely implemented as an authoring tool. However, it offers a limited set of static features, such as dashboards and water pump control, which seems to require deep recoding to modify or extend and constitutes a barrier for agriculture professionals’ redesign autonomy. Additionally, while the study primarily focuses on the low-cost setup and implementation of a prototype system for remote irrigation, a narrow set of sensors (temperature, humidity, and soil moisture) was employed, restricting its potential for robust decision support. Although the results document several crop developments and water consumption results, the study does not demonstrate significant decision support for optimizing irrigation schedules. Additionally, the proposal appears to lack insights into the system’s applicability in real production environments. The authors of [22] built an IoT-based prototype to monitor temperature and relative humidity and manage a water pump to balance the water content in the soil. Similar to what other researchers did, these authors resorted to Google Sheets as a cloud database and to a web application, which was developed to analyze the data and present a dashboard for decision-making support regarding the activation of a water pump, manually or remotely managed. However, this proposal employs a limited set of sensors for monitoring water needs. Additionally, the study primarily focused on system design and implementation, which carried out exhaustive data sampling—1–2 readings per minute. Furthermore, no validation was conducted to assess irrigation decision support capacity. Also, the soil moisture sensor employed showed issues related to (i) corrosion after a short period of use and (ii) low accuracy [23]. Resorting to similar technologies but for distinct purposes, in another proposal [24], the authors used an IoT system to monitor and display real-time meteorological parameters, with data stored in Google Sheets for further analysis, namely, air temperature, relative humidity, atmospheric pressure, air quality, and precipitation. Also noteworthy is the work of Behera et al. [25], who used an IoT system to monitor soil parameters and detect animals near the land. It uses sensors to monitor air temperature and relative humidity, soil water content, and cameras to detect objects using a YOLOv3-based algorithm. The data were stored in Google Sheets for further analysis.

Despite IoT’s potential to positively transform agriculture, its widespread adoption is limited by the resilience of some communities of farmers to change their traditional practices, especially among small/medium-scale holders. Underlying these challenges are several factors, such as the limited access to education affecting this professional class [26], the lack of digital literacy hampering the adoption of technology for enhancing agricultural management and productivity [27], and the still low dissemination of farmer-oriented information regarding IoT and its benefits [28].

To overcome these barriers, practical approaches apply user-centered designs and distributed computing architectures that simplify IoT implementation [29]. Additionally, educational initiatives such as distance-learning courses can empower farmers to use IoT technology effectively [26], opening up various possibilities for exploration, such as remote monitoring, predictive analytics, and weather forecasting, among other tools that add significant value to modern agriculture [30].

Tailored to agriculture-related professionals, this paper aims to address challenges that, together or individually, intend to explore perspectives requiring reflection, particularly concerning low-cost data management and refined visualization for crop-specific, time-effective, and informed decision making. More precisely, the mentioned challenges are listed as follows:

• Propose an accessible, flexible, and replicable smart irrigation IoT solution based on open-source, low-cost technologies, designed primarily for—but not limited to—farmers with little or no technological expertise and supported by extensive, tailored documentation to foster inclusivity in the digital era.
• Facilitate technology adoption in agriculture by promoting the use of widely known and accessible tools such as Google Sheets for data management, visualization, and interpretation.
• Demonstrate, in a real production context, that it is possible to reduce both the frequency and duration of irrigation events based on sensor data while maintaining adequate water comfort for the plants.
• Complement agronomic knowledge with simple, field-adapted technological tools, promoting a practical and informed approach to irrigation management.
• Promote transversal smart-irrigation practices through continuous sensor-based monitoring in real production environments, considering as a case study a professional Actinidia deliciosa orchard.

Considering the challenges and opportunities previously highlighted, this work proposes a low-cost smart-irrigation system based on IOT for Agriculture—hereinafter referred as IRRIOTA—, designed under a do-it-yourself (DIY) implementation model and extensively documented to support replication, regardless of the user’s technological background. IRRIOTA relies entirely on open-source technologies for both data acquisition and transmission. An edge end with processing and connectivity capabilities is programmed to perform periodic readings from connected sensors and send the data to Google Sheets in real time. Once stored, spreadsheet formulas and pivot tables allow for the processing of information from an agronomic perspective. Therefore, affordable IoT sensors are combined with the free and widely accessible Google Sheets platform, which, through built-in application programming interfaces (APIs), enables seamless data handling, visualization, analysis, and decision support. Google Sheets was chosen for its intuitive interface and the avoidance of custom app development, which would otherwise require specific training for farmers. Its global familiarity, extensive documentation, and strong community support help democratize access to digital agriculture tools.

As collaboration between academia and companies is essential for validating and improving prototypes under actual usage conditions, implementing and testing the proposed IRRIOTA system was carried out in close collaboration with a kiwi production company rather than relying solely on indicators extracted from typical research settings with controlled conditions. Therefore, the prototype was adapted to the company’s specific irrigation needs and operational dynamics, requiring minimal adjustments by the technology user. Notwithstanding, the proposed solution, including its physical setup, is designed to be easily replicated in other agricultural contexts.

Considering the professional environment targeted for testing IRRIOTA, the next section provides a relevant and comprehensive contextualization of Actinidia.

1.2. Actinidia chinensis: Origins, Adaptation to Mediterranean Territory, and Climate-Driven Irrigation Needs

The Actinidia chinensis plant, from which the deliciosa variety derives, originates in the region between central and southeastern China, probably near the Yangtze River—an area characterized by a subtropical climate with high summer temperatures, relative humidity, and precipitation [31]. Conversely, the Mediterranean region does not have the natural conditions for the regular development of Actinidia due to its high light intensity, low precipitation during spring and summer—which coincides with the plant’s peak development stage—and relatively high vapor pressure deficits [32]. Figure 1, which presents a plot obtained from the WeatherSpark© web system [33,34], clearly illustrates the differences in weather conditions between two climate-representative sites over an entire year. It is evident that during the plant’s most active growth phase, the climatic conditions in its region of origin are sufficient to meet its water requirements, whereas the Mediterranean conditions are not.

The physiological adaptations to the external environment of its native habitat include a densely developed canopy with large leaves, resulting in significant water loss through transpiration [35]. Additionally, Actinidia’s large xylem vessels in the stem enable high hydraulic conductivity [32]. Its high root density—characterized by robust primary roots and numerous branched secondary roots that predominantly spread within the top 60 cm of soil, allowing exploration of large underground areas [36,37]—makes this plant a species with substantial water requirements.

