Analysis of an Internet of Things (IoT)-Based Soil Moisture Monitoring System to Support the Irrigation of Chili Plants
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Keywords: Internet of Things (IoT), Soil Moisture, Real-Time Monitoring, Chili Plant Irrigation, Smart Farming.Abstract
Soil moisture is one of the key factors affecting the growth and productivity of chili pepper plants. Soil moisture monitoring, which is still done manually, often results in improper watering because it relies on visual observations and farmers’ experience. Advances in the Internet of Things (IoT) offer a solution through monitoring systems capable of providing real-time information on soil conditions. This study aims to analyze an Internet of Things (IoT)-based soil moisture monitoring system to support the irrigation process for chili pepper plants. The study employs a quantitative method using a systems analysis approach. Data were obtained through soil moisture measurements using sensors integrated with a microcontroller, an internet network, an IoT platform, and a monitoring dashboard. The sensor readings were then analyzed using quantitative descriptive methods to evaluate the system’s ability to support irrigation decision-making. The results showed that the system was capable of monitoring soil moisture in real time and displaying the data via the monitoring dashboard. The soil moisture values obtained during consecutive observations were 35%, 42%, 55%, 68%, 74%, and 63%. At soil moisture levels of 35% and 42%, the system identified the soil as being in the “dry” category, indicating a need for watering. After watering, the moisture level rose to 74%, indicating that the soil was in the “wet” category. The system also successfully transmitted data automatically via the internet, allowing for remote monitoring. The IoT-based soil moisture monitoring system has proven capable of supporting more effective and efficient watering of chili plants by providing accurate, real-time information on soil conditions. Future research could develop the system with an automatic watering feature to improve the efficiency of crop management.
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