Design and Experimental Validation of a Low-Power IoT-Based Smart Irrigation System Using LoRa, ET0, and Crop Water Stress Index for Precision Agriculture
A low-power Internet of Things (IoT)-based irrigation system that integrates environmental, soil, and crop-level information within a low-power IoT framework for adaptive irrigation management is presented.
Abstract
Efficient irrigation management requires complementary information on atmospheric demand, soil conditions, and crop water stress. This study presents a low-power Internet of Things (IoT)-based irrigation system that integrates these components within a unified monitoring and control framework. The system combines LoRa communication, ESP32-based sensor nodes, soil and meteorological sensing, FAO-56 reference evapotranspiration (ET0), and canopy-temperature-based Crop Water Stress Index (CWSI). Irrigation decisions rely on the complementary use of ET0, in situ soil measurements, and CWSI rather than on a single indicator. A hybrid time-, event-, and query-driven acquisition strategy was implemented to adapt node activity and limit communication overhead. The system was deployed under outdoor conditions in Oujda, Morocco, demonstrating integrated sensing, wireless data transmission, crop-stress monitoring, and automated irrigation control. Energy characterization further showed distinct consumption profiles across sensing, communication, actuation, and low-power operating states, supporting the use of duty cycling to limit active node operation. The results demonstrate the feasibility of integrating environmental, soil, and crop-level information within a low-power IoT framework for adaptive irrigation management.
Water scarcity and inefficient irrigation management present ongoing challenges for sustainable agricultural practice. This paper presents the design and implementation of an Internet of Things (IoT)-based smart irrigation system integrating DHT11 temperature-humidity sensors, capacitive soil moisture sensors, an ESP32 microcontroller, and automated water pump control with real-time remote monitoring capability. The system employs the Message Queuing Telemetry Transport (MQTT) protocol for secure, lightweight communication among the ESP32 firmware layer, a Golang backend server, and a ReactJS-based web dashboard, enabling remote supervision and actuation via standard web browsers. A threshold-based hysteresis control algorithm governs pump activation, triggering irrigation when soil moisture analog readings exceed 3000 (indicative of dry soil) and halting operation when readings fall below 2000 (indicative of adequately moist soil), effectively preventing rapid pump cycling. A fourteen-day evaluation demonstrated 99.29 percent MQTT message delivery reliability, a mean end-to-end latency of 555 ms, and 100 percent successful irrigation activations across eight distinct irrigation cycles with zero false-positive triggers. Sensor validation revealed a mean temperature deviation of 1.3 degrees Celsius, a humidity deviation of 3.2 percent relative humidity (RH), and a soil moisture correlation coefficient (R2) of 0.94 against gravimetric reference measurements. The system was assembled at a total hardware expenditure of under 30 United States Dollars (USD), positioning it as an accessible solution for small-scale and urban agricultural settings. Distinguishing features include integrated automated and manual control, Transport Layer Security (TLS)-secured MQTT communication, real-time Telegram-based notifications, and PostgreSQL-backed time-series data persistence.
Sheraldo Halim, D. N. Utama, Pandu Wicaksono· International Conference on...· 0 citations
Effective characterization of crop water use and water stress at the field scale remains a key limitation in precision agriculture. This study presents a multi-scale sensing framework that integrates continuous in situ IoT measurements with satellite- and UAS-derived observations to enable cross-scale assessment of crop water dynamics. The system integrates soil and open-field microclimate, and actual crop evapotranspiration (ET) sensors within a WiFi-enabled Arduino-based IoT architecture for real-time data acquisition and cloud storage. Performance was evaluated in a soybean field by comparing LI-710–derived ET with METRIC EEFLUX satellite products and the FAO-56 crop coefficient method. Results showed overestimation of crop ET by remote sensing and empirical approaches, with strong agreement in temporal patterns (R² = 0.67–0.95, p < 0.05), reflecting scale-dependent differences between continuous field measurements and temporally discrete or generalized estimates. A Water Stress Index derived from in situ measurements exhibited a strong negative correlation with soil volumetric water content and consistent relationships with NDVI from both UAS and Landsat 8/9 imagery, linking soil moisture dynamics, crop physiological response, and canopy condition. Overall, the framework demonstrates how integrating continuous field sensing with remote sensing observations enhances characterization of crop water use and water stress beyond what either sensing modality can achieve independently, providing a foundation for improved irrigation decision support under field conditions.
A. Sarr, A. Chandel, Lamine Diop et al.· Discover Agriculture· 0 citations
Measuring the water table level is a critical factor in irrigated agriculture in arid regions, as it can significantly influence the exchange of water and nutrients with crops. This work presents the design, implementation, and field validation of an open-source, solar-powered IoT device for autonomous groundwater level monitoring, combining long-range low-power LoRa communication, a non-contact pressure-based level sensor using the trapped-air capillary method, and an efficient power management stage that seamlessly switches between solar and battery power. Unlike existing commercial leveloggers, which are costly and lack integrated wireless telemetry and solar-based autonomy, the proposed platform is presented as a fully open-source, low-cost alternative purpose-built for unattended deployment in areas without grid power or cellular coverage. The system was validated through a multi-day field trial and dedicated communication tests, demonstrating a stable power conversion efficiency of 84–90%, a five-day autonomous operation without any deep-discharge event, high linearity (R2 = 0.9998) of the level module over a 0–2 m range with a resolution of approximately 1.94 mm per ADC count, and a reliable LoRa link of up to 8.51 km in an urban/suburban environment despite non-line-of-sight conditions. With an estimated hardware cost of approximately $100 USD per unit, the device represents a low-cost, low-maintenance tool capable of generating knowledge about water resources to optimize irrigation and crop management in the face of climate change.
