More than ever, creative solutions are required to fight water scarcity and improve resource efficiency in agriculture. Using the ESP32 Devkit-C microcontroller, this project demonstrates a smart water management system designed for precision farming. The TensorFlow Lite system integrates machine learning methods, particularly support vector machines (SVM), to provide localised and real-time irrigation decision-making. The hardware setup consists of DHT11 and soil moisture sensors connected to a relay module that manages the water pump. Based on the state of the environment at the time, the ESP32 makes intelligent irrigation decisions by interpreting sensor data. Effective edge AI processing is made possible by pre-trained machine learning models. By supplying water precisely when and where it is required, the system can increase crop yields by about 20% and improve irrigation efficiency by up to 25%. The smart water management system addresses the dynamic challenges of water resource management and promotes sustainable farming practices by integrating hardware, machine learning, and real-time decision-making to offer a scalable and flexible solution for contemporary precision agriculture.
Mahajan et al. (Thu,) studied this question.
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