ML Assisted Smart Watering System with IoT for Cloud-Based Precision Agriculture

This paper proposes an automated water management system that uses sensing technology, cloud computing, and machine learning to optimize irrigation in agriculture. The system integrates soil moisture sensors and weather data to accurately determine the optimal water content for crops. Solenoid valves are used to regulate water flow based on the outputs of a machine-learning model. Sensor data and irrigation decisions are stored in the cloud and can be accessed by farmers through a mobile application. This innovative solution addresses the challenges posed by population growth, urbanization, and limited land resources. By minimizing water wastage and eliminating the need for manual monitoring, the system enhances agricultural productivity. Implementation of the proposed ML-based smart irrigation work proved that more than 50% water can be saved as compared to the traditional irrigation system. Additionally, it prevents waterlogging and nutrient leaching, contributing to sustainable food production in the face of a global water crisis.