Despite the environmental differences, the Mediterranean’s winter provides around 700–800 chilling hours (below 7 °C), which the plant harnesses during the dormancy period. The remaining limitations are alleviated by seeding the plant in clayey and well-drained soils and performing proper irrigation to ensure healthy levels of water content [38].

Ideal temperatures for the development of Actinidia range between 14 °C and 25 °C. However, if the relative humidity of the air or the soil water content is satisfactory, higher temperatures do not become a limiting factor for crop development [39]. Conversely, relative humidity under 30–40% leads to higher transpiration rates, hampering the plant’s compensation mechanisms—more specifically, the absorption of water by the roots—for dealing with associated dehydration [40].

When Actinidia is exposed to water stress conditions—typical in the Mediterranean summer—stomatal resistance increases to reduce the transpiration rate, reaching up to 50%, and the consequent water loss [38]. As a result, there is a decrease in the photosynthetic productivity due to the reduction of both gas exchange and chlorophyll content resulting from oxidative stress, which, in addition to the inherent deficiencies in chlorophyll synthesis, can also induce changes in thylakoid membrane structure [41].

In Portugal, Actinidia requires approximately 6000 m³ of water per hectare to produce around 25,000 kg of fruit, and the most commonly used irrigation method for Actinidia cultivars is micro-sprinkler irrigation [39,42]. However, this amount varies depending on soil water content and atmospheric evaporative demand throughout the year [39]. For instance, higher temperatures increase evaporative demand, causing plants to consume more water while leading to more significant water loss from the soil due to regular root activities.

Considering the irrigation management of Actinidia within the Portuguese climatic context, this study presents an approach to leveraging low-cost IoT solutions combined with user-friendly online technologies for spreadsheet-based management, which may be more accessible and familiar to farming managers. Essentially, considering the set of IoT readings rendered to a Google Sheets instance, as well as a minimum reference value under which the plant ceases to be in water comfort and begins to experience difficulties in extracting water from the soil [39], a simple, yet solid, decision support for carrying out adequate irrigation procedures can be provided. In the case of Actinidia crops, the water potential values to consider for proper irrigation management are described in

Table 1. Soil water potential guidelines.

Soil Water Potential (Cbar or Kpa)	Description
0	The soil is fully saturated with water. These conditions should be tackled, as they cause root asphyxiation.
[0–10]	The soil has excess water. If the issue persists, irrigation management and/or soil drainage should be reviewed.
[10–20]	Optimal moisture values for plant development. Irrigation should start from 15–20 cbar in soils with low water retention capacity.
[20–40]	Ideal to trigger irrigation in sandy loam to sandy soils and in loamy to clayey soils during periods of high evapotranspiration.
>40	Ideal value to trigger irrigation in fine-textured (loamy to clay) soils under normal evaporative demand conditions.

.

The remainder of this work is organized as follows: Section 2 introduces the IRRIOTA system, followed by an implementation proposal and comprehensive guidelines for its replication by professionals with or without a technological background, which are addressed in Section 3; Section 4 details tests conducted on IRRIOTA in a professional Actinidia orchard, along with a concise discussion of the results; finally, Section 5 presents the main conclusions, along with a few drawbacks associated with the proposed IoT solution and outlines directions for future work.

2. IRRIOTA Proposal Overview: A Low-Cost IoT and Data Management Solution for Smart Irrigation

This section presents an overview of IRRIOTA’s architecture and specification, aiming to propose a flexible and reproducible setup. It also provides guidelines for edge-level operation focused on environmental sensing and for integration with a community-accessible, cloud-based service to enable cost-effective data management and analytics.

2.1. Main Architecture

The architecture of the IRRIOTA system, depicted in Figure 2, integrates various agroclimatic sensors connected to a central data collection station (CDCS), configuring a sensor network. In terms of functionality, the data collected by the sensors are grouped by the CDCS, which forwards the data to an online cloud-based platform through a local Wi-Fi router with internet connection. Therefore, users can remotely access and monitor their stored field-related data by resorting to the cloud-based platform services.

It should be noted that all components of the conceptual architecture—sensors, CDCS, communication devices, cloud platform, etc.—were carefully selected not only to ensure full operability but also to align with affordable and accessible tools made available by the market or service providers, enabling professionals to implement the system in practice. This is further addressed in a subsequent section of this work, which provides both the methodological guidelines for identifying and selecting low-cost devices and the technical details for assembling and implementing a fully formed IRRIOTA system, including its physical and logical components, in integration with a specific cloud service.

2.2. Edge Node Behavior Implementation and Cloud-Based Services Integration

At the edge node, the CDCS controller runs custom firmware to collect environmental and irrigation-related sensor data and transmit them to a cloud service. In turn, the same cloud platform is used to receive, store, and process such data, providing insightful irrigation-related decision support through visualization dashboards.

2.2.1. CDCS Implementation

The primary operations were designed to support the core logic of the CDCS. These include collecting data from sensors—corresponding to air temperature, relative humidity, pluviometry, soil matric potential, and soil water content readings—every 30 min and transmitting them to a cloud-based storage service. Figure 3 illustrates the general structure of the program as a flowchart. It should be noted that soil water content is calculated by linear interpolation of the average values acquired from the gap between the sampling port probes, which are retained by capacitive sensors.

2.2.2. Integration with Cloud-Based Services

The cloud automates the storage of new CDCS-based entries—corresponding to sets of readings regarding air temperature, relative humidity, pluviometry, soil matric potential, and soil water content—, each containing date and time values based on a Universal Coordinated Time (UTC) server. Figure 4 illustrates the overall process for receiving new agroclimatic data on the cloud side.

By receiving sensor readings every 30 min, 48 readings can be collected daily for the entire sensor set, resulting in around 1440 observations per sensor over a month. To aid in the interpretation of the data, tables with filters can be implemented. Specifically, the user can consult data within time frames covering the last seven days, the last two days, the previous day relative to the current date, and the current day itself. By incorporating simple calculus with the integrated data, such as averages and minimum and maximum values, it is also possible to set up dashboards for specific data visualization. At the cloud end, IRRIOTA’s dashboards were planned to address the following key elements:

• Daily summary: A structured table presenting the latest sensor readings, which are organized by parameter and include summary statistics.
• Weekly historical trends: Line charts illustrating the temporal variation in soil moisture content across different depths.
• Tension vs. soil moisture: A dual-axis chart combining soil water tension (centibars) and volumetric water content (percentage), enabling a comparative analysis.
• Alert system: Conditional formatting to highlight critical thresholds, such as tension values exceeding around 40 cb, supporting rapid response to field conditions.
• Precipitation monitoring: A bar chart presenting daily cumulative rainfall data.

