IoT Implementation in Machine Learning-Based Automatic Irrigation System for Smart Agriculture
Keywords:
Smart Agriculture, Internet of Things (IoT), Machine Learning, Automatic Irrigation, Water Optimization, Precision AgricultureAbstract
Water volumetric losses and decreased crop productivity in the agricultural sector are often caused by traditional irrigation systems that are inflexible in dealing with fluctuating environmental variables. To address this problem, this research integrates IoT architecture and Machine Learning intelligent computing to create an automatic irrigation system that is water-efficient and capable of maintaining ideal soil quality. This quantitative experiment utilizes an ESP32 microcontroller, a humidity sensor, and a DHT22 temperature sensor as instruments for collecting direct environmental data. This data is evaluated by an ML model (based on local weather data) to dynamically determine the volume and schedule of watering. The device's performance was analyzed over several weeks of operation and compared to a conventional automatic irrigation system. The system recorded 94.5% accuracy in projecting soil hydration needs. Empirical testing proved that this smart device is capable of saving water by 28.4% compared to conventional systems, while ensuring stable soil moisture within agronomic parameter limits (60%–80%). The synergy of IoT and machine learning has proven to provide a precise solution for water management in today's smart agriculture. Future research should be directed at the application of deep learning algorithms for long-term microweather predictions and implementation on a more complex variety of crop types.
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