Efficient water management is crucial for sustainable agriculture, and the integration of Internet of Things (IoT) technologies in irrigation systems offers innovative solutions to optimize resource use. In this systematic review, the current landscape of Internet of Things (IoT) applications in irrigation management was investigated. The study aimed to identify key research trends and technological developments in the field. Using VOSviewer (CWTS, Leiden, The Netherlands) for bibliometric mapping, the influential research clusters were identified. The analysis revealed a significant rise in scholarly interest, with peak activity between 2020 and 2022, and a shift towards interdisciplinary and applied research. Additionally, the content analysis revealed prevalent agricultural applications, frequently employed microcontroller units (MCUs), widely used sensors, and trends in communication technologies such as the increasing adoption of low-power, scalable communication protocols for real-time data acquisition. This study not only offers a comprehensive overview of the current status of IoT integration in smart irrigation but also highlights the technological advancements. Future research directions include integrating IoT with emerging technologies such as artificial intelligence, edge computing, and blockchain to enhance decision-support systems and predictive irrigation strategies. By examining the transformative potential of IoT, this study provides valuable insights for researchers and practitioners seeking to enhance agricultural productivity, optimize resource use, and improve sustainability.

The calibration of capacitive soil moisture sensors is an essential step towards their integration into smart solutions. This study investigates the calibration of a widely used low-cost capacitive soil moisture sensor (SKU:SEN0193, DFRobot, Shanghai, China) in a loamy silt soil typically found in the Puglia region of Italy. The calibration function was derived from a random sample of 12 sensors, with three soil sample replicas per sensor, each of which had one of five gravimetric soil moisture contents, from relatively dry (5%) to full saturation (40%). The study reports the resulting calibration function along with the accuracy achieved with the generalized calibration function. The sensors proved to be accurate, with an R² value ranging between 0.85 and 0.87 and a root mean square value (RMSE) ranging between 4.5 and 4.9%. The variation between the sensors was also investigated. The results showed that with higher soil moisture contents (above 30%), the sensor-to-sensor variability becomes significant, with a coefficient of variation (CV) ranging between 10 and 16%; meanwhile, in lower soil moisture contents, the CV ranged between 6.5 and 10.3%, implying that it is more consistent in lower moisture content within this soil condition. The resulting calibration function enhances the integration of such low-cost sensors into smart farming solutions. With proper calibration, these affordable capacitive sensors can achieve a high degree of accuracy, making them a viable option for widespread use in cost-effective precision agricultural applications.

Agrivoltaics, a system combining the production of agricultural crops and solar energy on the same land area, offers a potential solution to land use competition between different sectors. However, concerns have been raised regarding the impact of shade on plant growth under Agrivoltaic Systems (AVSs). Numerous studies have explored the effects of AVSs shading on agricultural crops. However, most of these studies focused on shade-tolerant crops, leaving a gap in the understanding of how these systems affect shade-intolerant crops. To this end, this study was conducted in Bari, southern Italy, using two types of AVSs: conventional (Con) and semi-transparent (ST) panels. The objective was to assess the impacts of the different levels of shading on the tomato yield and fruit quality. Tomato cultivation occurred between May and August under various conditions: Con panels, ST panels, and Open Field. The results revealed that soil temperature decreased under both AVSs compared to in the open field conditions. However, the significant reduction in photosynthetically active radiation (PAR), up to 43% in ST and 67% in Con, led to yield reductions ranging between 28% and 58% in ST and Con, respectively. Nonetheless, AVSs demonstrated their potential to reduce irrigation water demand by over 15% in ST and more than 20% in Con. Interestingly, the AVSs reduced fruit size but improved certain fruit quality attributes, such as titratable acidity, which is closely correlated with fruit flavour. These findings highlight the challenges of cultivating shade-intolerant crops under AVSs in a Mediterranean climate, while temperate, dry conditions may offer more favourable prospects for agricultural production.