Both tabular and dashboard views can be provided either resorting to custom web-based implementation or through the support of resources directly embedded in the cloud-service, depending on the chosen provider.

The selection of IRRIOTA’s physical components and respective implementation followed a systematic process that will be shared in the following section under the form of technical guidelines for replicating the proposed IoT system or a similar one.

3. IRRIOTA from Edge to Cloud: System Implementation, Hardware Selection, Deployment Guidelines, and Integration

To facilitate the replication of an IRRIOTA-based system and support its broader application in agricultural contexts, this section begins by proposing a set of cost-effective core components for building an agro-monitoring setup (edge node), along with the main steps for its assembly. It should be noted that rather than designing and building new physical components, market-available parts were selected and integrated. With this consideration in mind, a market survey is also presented, based on online platforms that commercialize hardware, from which off-the-shelf components can be found and acquired. Moreover, coding details and configuration procedures are addressed, covering both the programming of the agro-monitoring setup and the integration of a specific cloud service (Google). Moreover, key aspects related to cybersecurity are encompassed.

3.1. Edge Node Hardware Proposal

For the deployment of an IRRIOTA-based solution, an ESP32-S2-SAOLA-1M development board was proposed as the CDCS component, serving as the central hub for integrating various peripheral sensors with different environmental measurement capabilities into a unified sensor network. The selected sensors—used to measure air temperature, relative humidity, soil temperature, soil water content, soil matric potential, and soil water input—are listed in

Table 2. Sensor information, respective protocols, applications, and accuracy.

Model/Brand	Monitoring Application	Protocol	Accuracy
DHT11 (Aosong (Guangzhou) Electronics Co., Ltd., Guangzhou, China)	AT and RH 1	Digital 1-wire	AT ± 2.0 °C; RH ± 5.0% [43]
DS18B20 (Shenzhen RPD Sensor Technology Co., Ltd., Shenzhen, China)	Soil temperature	Digital 1-wire	±0.5 °C [44]
SongHe B07SYBSHGX (Songhe, Shenzhen, China)	Soil water content	Simple analog reading	89% [45] 2
Watermark 200SS (Irrometer Company, Inc., Riverside, CA, USA)	Soil matric potential	Simple analog reading	94% [46] 2
WH-SP-RG Rain Gauge (Shenzhen Omena Technology Co., Ltd., Shenzhen, China)	Soil water input	Simple digital reading	90% [47] 2

¹ AT and RH stand for air temperature and relative humidity, respectively. ² For sensors whose accuracy/error details were unavailable in manufacturers’ data sheets, insights were obtained from the literature, using percentages of correlation with real values.

, along with their respective protocol and accuracy specifications.

Figure 5 shows the physical prototype ready for field deployment, which was developed based on the architecture and hardware previously proposed. With respect to the cost of this configuration, approximately EUR 232.00 were invested [48]. Later in this section, a market survey is presented for replicating a similarly priced setup.

3.2. Edge Node Components and Configurations

While the proposed setup is intended for use by both professionals with expertise in electronic or electrical systems and those without—such as agronomists or small/medium-scale farmers—particular attention was given to developing clear, appropriately tailored instructions for both group. To maximize stakeholders profile coverage during the setup and assembly stage, two complementary visual guides were created, focusing on facilitating the successful construction of a functional prototype.

Figure 6 presents the electrical schematic of the circuit, where the connections between components are depicted using a standardized technical format. This type of representation is essential for ensuring design consistency and is intended for technically proficient stakeholders.

On the other hand, recognizing that interpreting electrical schematics may be challenging for stakeholders with limited technical background in electronics or electrical systems, a second, more intuitive and practical visual representation was developed, as shown in Figure 7. While based on the original electrical schematic, this version presents the connections in a clearer and more accessible manner—for example, by depicting the relative physical positions of components and using color-coded wiring paths—to support hands-on replication. These visual instructions are designed to more closely resemble the final real-world setup, thereby facilitating comprehension even for users without prior experience in electronics.

The electronic architecture of the system was designed to ensure modularity, low cost, and operational robustness under agricultural field conditions. The entire assembly was implemented on a breadboard, using low-gauge, highly flexible silicone wires to interconnect the various components. This approach enables rapid, safe, and full functional prototyping, while allowing the feasibility of maintenance and future modifications, without the need to undergo through the development of a printed circuit board (PCB).

3.2.1. CDCS Hardware

The central component of the system—conceptually defined as the CDCS in the previous section—consists of a commercially available cost-effective ESP32-S2-SAOLA-1M development board (hereafter simply referred as ESP32), which is responsible for acquiring sensor data, processing them, and transmitting them to the cloud. The sensors are connected to the ESP32 via WAGO connectors using shielded four-conductor cables. The shielding helps minimize electromagnetic interference and ensures signal integrity.

3.2.2. Digital Sensors and Pull-Up Resistors

As previously highlighted, the digital sensors proposed to be used with the system include a DHT11 (air temperature and relative humidity), a DS18B20 (soil temperature), and a tipping-bucket-type rain gauge based on a mechanical switch. These sensors are directly connected to the digital input pins of the ESP32.

Each of the three sensors was fitted with its own 10 kΩ pull-up resistor, which maintains a stable logic level when the sensors are not actively transmitting. In the case of the tipping-bucket rain gauge—where each tilt of the bucket momentarily closes a switch to indicate a fixed amount of rainfall—the pull-up resistor ensures accurate detection of the resulting electrical pulses.

3.2.3. Analog Sensors and Multiplexing

The system includes four capacitive soil moisture sensors and a single 200SS Watermark, as previously declared. The capacitive sensors, which output a variable analog voltage proportional to soil moisture, are connected directly to the analog inputs (ADC) of the ESP32. On the other hand, the Watermark sensor, which is inherently resistive, resorts to an analog multiplexer (74HC4051) to avoid continuous polarization of the electrodes. During each reading cycle, the multiplexer selectively enables the circuit, allowing a voltage divider composed of the sensor and a 7.87 kΩ resistor to be briefly powered. The voltage at the midpoint of the divider reflects the soil water potential. In complement, a 3.3 V Zener diode was also added to protect the analog input from possible overvoltage.

3.2.4. Protection and Encapsulation

To ensure long-term functionality and data integrity in harsh agricultural environments—particularly in outdoor or partially buried deployments—several protection and encapsulation strategies were implemented, aiming to address environmental challenges such as high humidity, dust intrusion, water exposure, and corrosion, which are common in irrigated field systems.