Danilo Coletto Gallego, J. Vanzolini, Rodrigo M. Santos et al.· Hardware· 0 citations
Efficient water management is a persistent challenge in modern agriculture, especially in arid and semi-arid regions. The development and adoption of advanced soil sensor technologies are essential for optimising water use and supporting sustainable, high-yield agricultural systems. This systematic review compares primary soil sensor methodologies and synthesises findings from 156 peer-reviewed studies published between 2015 and 2025. The review assesses dielectric techniques, including time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR), as well as capacitive sensors, tensiometers, neutron probes, and instruments for measuring salinity and pH. Meta-analytical results show that TDR systems achieve high accuracy (pooled RMSE: 0.016 m³/m³) but are associated with significant costs ($500–$1200 per unit). In contrast, low-cost capacitive sensors can provide acceptable accuracy (RMSE: 0.045 m³/m³) when rigorously calibrated. LoRaWAN (Long Range Wide Area Network) is identified as the most effective communication protocol for agricultural Internet of Things (IoT) applications, with transmission ranges of 2–15 km and battery lifespans of 2–10 years. The use of machine learning methods improves irrigation scheduling accuracy by 15–30%, and artificial intelligence (AI) systems can achieve water savings of 35–50%. Key research gaps include the absence of standardised calibration procedures, as 67% of studies lack comprehensive validation, limited long-term stability assessments, with only 23% reporting data beyond six months, and insufficient investigation of multi-sensor data fusion. The review concludes by identifying future research priorities to support the development of robust and scalable innovative irrigation systems for sustainable agriculture.
Omar Talib Khazraji, Marwan J. Hussein, Ahmed M. Almawla· Academic Journal of Electric...· 0 citations
Agriculture is increasingly challenged by water scarcity, climate change, and rising energy demands, requiring more efficient and sustainable irrigation solutions. Conventional irrigation systems often lack the capability to adapt their operation to changing soil conditions and renewable energy availability, resulting in inefficient water and energy use. This study proposes a digital twin-based framework for energy management and irrigation optimization in photovoltaic (PV)-powered smart agriculture systems using Internet of Things (IoT) soil monitoring. The proposed system integrates a physical irrigation infrastructure, an IoT monitoring network, a fuzzy logic control layer, and a digital twin environment that periodically synchronizes the virtual model with IoT measurements to support the system representation and decision-making. The digital twin models soil moisture, temperature, nutrient levels, PV energy generation, battery state of charge, and irrigation water consumption. The virtual representation was periodically aligned with the physical system using measurements transmitted through the long-range (LoRa)-based network. An energy-aware irrigation scheduling strategy was developed to optimize irrigation timing based on soil conditions, battery status, and solar energy availability. The framework was evaluated using field data collected in a real apple orchard through an ESP32-based IoT platform and a standalone PV-powered irrigation system; quantitative experimental validation was performed for the soil twin. The results demonstrate high soil twin synchronization accuracy, with an overall RMSE of 1.47 percentage points and R2 of 0.981, based on experimental field measurements. The energy twin and irrigation twin were evaluated using experimentally acquired sensor data together with model-based performance assessment, demonstrating the potential of the proposed digital twin framework for integrated water–energy management in smart agriculture.
R. Kabakchieva, P. Stanchev, Nikolay Hinov· Electronics· 0 citations
Abstract Efficient irrigation is vital for crop cultivation, particularly for eggplant (Solanum melongena), which ranks as Malaysia‘s fourth most important vegetable crop. This study evaluates the effectiveness of an Internet of Things (IoT)-based smart irrigation system in enhancing eggplant growth under rain shelter conditions. The system monitored environmental and soil parameters, including air temperature, relative humidity, soil pH, electrical conductivity (EC), and soil moisture and temperature, enabling remote irrigation adjustments to optimise plant health and resource use. Plant growth and environmental data were collected using both manual and IoT-based monitoring systems. IoT sensor measurements were calibrated against manual sensors at specified intervals. Results revealed a 32-day time series of sensor data and calibration curves, with R-squared values ranging from 0.78 to 0.98, indicating strong agreement between manual and IoT measurements. Eggplants irrigated using the IoT system showed significant improvements in plant height, leaf length, leaf diameter, stem thickness and root biomass compared to manual irrigation. The IoT-based monitoring system also produced reliable and consistent measurements, supporting its accuracy and applicability. In conclusion, the IoT-based smart irrigation system enhanced eggplant growth, reduced resource wastage, and improved overall efficiency, highlighting its potential for sustainable agricultural practices.
Wan Siti Aminah Wan Mustamal, R. Ruslan, A. Aznan· Acta Technologica Agricultur...· 0 citations
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