*3.3. Water Quality Measurements for Decision Making According to Its Quality*

In Tinos Island, a low-cost desalination system based on the principles of evaporation and condensation was developed, as shown in the lower part of Figure 15, for irrigating the crops in the greenhouse that was constructed beside it.

**Figure 15.** Solar power desalination system.

Seawater was pumped into a tank used for storing sea water, then transferred into the system for desalination, stored in a second tank (Figure 16a), and finally was transferred to a third bigger tank used for the irrigation of the greenhouse crops (Figure 16b).

(**a**) (**b**)

**Figure 16.** Monitoring of water quality: (**a**) seawater tank; (**b**) irrigation tank.

To evaluate the performance of the desalination process, IoT nodes with water quality sensors were placed in the tanks for monitoring its quality parameters. Figure 17 presents the pH measurements in the seawater and desalinated water tanks.

**Figure 17.** pH monitoring.

The pH in the seawater tank varied between 8 and 8.2, with the desalinated water having differentiations on its pH, as it was affected by the performance of the desalination system, which varies depending on the weather conditions. As the salinity of the water can affect crop performance, the total dissolved solids (TDS) of the water stored in the irrigation tank were monitored. When TDS measurements exceeded the threshold defined by the user, tap water from the municipality's water supply network was added into the irrigation tank for mixing the salty water and reducing its final salinity, as shown at Figure 18.

**Figure 18.** Salinity reduction by mixing the desalinated water with tap water.

Moreover, the proposed system was used to measure the water quality and quantity of various open and closed type tanks in an eco-tourist facility. The quality measurements were used by the system to decide whether the water can be used to irrigate edible crops. In the case that the quality of the water was not acceptable for irrigation of edible crops as a result of its high turbidity, the water was used for the irrigation of non-food crops that were cultivated according to EU 2020/741 water reuse standards. Figure 16 presents the installation of the developed system in an open cistern used for collecting rainwater (Figure 19a) and in a closed tank (Figure 19b) used for collecting the reclaimed water

(**a**) (**b**)

coming from the facility. The pH and turbidity measurements of each tank are presented in Figure 20a and 20b, respectively.

**Figure 19.** Installations of the system for water quality and quantity measurements in: (**a**) open cistern; (**b**) closed tank.

**Figure 20.** Water quality measurements retrieved from: (**a**) open cistern; (**b**) closed tank.

### *3.4. Irrigation Scheduling*

The accuracy of the data provided in combination with the IoT node, which can be installed in any agricultural cropping system and activate different actuators, shapes the system's ability to perform precise calculations of irrigation water needs and apply automated irrigation. To achieve this, the FAO56 Penman-Monteith model [41] for computing crop water requirements was used. All the parameters for determining evapotranspiration were retrieved from sensors connected to the IoT node for monitoring the microclimate and the soil, while electrovalves were controlled from the node for enabling automated irrigation. A greenhouse was split into four plots, in which different tropical crops, such as bananas and pineapples, were cultivated. The irrigation of each plot was achieved using a drip irrigation system, and the irrigation schedule was fully automated using the developed IoT node (Figure 21).

**Figure 21.** IoT nodes for automating the irrigation in the greenhouse.

Figure 22 presents the average soil moisture per day and the days in which irrigation was applied (1 = Irrigation, 0 = No irrigation) from 15 October 2021 to 12 November 2021. From the figure, it is clear that the system was capable of efficiently irrigating the crops without stressing them, keeping soil moisture between 30 and 38%. Moreover, as evapotranspiration reduces during the winter, it clearly seems that the frequency of irrigation is lower in November compared to that in October.

**Figure 22.** Automated irrigation using the IoT node.

The system was also tested in open crops. Figure 23 presents the average soil moisture per day, in a clay loam field cultivated with onions that was automatically irrigated by the system.

**Figure 23.** Average soil moisture per day.

#### *3.5. Energy Autonomy*

As the developed node can provide extensive autonomy, a node was installed on 1 December 2020 in a forest, configured to have a sampling rate of 8 h for minimizing its consumption, as the high and dense canopy of the trees does not allow recharging using solar panels and negatively affects the mobile network signal strength. As a result of the weak signal, the node communicated using a normal communication protocol (GPRS–2G), which has a higher energy consumption compared to low-power protocols, but provides a higher range of coverage. After one year of operation, on 20 February 2022, the remaining battery capacity was 64%, achieving an average energy consumption of 2.4% per month.