All sensor connections were soldered to shielded four-conductor cables, replacing the original 15–20 cm wires with adjustable-length versions, thus improving installation flexibility. The number of conductors used varied according to each sensor’s specifications: For instance, the rain gauge and Watermark sensors require only two conductors, while the remaining typically use three. Shielded cabling not only provided this flexibility but also helped reduce electromagnetic interference and offered an additional physical barrier against moisture ingress.

Due to the sensors’ high exposure to potentially harmful conditions, further protective measures were taken to ensure system reliability. All solder joints were sealed with a waterproof silicone-based coating to prevent oxidation and corrosion from condensation or direct water contact. Heat-shrink tubing was applied to exposed terminals and cable connections, providing tight, durable insulation that resists both dust and moisture infiltration.

Additionally, all circuits were housed in IP65-rated junction boxes. These enclosures are dust-proof and protective against low-pressure water jets from any direction, making them particularly suitable for field installation in areas exposed to irrigation, precipitation, or spraying.

It is important to note that the affordability of many low-cost sensors on the market is partly due to the lack of integrated protective features. Therefore, applying external encapsulation becomes essential—not only to extend operational life but also to ensure measurement consistency over time. These protective strategies are especially relevant in small/medium-scale farming contexts, where long-term durability must be achieved without significantly increasing system costs.

Nonetheless, while these protective measures enhance system resilience, they introduce certain installation/maintenance intricacies that are noteworthy. For example, silicone coatings and heat-shrink tubing can make technical interventions more difficult—e.g., the replacement of a component. Similarly, the effectiveness of IP65-rated enclosures relies on proper sealing using cable glands, which may increase installation complexity for non-expert users. Even so, the combination of these solutions seems to offer a practical balance between cost-effectiveness, ease of assembly, and the physical robustness required for reliable field operation.

3.2.5. Field Installation Considerations

Regarding the installation preparatory procedures involving the previously highlighted hardware selected for the IRRIOTA-based edge node, DHT11, DS18B20, and WH-SP-RG sensors do not require calibration. In contrast, the SongHe B07SYBSHGX must be calibrated using, for example, the methodology described in [49], which consists of collecting and examining soil samples from around the production area where the prototype will be installed. Moreover, the calibration of the Watermark sensor is also recommended, resorting, for example, to the guidelines outlined in [49], which involve replacing the sensor with several known resistors of different values, starting with 220 Ω and ending with 8200 Ω, and comparing respective outputs with the values observed in the ESP32. The electrical resistance of the soil must then be converted into matric potential using the conversion formulas provided by the manufacturer of the Watermark sensor [50].

3.2.6. Components’ Market Landscape

Prior to inspecting the market towards the substantiation of the proposed solution, it is important to clearly identify all components involved in the construction of the system, as well as their respective roles and relationships for an integration compliant with the IRRIOTA architecture. To that end,

Table 3. Functional classification and data flow of components used in an IRRIOTA-compliant prototype. For each Component entry, Input Source indicates the power or signal issuer, while Output identifies the receiver of the produced data, signal, or physical connection.

Component	Category	Generic Purpose	Input Source	Output Target
DHT11 Sensor Module	Sensor	Temperature and Humidity Sensor (Digital)	Shielded Cable with 4 Conductors by ESP32	ESP32 by Shielded Cable with 4 Conductors
DS18B20 Sensor	Sensor	Soil Temperature Sensor (Digital)	Shielded Cable with 4 Conductors by ESP32	ESP32 by Shielded Cable with 4 Conductors
SongHe Moisture Capacitive Sensor V2.0	Sensor	Soil Water Moisture Sensor (Analog)	Shielded Cable with 4 Conductors by ESP32	ESP32 by Shielded Cable with 4 Conductors
Watermark 200SS Sensor	Sensor	Soil Water Tension Sensor (Resistive)	Shielded Cable with 4 Conductors by Multiplexer and ESP32	ESP32 by Shielded Cable with 4 Conductors
WH-SP-RG Rain Gauge Sensor	Sensor	Rainfall Sensor (Pulse/Bucket)	Shielded Cable with 4 Conductors by ESP32	ESP32 by Shielded Cable with 4 Conductors
8 Channel Multiplexer (74HC4051)	Interface (MUX)	Analog Signal Router (MUX)	ESP32	—
Shielded Cable with 4 Conductors	Cable	Power and Signal Cabling	All Sensors	Compact Wire Connectors (WAGO Type) Connected to ESP32
Soft Silicone Cable Kit (0.8 mm)	Cable	Flexible Prototyping Cable	Internal Wiring (ESP32, MUX, Resistors, etc.)	—
7.87 k Ohm Resistor (1%)	Passive Component	Pull-Up for Watermark Sensor	Watermark Circuit	—
3.3 V Zener Diode	Passive Component	Voltage Protection	Watermark Circuit	—
10 k Ohm Resistor (1%)	Passive Component	Pull-Up Resistors (Data Lines)	DHT11, DS18B20, Rain Gauge	—
ESP32-S2-SAOLA-1M Dev Board (Chosen)	Microcontroller	MCU with Wi-Fi	Wi-Fi → Cloud/Server	All Sensors
ESP32-S2-DevKitM-1-N4R2 Dev Board (Alternative)	Microcontroller	MCU with Wi-Fi	Wi-Fi → Cloud/Server	All Sensors
Heat Shrink Tube Kit (2–19.1 mm)	Protection	Insulation and Protection	Sensor and Cable Connections	—
Heat Shrink Tube (25 mm)	Protection	Cable Insulation (Larger Gauge)	Sensor and Cable Connections	—
Waterproof Coating	Protection	Weatherproofing Compound	Sensor Solder Joints	—
Protective Shield for Temp/RH Sensor	Protection	Protection from Sun and Rain	DHT11	—
Waterproof Junction Box	Enclosure	Enclosure for Electronics	Houses All Electronics	—
Compact Wire Connectors Kit (WAGO-type)	Connection	Tool-Free Electrical Connections	All Sensors + Shielded Cable with 4 Conductors	—
PVC Pipes (3/4 Inch)	Support	Physical Support	Sensor Installation Structure	—

presents a relevant set of hardware components used to set up an operational prototype, as well as the respective dependencies (input/output) between such components. These dependencies encompass both active components (e.g., sensors and microcontrollers) and passive or structural elements (e.g., resistors, cables, and protective enclosures).

Considering the specified equipment for achieving an IRRIOTA-like system and the intended audience—agronomists, tech-enthusiast farmers, among other similar profiles—, a hardware market survey was conducted in May 2025 across major global e-commerce platforms to identify suitable components, namely, Amazon, Temu, eBay, and AliExpress. Selection prioritized the best ratio between the number of positive feedback reviews and the total number of reviews of the buyers, as a per component indicator of both customer satisfaction and vendor reliability. Price data, collected between 13 and 19 March 2025, was converted to euros (EUR) using the exchange rate of the final day, excluding shipping costs, customs duties, and other variable charges.

