Integrated Farming System Prototype to Promote Agricultural Modernization and IoT-Based Land Utilization - Iqbal Faturachman Usman; Syamsir Syamsir; Stephan A. Hulukati; Abdul Majid Gaffar (Electrical Engineering Department Faculty of Engineering State University of Gorontalo, 2026)
This study designs and evaluates an Internet of Things (IoT)–based integrated farming prototype to address limited land availability and improve water-use efficiency at a small scale. The system employs an ESP32-S3 microcontroller integrated with soil moisture (SEN0308), water temperature (DS18B20), air temperature–humidity (DHT11), pH, TDS, and a YF-B1 flowmeter sensors. Measurements are acquired periodically and transmitted in real time to Google Sheets via a Google Apps Script Web App for logging and dashboard visualization. Irrigation control is implemented using a hysteresis strategy: the solenoid valve is activated (ON) when soil moisture falls below 50% and deactivated (OFF) when it exceeds 55%, with an additional flow-duration limit to prevent rapid ON–OFF cycling. An 8-day experiment shows stable system operation. The average water temperature was 29.01°C (range 22.34–35.94°C), air humidity reached 73.52%, and soil moisture averaged 52.47%; pH was 6.35 (4.13–7.00), TDS was 664 ppm (583–760 ppm), and the flow rate was 0.36 L/min with a total distributed volume of approximately 4,190 L. The system consistently turns the solenoid ON when soil moisture is below the ON threshold (50%) and turns it OFF when it exceeds the OFF threshold (55%), thereby maintaining soil moisture within the intended operating range. Compared with manual irrigation over the same testing period (6,588.08 L), the proposed system reduced water consumption by 36.4% (to 4,190.00 L). The main contribution of this work is the application of a simple hysteresis logic that is proven stable and efficient for drip irrigation in a small-scale integrated farming system. These findings confirm that IoT technology can modernize integrated farming on limited land while supporting circular agriculture and food resilience through data-driven monitoring and automated decision-making.