Adaptive Low-Power LoRa WSN for Real-Time Soil Monitoring in Remote Oil Palm Plantations (Faculty of Engineering, Universitas Tanjungpura, 2026)
Oil palm plantations in remote regions such as Sebatik, North Kalimantan, face significant challenges in sustainable soil management due to limited infrastructure and dynamic peat soil conditions. Conventional monitoring methods lack real-time capability and energy efficiency. To address this, this research proposes a novel adaptive low-power LoRa-based Wireless Sensor Network (WSN) that dynamically adjusts sensing and transmission frequency based on real-time soil parameters—specifically, moisture, temperature, and pH. Unlike fixed-interval systems, the proposed architecture implements edge-based logic on ESP32 nodes to escalate sampling during critical events (e.g., pH ≤ 4.5) and reduce activity during stable periods, optimizing energy use without cloud dependency. The system integrates LoRa SX1278 modules, a RAK2245 gateway, ChirpStack for secure data routing, and OpenRemote for visualization and alerts. Field testing over 7 days in three micro-zones (roadside, plantation center, drainage) demonstrated robust performance with average Packet Delivery Ratios of 97.2%, 82.5%, and 88.3%, respectively, and a communication range of up to 2.8 km. Crucially, the adaptive strategy reduced daily power consumption to 7.8 mAh—58% lower than a fixed 10-minute schedule—extending theoretical battery life from 6–8 months to over 14 months. Sensor accuracy remained high (moisture error: 1.68%; temperature: 3.09%; pH: 1.47 units), enabling timely agronomic interventions such as targeted liming. This work contributes an environment-responsive WSN architecture that balances energy efficiency and event responsiveness, offering a scalable, deployable model for precision agriculture in tropical peripheral regions with acidic soils and intermittent connectivity.