Table 4. Pricing per unitary component and minimum units for shipping (MUS) across Amazon, Temu, eBay, and AliExpress platforms. Survey conducted in May 2025.

Material	Qty	Amazon		Temu		eBay		AliExpress
		Price (€)/Unit	MUS	Price (€)/Unit	MUS	Price (€)/Unit	MUS	Price (€)/Unit	MUS
DHT11 Sensor Module	1	1.1 a	10	1.42 b	1	0.87 c	1	0.96 b	1
DS18B20 Sensor	1	1.84 a	5	1.55 b	5	1.92 a	1	0.93 b	1
SongHe Moisture Capacitive Sensor V2.0	4	1.84 a	5	1.05 b	5	1.2 b	5	0.69 b	5
Watermark 200SS Sensor	1	40.95 a	1	NF	NF	45.5 b	1	NF	NF
WH-SP-RG Rain Gauge Sensor	1	23.92 a	1	NF	NF	50.61 b	1	28.68 b	1
8 Channel Multiplexer (74HC4051)	2	0.74 a	10	NF	NF	2.21 a	5	0.28 b	10
Shielded Cable w/4 Conductors	35 m	0.91 a	45.72	0.89 b	45.72	1.6 b	50	2.83 b	40
Soft Silicone Cable Kit (0.8 mm)	2 m	8.26 a	1 f	6.86 b	1 f	12.69 a	1 f	4.19 b	1 f
7.87K Ohm Resistor (1%)	1	0.06 a	100	11.87 b	1 f,g	0.57 a	5	0.03 b	100
3.3V Zener Diode	2	7.35 a	1 f	3.38 b	1 f	0.09 a	10	0.02 b	100
10K Ohm Resistor (1%)	3	0.06 a	100	11.87 b	1  f,g	0.08 d	20	0.02 b	100
ESP32-S2-SAOLA-1M Dev Board (chosen)	1	15.64 a	1	NF	NF	NF	NF	12.26 b	1
ESP32-S2-DevKitM-1-N4R2 Dev Board (valid alt.)	1	7.36 a	1	NF	NF	NF	NF	9.48 b	1
Heat Shrink Tube Kit (2–19.1 mm)	1	9.19 a	1	4.51 b	1	30.36 a	1	2.12 b	1
Heat Shrink Tube 25 mm	1	7.81 a	1	2.29 b	1	6.96 a	1	4.55 b	1
Waterproof Coating	1	13.8 a	1	2.02 b	1	6.43 e	1	18.92 b	1
Protective Shield for Temp/RH Sensor	1	15.64 a	1	NF	NF	18.13 e	1	22.49 b	1
Waterproof Junction Box	1	46.01 a	1	25.47 b	1	65.33 a	1	46.18 b	1
Compact Wire Connectors Kit (WAGO type) h	1	22.96 a	1	13.4 b	1	14.25 a	1	23.73 b	1
PVC Pipes (3/4 inch)	0.5 m	7.32 a	1	NF	NF	12.87 a	1	8.38 b	1

The following exchange rates to the Euro (EUR) currency were determined on 19 May 2025, with minor fluctuations assumed: ^a USD-EUR: 1 USD = 0.92 EUR; ^b EUR (€); ^c CAD-EUR: 1 CAD = 0.64 EUR; ^d AUD-EUR: 1 AUD = 0.5818 EUR; ^e GBP-EUR: 1 GBP = 1.1929 EUR. ^f 1 Kit (includes the required component among other parts and/or in greater quantities). ^g These components are included in the same assorted kit. Purchasing a single kit is sufficient to meet the requirements of an IRRIOTA-driven setup. ^h Kit verified to include at least 9 connectors of 2 conductors and 4 connectors of 5 conductors. NF: Information not found on the consulted website.

summarizes the results of such survey.

Local Wi-Fi setups were excluded from the survey, as connectivity hardware depends on each stakeholder’s existing infrastructure. Possible options include 4G access points, standard Wi-Fi routers, or Wi-Fi repeaters to extend coverage. Connectivity costs vary with the internet service provider (ISP) and the equipment required to ensure reliable network coverage for IRRIOTA-based setups.

An additional filtering of the preliminary survey was also performed, resulting in the proposed purchase pack shown in

Table 5. Preferred components based on cost-effectiveness and top-ranked selling store, indicating minimum purchasable and recommended quantities.

Component	Minimum Purchasable Quantity	Recommended Quantity	Unitary Price (EUR)	Lower Price Provider
DHT11 Sensor Module	1	1	0.87	eBay
DS18B20 Sensor	1	1	0.93	AliExpress
SongHe Moisture Capacitive Sensor V2.0	4	5	3.45	AliExpress
Watermark 200SS Sensor	1	1	40.95	Amazon
WH-SP-RG Rain Gauge Sensor	1	1	23.92	Amazon
8 Channel Multiplexer (74HC4051)	2	10	0.56	AliExpress
Shielded Cable w/4 Conductors	35 (m)	45.72 (m)	31.15	Temu
Soft Silicone Cable Kit (0.8 mm)	2 (m)	1 (m)	4.19	AliExpress
7.87K Ohm Resistor (1%)	1	100	0.03	AliExpress
3.3V Zener Diode	2	100	0.04	AliExpress
10K Ohm Resistor (1%)	3	100	0.06	AliExpress
ESP32-S2-SAOLA-1M Board (chosen)	1	1	12.26	AliExpress
ESP32-S2-DevKitM-1-N4R2 Board (alternative)	1	1	7.36	Amazon
Heat Shrink Tube Kit (2–19.1 mm)	1	1	2.12	AliExpress
Heat Shrink Tube 25 mm	1	1	2.29	Temu
Waterproof Coating	1	1	2.02	Temu
Protective Shield for Temp/RH Sensor	1	1	15.64	Amazon
Waterproof Junction Box	1	1	25.47	Temu
Compact Wire Connectors Kit (WAGO type)	1	1	13.4	Temu
PVC Pipes (3/4 inch)	0.5 (m)	1 (m)	8.38	AliExpress
		Total Cost	EUR   206.07

. This pack contains the minimum set of components, a recommended surplus for spare parts, and the most cost-effective option identified for each device class.