With a sampling rate of one hour, which is acceptable in most cases of agricultural monitoring (e.g., soil moisture content measurements), the system has an energy autonomy of 210 days. This makes the IoT node ideal for using it on any annual crop, as it can work during the entire cropping period without recharging. In the case that more intensive measurements are needed, a solar panel of less than 0.5 W is capable of providing to the node the energy required for its operation.

#### **4. Discussion**

The findings in this study indicate that low-cost technologies and standards can be used for developing low-cost, highly accurate, and easy-to-use systems that can be applied to enable irrigation scheduling and water management. As the node was exclusively based on the Arduino architecture and components, its hardware cost was very low, making it affordable to any farmer. The node was developed as a pure IoT device supporting cellular network technology protocols, making it capable of working in any area in which a cellular network is available.

Furthermore, as the price of sensors is constantly dropping, farmers can purchase sensors of high accuracy that can almost provide a perfect coefficient of determination (R2 = 0.9914) at a very low price, permitting the fast depreciation of the investment for the system. As these sensors can provide data of high quality, their use can help farmers in decision making, by minimizing the inputs' cost and increasing their production. Likewise, low-cost actuators can be applied for automating and for remote controlling water management, increasing the usability of the system.

The sensors, after small modifications mostly related to making them waterproof, were introduced to be sufficiently reliable. More testing will be needed for evaluating their durability over time in the open agricultural environment.

The system was able to provide a variety of different type of measurements, including weather data, water quantity data, water quality data, and soil data. By computing crop water requirements, it was possible to automate irrigation scheduling providing the optimal water quantity, while simultaneously minimizing its consumption. Moreover, the system proved its capabilities on managing the different water sources in real environment in an extensive pilot testing that was conducted in three different pilot sites.

The system was developed as a "plug and play" device and pushing its start button is the only action needed for making the node fully functional. By adopting this simplified user experience, there is no need of any special knowledge or training for installing and configuring it, contributing on removing the demographic traits of the farmers barriers, which affect the adoption of new technologies.

Its small size, its durability, and its extensive energy autonomy make it suitable for a lot of cases, providing its effectiveness and usability. The final prototype was ready for testing in an operational environment in January 2020, and to date more than 200 systems have been installed. The system has proved to be extremely reliable, as to date there have been no hardware fails. Its development with open source Arduino technologies makes it modular, flexible, and upgradable to support more sensors and actuators than the existing ones, finally suggesting its capability for application in a vast number of agricultural operations in the future.

As the global population is constantly increasing and the cultivated areas are decreasing, new technologies will become a necessity as the only sustainable way for increasing agricultural output. It seems that low-cost IoT technologies will play a critical role in this transition, and they will contribute to the entering in the new era of holistic farm management, assisted by the extensive monitoring of the agricultural environment and automation of field operations.

Originally, the IoT system was developed for monitoring and controlling water to enable smart irrigation in open fields. As a result of its characteristics (very small size, energy autonomy, automation capabilities, high accuracy, support of different types of sensors, IP67 protection, and its low price), the node was already tested in various environments as forestry (monitoring of environmental parameters in forests), large water infrastructures (monitoring of water quantities), meteorology (for monitoring the weather), and for smart cities with very promising initial results.

### **5. Conclusions**

From the presented results, it can be concluded that:


**Author Contributions:** Conceptualization, Z.T.; methodology, Z.T. and E.S.; writing—original draft preparation, Z.T.; writing—review and editing, E.S., S.F., I.G. and T.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Horizon 2020 research and innovation program "Demonstration of water loops with innovative regenerative business models for the Mediterranean region—HYDROUSA" (grant agreement No. 776643).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This research was supported by the Horizon 2020 research and innovation program "Demonstration of water loops with innovative regenerative business models for the Mediterranean region—HYDROUSA" (grant agreement No 776643).

**Conflicts of Interest:** The authors declare no conflict of interest.
