Optimization of Rice Field Irrigation Based on Fuzzy Logic and the Internet of Things Through Water Level Analysis

Abstract

The low efficiency of conventional irrigation systems often results in water waste and decreased rice productivity. The research was carried out by designing an automatic monitoring and control system using a water level sensor, a Raspberry Pi Pico W microcontroller, a water pump, and a Blynk application as a real-time monitoring medium. Water level data is processed by fuzzy logic method to categorize low, normal, or high conditions, so that the system can adjust the water pump adaptively according to the needs of the land. The results of the study show that the integration of IoT and fuzzy logic is able to improve water use efficiency, maintain soil moisture at optimal conditions, and support better rice growth. The system has also been proven to be accurate in the classification of water conditions with a success rate above 90%. Thus, this research contributes to the development of smart agricultural technologies that can increase productivity while supporting sustainable agricultural practices.