While the presented options serve as a practical reference for consciously identifying components to assemble an IRRIOTA-based edge node, each stakeholder must adapt the hardware and supplier selection to their specific context (e.g., budget constraints, geographical location) and purchasing conditions (e.g., stock availability, shipping methods, and overall purchase volume—as larger orders may sometimes qualify for free shipping and other benefits—, among others). Moreover, in a dynamic market environment, prices should be monitored over time to track fluctuations and make purchasing decisions with full awareness of these changes.

3.3. Edge Node Code Implementation

The Arduino Integrated Development Environment (IDE) provides a graphical user interface (GUI) that facilitates programming by allowing users to easily import device-specific libraries and organize their coding workflow. In the context of the selected ESP32 microcontroller, once the appropriate library is imported into the Arduino IDE, the C/C++ programming interface can be used to implement the firmware for an IRRIOTA-inspired edge node. In line with that implementation, two core functions operating with a periodicity of 30 min are required to model the node’s behavior: (i) one for collecting data from all sensors and (ii) another for transmitting such data to a cloud-based storage platform. More specifically, the ESP32 microcontroller collects and transmits sensor data to Google’s cloud-based platform via a Hypertext Transfer Protocol (HTTP) endpoint using a GET request. The source code developed for this IRRIOTA-based node is publicly available on GitHub (version 1.0) [51], along with comprehensive instructions for integration with the Google cloud-based platform.

3.4. Cloud Integration for Managing Data and Providing Visualization and Decision Support

The data acquired by the edge node (ESP32) are transmitted every 30 min to Google Cloud via a Representational State Transfer (REST) API, resulting in up to 48 sensor readings per day or approximately 1440 observations per sensor each month. On the cloud side, the API processes the incoming requests by extracting the relevant parameters and automatically stores the data in a Google Sheets instance, which is dynamically updated as new entries arrive.

This cloud-based approach offers several advantages. First, it leverages a widely accessible and familiar platform—Google Sheets—eliminating the need for complex server configurations or advanced self-managed cloud infrastructure. The result is a cost-effective, lightweight, and low-code solution for real-time data storage and management. Moreover, the reduced reliance on advanced programming skills makes the platform particularly suitable for non-technical professionals interested in developing their own context-aware agricultural applications. Because the integration of additional and more diverse sensors can be easily managed through simple adjustments to the HTTP-based requests, the solution demonstrates both flexibility and scalability enabled by Google Cloud services.

Beyond storage, a number of Google tools also provide a powerful tool for data visualization and interpretation, namely, through Google Sheets. Features such as daily summaries, weekly historical trends, sensor comparisons, and alert mechanisms are available and can be presented through—but are not confined to—tabular views or dynamic dashboards. These visualization tools can be accessed remotely and in real time, aligning with the cloud integration goals outlined in the previous section.

To further support analysis, a filtering strategy was implemented using native Google Sheets functionalities, including multiple pivot tables that aid in summarizing sensors’ data reported from recent periods. Users can quickly consult such data for the current day, the previous day, the last two days, or the last seven days. With simple processing functions—such as calculating averages, as well as minimum and maximum values—Google Sheets enables the creation of real-time dashboards featuring interactive charts and tables, ultimately providing actionable insights to support data-driven irrigation decisions in agricultural environments. Aligned with these features, the following resources are available:

• Visual indicators:

  -

  Climate conditions and crop growth: A color-based tabular scheme was implemented to showcase crop growth tendency, allowing automatic adjustment based on temperature and relative humidity indicators, with visual highlights for representing the most and the least favorable conditions.

  -

  Approximation to optimal irrigation: An indicator chart that was developed to display the current matric potential of the soil and notify the proper time frame for irrigation.

  -

  Data summary: Refers to functionalities that enable the consultation of parameter-specific charts that organize data based on temperature, relative humidity, soil water input, and soil moisture levels.

• Summary tables: Matrix-based elements displaying results based on data collected from the current and previous days for quick and up-to-date analysis.
• Warning for extreme events: Cell-based highlights designed to draw attention to outstanding climatic conditions.

3.5. End-to-End Cybersecurity Considerations

In the context of cybersecurity, due to the adoption of Google as a third-party platform, data privacy and ownership are inherently managed by the service provider.

In compliance with the proposed architecture, the edge side of IRRIOTA must be configured to operate through a Google cloud service account with restricted permissions, which ensures that only an authorized device—associated with a valid owner’s account—can access or modify the information stored at the cloud side. Authentication keys are stored securely on the device and never shared publicly. Furthermore, procedures were implemented at the edge device side to ensure data redundancy upon cloud service failure events by storing an array of agroclimatic parameters locally, which are automatically transmitted once service restoration is detected. Regarding physical intrusion susceptibility, the current edge node configuration does not yet address this aspect. However, guidelines for monitoring and preventing such potentially malicious actions may include the deployment of intelligent surveillance systems, as proposed in [52].

From the user’s perspective, IRRIOTA-related data stored in the cloud can be accessed through most popular web browsers (Google Cloud, Mozilla Firefox, Microsoft Edge, and so on) by authenticating with a valid account—corresponding to an edge node owner—in Google portal, which intrinsically addresses key concerns related to both data protection and authorship attribution. While the data remain the property of the Google registered agricultural producer, hosting them on Google servers requires compliance with the provider’s privacy policies. In contexts where higher confidentiality is required, private or hybrid storage solutions could be considered to increase direct control over the collected agricultural information, although their affordability may vary depending on pricing plans and stakeholders’ resources.

A final remark concerns data confidentiality, integrity, and authenticity, which are transversely ensured across Google’s services—whether in communications between the user and Google’s frontend tools or between the device and the Google API—through the use of HTTP over Transport Layer Security (TLS), providing end-to-end security via encryption mechanisms.

In the following section, tests made to an IRRIOTA-inspired replica conducted within professional Actinidia orchard settings are presented.

4. Assessing IRRIOTA Under Operation in a Commercial Actinidia Orchard

The tests made to the irrigation management solution proposed in this paper—IRRIOTA—involve monitoring commercial Actinidia cultivars. Therefore, this section not only characterizes the market-oriented Kiwi producer that hosted this solution but also documents the irrigation procedures performed in the field to keep Actinidia crops hydrated and healthy.

4.1. Kiwicoop’s Commercial Actinidia Orchard Contextualization

The proposed solution to optimize water use during irrigation, materialized in an IRRIOTA-compliant prototype, has been tested in the orchard of Kiwicoop—Cooperativa Frutícola Da Bairrada, Crl—located in Oliveira do Bairro, Aveiro, Portugal (Figure 8a). With an approximate area of 2.4 ha, this orchard was planted in October 2020 to grow yellow kiwifruit of the Dori variety. In 2021, irrigation was empirically performed by providing 20 min of water per day to the plants, as is traditionally done before the installation of the proposed IoT-based solution.

