Design and Experimental Validation of a Low-Power IoT-Based Smart Irrigation System Using LoRa, ET0, and Crop Water Stress Index for Precision Agriculture

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.

Plant phenotyping relevance

低消費電力IoT基盤の開発・実証が中心で、作物の水ストレスをCWSIで取得・監視する植物フェノタイピング要素が技術的に組み込まれている。

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