Integration of Weather API and IoT in an Automatic Irrigation System Based on Soil Moisture for Sugarcane Farming
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Abstract
National sugarcane productivity in Indonesia faces significant challenges due to climate change and a reliance on rain-fed lands, as is the case in Banjarejo Village, Grobogan Regency. Prolonged drought conditions frequently lead to unstable crop growth and diminished harvest yields. This study aims to design and implement an adaptive, Internet of Things (IoT)-based automated irrigation system by integrating soil moisture sensors with weather forecast data obtained via the OpenWeatherMap Application Programming Interface (API). The system utilizes an ESP32 microcontroller as its central control unit and solar panels as its primary energy source to support sustainable agriculture. The research methodology encompasses hardware design, employing Capacitive v1.2 soil moisture sensors, a 200 Wp solar module, and a 100 Ah battery; as well as software development utilizing JSON deserialization methods on the Blynk platform. Testing results demonstrate that the sensors exhibit a remarkably high level of accuracy, with an average error rate of merely 0.528%. The integration of weather data enables the system to make more intelligent irrigation decisions; the pump activates only when soil moisture levels fall below 40% and no rainfall is forecast. Over a one-month testing period, the system achieved a 100% success rate in executing its control logic and maintaining real-time data synchronization. The use of solar panels also proved effective, generating a peak power output of 87.4 Watts under bright, sunny conditions. In conclusion, this integration of IoT technology, real-time weather data, and renewable energy successfully enhances water-use efficiency and remote monitoring effectiveness, offering an innovative solution for sugarcane farmers as they navigate dynamic environmental conditions.
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References
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