Throughout the entire campaign assessing the proposed irrigation solution, two groups of Kiwicoop professionals were collaboratively involved:

• Technicians: Employees with management expertise on Actinidia to whom the solution was transferred, enabling data-driven decision making oriented to irrigation procedures.
• Farmers: Employees directly involved in field operations, instructed and guided by the technicians to adjust irrigation procedures.

4.2. Irrigation Monitoring and Procedures Following IRRIOTA Prototype Installation

An IRRIOTA prototype was deployed and tested in a specific sector of Kiwicoop’s Actinidia orchard (Figure 8b), with the goal of assisting on-site technicians in optimizing manually controlled irrigation through the use of sensor data to guide micro-sprinkler operation (Figure 8c).

Two irrigation strategies were maintained over the tests with IRRIOTA: (i) traditional (20 min of water supply per day) and (ii) IoT managed. To ensure uniform water distribution in the monitored sector while maintaining a reliable correspondence with the IRRIOTA agroclimatic data, irrigation cycles in the non-monitored sectors were properly coordinated, thereby mitigating flow rate fluctuations in the main supply pipeline.

The integration of IoT sensor data with Google Sheets streamlined the decision-making process regarding (manual) irrigation management by enabling centralized, real-time access to essential agroclimatic parameters such as soil water content (at multiple depths), air temperature, and accumulated rainfall. Data were automatically collected by IRRIOTA’s ESP32 and transmitted every 30 min to a Google Sheets instance configured for cloud-based storage. Over that spreadsheet data, informative dashboards were configured to highlight trends and critical values, helping technicians visually interpret soil moisture dynamics and correlate them with irrigation events. One of the main challenges in data visualization was to ensure that the information was presented in a clear and accessible manner, allowing intuitive interpretation and reducing the need for specialized training. This aspect was refined through technicians’ iterative feedback. Regarding the parameters considered for irrigation management, the following were included:

• Air temperature (°C)—measured by one sensor placed two meters height;
• Relative humidity (%)—measured by one sensor also installed at a height of two meters;
• Total water applied to the soil (mm) by irrigation or precipitation—measured by one rain gauge placed at ground level;
• Soil water content (% volume)—measured by four capacitive sensors placed along 56 cm of depth to cover the effective root zone;
• Soil water tension (cBar)—measured by a single resistive sensor buried within a profundity range of 22–29 cm and another temperature sensor placed at 25 cm depth to measure soil temperature (°C).

These parameters enabled the technicians to assess water comfort consistency for Actinidia crops and to coordinate necessary manual irrigation adjustments with the operating farmers, relying on agroclimatic data-based evidence, in contrast to the pre-implemented 20 min irrigation routines. Moreover, is situ monitoring for visual confirmation of water stress symptoms was carried out, specifically focusing on leaf wilting/rolling in the plants.

The following subsection presents the results obtained from the proposed IoT experimental setup and provides a brief discussion.

4.3. Results and Discussion

The system was implemented and deployed in the Kiwicoop orchard from 8 June to 10 September 2021. In the specific terrain sector where the proposed IoT station was installed, the irrigation procedures followed Kiwicoop’s specifications.

4.3.1. Irrigation Strategies: From Standard Procedures to Decisions Supported by the Proposed System

Initially, daily irrigation was performed for 20 min starting at 11:00 AM, and this strategy remained until 21 July 2021. Sensor data collected between 9 and 15 July 2021 are presented in Figure 9.

The highest temperatures of the week were recorded on 15 July 2021. However, the irrigation performed that day ensured sufficient water availability, preventing temperature from becoming a limiting factor for the plant’s development.

The analysis of the two soil water charts at the bottom of Figure 9 indicates no limitations in the plant’s access to water during that week. Additionally, plausible irrigation maintenance can be inferred from the chart presenting soil water data for the last two days, which is located in the lower-left section of Figure 9. This chart suggests that irrigation occurred every day on 14 and 15 July 2021, starting at 11:00 AM, with noticeable variations in soil water content. Conversely, unusual readings were observed in the pluviometer on 13 and 14 July 2021, which were likely caused by side winds.

Based on the initial data assessment, a decision was made to change the irrigation regime, more specifically, to 20 min sessions every other day (EOD). The results recorded by the proposed system between 22 July and 28 July 2021 are shown in Figure 10. During that period, temperatures were mild, with 22 July presenting the lowest temperature in the range.

The 20 min EOD irrigation strategy data analysis reveals that approximately 11 mm of water was applied during each session. The couple of soil water charts at the bottom of Figure 10 indicate suitable plant water availability throughout the test period. However, staggered irrigation patterns were observed, as the soil water indicator chart for the last two days of the trial (see the lower-left section of Figure 10) displays only one irrigation event characterized by a noticeable decrease in soil water content before irrigation and followed by a subsequent increase. Sensor 4, positioned below the effective root zone, exhibited minimal variation, suggesting no water wastage due to irrigation.

Implementing an alternate irrigation strategy demonstrated that soil water availability remained sufficient for the plants and was never a limiting factor. This finding led to insights for further water reduction, prompting the development of another irrigation strategy. The revised approach reduced irrigation duration from 20 to 15 min while maintaining the EOD schedule. This latter irrigation procedure was divided into two sessions: one of 10 min in the morning to replenish soil water content and another of 5 min in the afternoon, serving the dual purpose of increasing soil moisture and refreshing the plant’s surrounding environment. This practice was deemed particularly relevant during August, which is a period characterized by more challenging climatic conditions for plant development. The results of this adjustment are presented in Figure 11.

Data analysis from the proposed irrigation strategy adjustment to EOD cycles with 15 min of duration per day revealed that the water application ranged between 8 and 9 mm per session. From 16 August to 22, the temperatures remained mild, with 22 August showing the slightest temperature variation. The two soil water charts confirm that plants experienced no limitations in water access during the test period.

Like the previous strategy, staggered irrigation can be confirmed in the soil water indicator chart for the last couple of monitored days—21 and 22 August. The underlying pattern corresponds to an initial decrease in soil water tension from 30 to 15 cbars in the morning, followed by a second decrease in the afternoon to approximately 1 bar. Sensor 4, placed below the adequate thickness, showed slight variation, indicating no water wastage resulting from irrigation.

All addressed irrigation schedules are summarized in

Table 6. Records of weekly irrigation times before and after the IRRIOTA prototype installation.