Zeolites can be extensively employed in agricultural activities because they improve soil properties such as infiltration rates, saturated hydraulic conductivity, water holding capacity, and cation exchange capacity. Natural and synthetic zeolites can efficiently hold water. Zeolites are also believed to have the ability to lose and gain water reversibly, without changing their crystal structure. In the present study, several laboratory tests were carried out using: (i) zeolite synthesized from coal fly ash (a waste product from burning coal in thermoelectric power plants), (ii) a silty loam soil, typically found in Southern Italy, and (iii) sunflower as a reference plant. The selected soil was amended with different percentages of zeolite (1, 2, 5, and 10%) and the effects of the synthetic mineral addition on the hydrophysical properties of the soil and plant growth were evaluated. The results indicated that soil–zeolite mixtures retained water more efficiently by pore radius modification. However, this causes a variation in the range of plant-available water towards higher soil humidity values, as the amount of added zeolite increases. These data confirm that zeolite addition modifies the selected hydrophysical properties of the soil with the effect of decreasing the soil drainage capacity, making the soil less habitable for plant growth.

Efficient irrigation management is crucial for optimizing water use and productivity in agriculture, particularly in water-scarce regions. This study evaluated the effectiveness of soil-based and weather-based irrigation management using a low-cost (DIY) Internet of Things (IoT) capacitive soil moisture sensor on drip-irrigated lettuce. A field experiment was conducted to compare water productivity and water use efficiency between the two management approaches. The soil-based system utilized real-time data from IoT sensors to guide irrigation scheduling, while the weather-based system relied on evapotranspiration data. The IoT-enabled system used 28.8% less water and reduced the pumping hours by 16.2% compared with the conventional weather-based methods. In terms of crop water productivity (CWP), the IoT system reached 16 kg/m³, which was 52.5% higher than the conventional method (10.5 kg/m³). Furthermore, the developed DIY sensor was compared with existing commercial soil moisture sensors, namely, Teros 54 and Drill& Drop. The developed prototype demonstrated reliability and accuracy comparable to other commercial sensors, with an R² = 0.6, validating its utility for enhanced data-driven irrigation, giving its initial low cost (USD 62). These findings highlight the potential of low-cost soil-based IoT systems in enhancing irrigation efficiency and supporting sustainable agriculture, particularly in resource-limited settings.

This paper mainly aims to illustrate an irrigation management tool to simulate scheduling of district-level water needs over the course of an irrigation season. The tool is mostly based on a daily model for simulating flow of water (and solutes) in heterogeneous agri-environmental systems (called FLOWS-HAGES). The model produces information on the daily evolution of: soil water contents and pressure potentials in the soil profile; water uptake and actual evapotranspiration; stress periods for each crop; return fluxes to the groundwater and their quality in terms of solute concentrations (e.g., nitrates). FLOWS-HAGES provides a daily list of hydrants to be operated according to water or crop-based criteria. The daily optimal sequence of hydrant use may thus be established by passing the volumes to be delivered on to the model for simulating the hydraulics of the irrigation network, in order to ensure that the discharges flowing inside the network of distribution pipes are delivered under optimal pressure head distribution in the system. All the above evaluations can be carried out in a stochastic framework to account for soil heterogeneity and climate changes. To illustrate the potential of FLOWS-HAGES, a case study was considered for a selected sector of the Irrigation District 10 in the “Sinistra Ofanto” irrigation system (southern Italy, Apulia region). In a 139 ha area (Sector 6 of the Irrigation District), soil profiles were analyzed for characterization of hydraulic properties variability. Hydraulic properties were determined by a combination of field and laboratory measurements. Model simulations were validated by comparing soil water storage simulated and measured by a sensor based on electromagnetic induction technique. Irrigation water volumes and frequency calculated by the model were compared to the volumes actually supplied by the farmers. Compared to the farmers behavior, the model simulates more frequent irrigations with lower irrigation volumes. Finally, some indexes of irrigation performance were calculated for each farm under study. The resulting maps provide useful information on the spatial distribution of farmer behavior, indicating the abuse or underuse of water as well as the fraction of the water lost by drainage following the irrigation method applied.