Implementation of IoT-Based Smart Agriculture for Agricultural Land Monitoring
Keywords:
Smart Agriculture, Internet of Things, land monitoring, Digital farming, Sensor IoTAbstract
The agricultural sector currently faces a number of challenges, ranging from climate anomalies, water availability crises, inefficient land monitoring, and the continued prevalence of manual management practices. Advances in Internet of Things (IoT) technology have paved the way for the use of Smart Agriculture to optimize real-time monitoring of land conditions. This study aims to design and implement an IoT-based Smart Agriculture system for monitoring agricultural land, enabling faster, more precise, and more efficient monitoring. The approach adopted is Research and Development (R&D), encompassing the stages of needs identification, system design, prototype assembly, device testing, and performance evaluation. This system was developed using a microcontroller integrated with soil moisture sensors, temperature and humidity sensors, and a soil pH meter. The data obtained is then transmitted via an internet connection to a server to be presented on a web dashboard interface. Test results show that the device successfully reads land conditions independently and transfers data to the server at specific time intervals. The soil moisture reader component is capable of tracking water volume fluctuations, both in dry and post-irrigation soil conditions. Temperature and humidity sensors display consistent data following daily weather dynamics, while the soil pH sensor produces accuracy within a reasonable tolerance range. The dashboard is proven capable of presenting up-to-date information, graphic visualizations, and warning notifications when soil moisture levels drop below the minimum limit. The implementation of Smart Agriculture with IoT technology has proven effective as a solution for monitoring today's agricultural land. This innovation can boost the efficiency of monitoring planted areas and facilitate the rapid determination of cultivation decisions. For future improvements, the system can be directed towards the integration of automatic irrigation features and predictive analytics supported by artificial intelligence (AI).
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