	Daily Irrigation 20 min Daily	Custom Irrigation 20 min EOD	Custom Irrigation 15 min EOD
1st Week	140 min	80 min (4 days)	60 min (4 days)
2nd Week	140 min	60 min (3 days)	45 min (3 days)
3rd Week	140 min	80 min (4 days)	60 min (4 days)
4th Week	140 min	60 min (3 days)	45 min (3 days)
Total	560 min	280 min	210 min
Time-based   savings (%)	Reference	50%	62.50%

, which highlights considerable differences in time-based water usage.

By switching irrigation schedule from 20 min daily to 15 min EOD, water application time was reduced by 62.50%. As confirmed by technicians, none of these readjustments induced water stress in Actinidia, as verified through IRRIOTA’s remote dashboards and corroborated by the absence of visual symptoms in the plants—namely, leaf wilting/rolling.

4.3.2. System Responsiveness to Adverse Climatic Conditions

Regarding fruit production, the impacts of climate change are evident not only by the increase in average temperatures and the decrease of average precipitation reduction but also by the intensification of extreme events in both number and severity, mainly consisting of heat waves and intense rainfall [53].

Figure 12 presents images from July 2021, showing the effects of several days of unfavorable climatic conditions on the development of kiwifruit in a Hayward variety orchard near the prototype installation site.

Adverse climatic conditions during Actinidia’s growth and maturation can significantly damage leaves and fruits [54,55,56]. However, as evidenced in the dashboard shown in Figure 12, the warnings provided by the proposed system demonstrate its capability to detect such extreme events and enable the time-effective implementation of mitigation measures.

4.4. Comparative Analysis of IRRIOTA with Related Work

Compared with IRRIOTA, the approaches found in the literature that combine IoT and data management for agriculture present certain noteworthy limitations. These include the lack of detailed information to support the replicability of the respective proposals, particularly for non-technical stakeholders. Examples include incomplete descriptions of the adopted sensors (e.g., soil water content) and the absence of clear instructions for assembling and configuring the underlying systems [20]. Other common constraints are the reliance on a limited set of sensors—generally restricted to temperature, humidity, and soil water content, which can affect the robustness of decision-making support [21,22]—and the use of platforms with static or proprietary (non-open-source) functionalities that may hinder challenge-oriented adaptations [21]. Some studies also report technical issues, such as sensor corrosion and low accuracy [23], hampering operational effectiveness assessment. Others, while focusing on irrigation management, adopt crop-peripheral approaches mainly based on meteorological monitoring [24]. Furthermore, most of the surveyed works do not include validation under real production conditions, which limits the understanding of their potential impact on irrigation efficiency and water savings, especially in relevant operational environments [21,22].

The next and final section summarizes the proposed IRRIOTA solution, outlines the main remaining open issues, and covers possible avenues for future work.

5. Conclusions and Future Work

This article presented IRRIOTA, a low-cost and open-source-based solution that integrates multiple sensors to monitor critical agroclimatic parameters for continuous decision-making support, made operational through manual irrigation. The system was integrated with Google Sheets, leveraging data integration through a specialized Google API to harness the benefits of a powerful, community-accessible platform with user-friendly interfaces. For reproducibility purposes targeting technical (e.g., digital infrastructure managers and developers) and non-technical audiences (e.g., farmers), a set of comprehensive and detailed guidelines for building and configuring an IRRIOTA-inspired system were also proposed. Furthermore, the versatile reconfigurability of the IRRIOTA solution promotes scalability, encompassing not only other strategic agricultural tasks beyond irrigation management—such as pest and disease control, monitoring of phenological stages, and climate trend analysis [57]—but also economically relevant crops, including vineyards and chestnut groves. Such operational shifts must, however, entail the following:

• Identifying crop-specific optimal physiological conditions;
• Determining crop-specific requirements for sensor installation;
• Adjusting crop-related thresholds and reference values within Google Sheets dashboards to enhance decision support.

Google Sheets demonstrated great flexibility as a management tool, effectively providing interfaces with an application frontend that features various visualization styles for decision support. By enabling the implementation of interactive tabular views and dashboards with data filters, a clear and more intuitive visual representation of parameter trends was achieved, providing a better understanding of the monitored variables and overcoming the limited perspective offered by data tables.

Tests carried out with an IRRIOTA-compliant setup in a professional Actinidia orchard showed that the system’s monitoring capabilities assisted in-house technicians in adjusting their irrigation strategies, achieving time-based water savings of up to 62.50%. However, some limitations related to the chosen sensors were identified, along with the following possible solutions:

• Pluviometer readings: Accurate on days with stable wind but windy conditions affected micro-sprinkler water trajectories, reducing sensor performance. This can be mitigated by installing a flow meter in the irrigation piping, connected to the microcontroller, to precisely measure water debit.
• Capacitive sensor stability: While Watermark sensors performed reliably, capacitive soil moisture sensors produced unstable readings. This can be addressed by calibrating them with gravimetric laboratory tests using diverse soil moisture samples, as recommended in [58], and by regularly adjusting them in field conditions. Alternatively, other reliable soil moisture sensors with proven performance may be found in the market, with varying cost-effectiveness.

In addition, there is plenty of room for progresses regarding energy supply autonomy and communication means. Regarding the former, commercially available photovoltaic panel kits and related components could alternatively be used to ensure self-sufficiency in remote settings. More specifically, for an IRRIOTA-like setup, such kits should include (i) a compact 5W photovoltaic panel; (ii) a solar charge controller, either based on Pulse Width Modulation (PWM) or Maximum Power Point Tracking (MPPT); and a rechargeable battery, often lithium iron phosphate (LiFePO₄) or sealed lead acid (considering the proper handling measures for environmental protection). All these components should be housed within a weather-resistant enclosure. Such solutions target low-power consumption devices, such as ESP32-based sensor nodes, offering year-round autonomy in regions with consistent sunlight. Their modular design facilitates easy scaling and component replacement. Regarding alternatives to the current IRRIOTA connectivity settings, a 4G/5G-based mobile internet hotspot can be integrated as a viable communication platform in locations lacking Wi-Fi access. Other options, such as long-range (LoRa) networks, are also worth considering in scenarios with similar constraints.

Considering the identified issues, future work should encompass the following open challenges:

• Addressing the limitations observed with capacitive sensors to improve data reliability;
• Implementing solar panels as a sustainable energy source;
• Integrating a long-range communication based on a 4G or LoRa module, interfacing with the internet either through self-contained resources or by leveraging local connectivity gateways, depending on the specific requirements of the technology.

Finally, applying artificial intelligence-based data mining and reasoning to the information stored in Google Sheets represents a promising direction for future developments, particularly through the integration of complementary tools such as Google Colab. Such synergy would enhance data analysis capabilities and provide farmers with extended decision support.