IoT Based Smart Agriculture Monitoring and Irrigation System

Abstract

The demand for effective agricultural methods has been heightened due to the rapid increase in population and the impact of climate change. This study focuses on the task of maximising water efficiency and monitoring crop conditions in agricultural fields. The primary objectives of this research are to develop a system that integrates sensors, microcontrollers, and cloud applications to monitor and control critical environmental factors affecting plant growth. This study employs IoT sensors, data analytics, and machine learning algorithms to conduct extensive field experiments to gather and analyse data on soil moisture, temperature, humidity, and plant physiological parameters. Conventional procedures for sensor calibration, data collection, and analysis are utilised to guarantee precision and dependability. The key findings demonstrate connections among various environmental conditions, soil moisture levels, and indicators of crop health. The data trends show how different irrigation schedules affect both crop yield and water consumption. The importance of real-time monitoring in agriculture can improve crop yield. Suggested actions involve conducting additional research on adaptive irrigation algorithms and incorporating predictive models to enhance resource efficiency in agriculture.