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Development and Field Evaluation of an IoT-Based Automated Alternate Wetting and Drying Irrigation System for Rice

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2026 ASABE Annual International Meeting  2600052.(doi:10.13031/aim.202600052)
Authors:   Debjit Roy, Md. Zakir Hossen, M. A. Rob, Priya Lal Chandra Paul, Md. Belal Hossain, Mst. Shetara Yesmin, Palash Kumar Kundu, Jinat Sharmin, Md. Rezoan Bin Hafiz Pranto, Md. Towfiqul Islam, Fazlul Hasan Siddiqui, Md. Mahbubul Alam
Keywords:   Automation, AWD, IoT, smart irrigation, LoRa, precision water management, rice

Abstract. Efficient irrigation is critical for sustainable rice production in Bangladesh, where Boro rice is irrigated by groundwater-intensive conventional flooding. Alternate Wetting and Drying (AWD) can reduce irrigation demand, but implementation requires observation and timely pump operation. This study summarizes the development and field evaluation of an Internet of Things (IoT)-based automated AWD system developed at the Bangladesh Rice Research Institute. The system evolved from a solar-powered Arduino, ultrasonic sensor, and GSM configuration to a low-power LoRa signaling architecture and a multifunctional sensing platform. In Version 1.0, ultrasonic water-level measurements showed strong agreement with manual observations (d = 0.973, NSE = 0.899, and RMSE = 1.249 cm). In a Boro 2023–2024 field experiment with BRRI dhan58, automated AWD saved about 29% of irrigation water relative to conventional flooding while maintaining similar yield; grain yield was 6.8 t ha−1 and water productivity was 0.64 kg m−3. The system also reduced irrigation labor and achieved a benefit–cost ratio of 1.47. Version 2.0 replaced cellular communication with a 915-MHz LoRa-based wireless communication architecture, enabling multiple field units through a gateway and supporting cloud-based monitoring, scheduling, and control. Field tests achieved reliable transmission to 500 m and approximately 3 km observed range in an urban setting, while the rural range was projected to approach 10 km. A custom gate valve reduced valve energy demand. Version 3.0 is being advanced toward multisensor environmental monitoring and AI/ML-assisted irrigation scheduling. This development demonstrates a pathway from AWD automation to scalable precision irrigation.

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