A GSM-Based Wireless Sensor Network for Real-Time Monitoring and Control of Agricultural Parameters

In this study, a GSM-based wireless sensor network architecture is proposed to support precision agriculture through remote monitoring and control of vital environmental parameters. The work establishes real-time data acquisition and analysis to identify the growing significance of data-driven farming in contemporary agriculture. The network employs sensors to monitor temperature, humidity, soil moisture, pH, and water levels and send this data through GSM modules to a central server. A strong backend system, developed with Python frameworks such as Flask or Django, processes the sensor data and converts it into actionable information for farmers. Machine learning algorithms are incorporated intopredictive functions such as irrigation scheduling and early warning of unfavorable conditions The system allows bidirectional communication, enabling farmers to control agricultural environments through mobile apps or web-based dashboards remotely. Real-time sensor readings are displayed on mobile phones, with SMS alerts and notifications activated when parameters stray from set thresholds. The frontend interface, which is built using Android, JavaScript, and the Kivy framework, provides an easy-to-use interface for remote monitoring and control, allowing for quick responses to environmental fluctuations. The system makes a contribution to precision agriculture through the provision of an integrated solution for remote monitoring and control of critical agricultural parameters. Machine learning integration enables informed decision-making from real-time sensor readings. With the combination of sensor data acquisition, GSM voice/data capabilities, and user-friendly interfaces, the system facilitates farmers to maximize resource use, sustain soil health, and enhance crop management as a whole. This method seeks to reduce resource wastage and maximize farm productivity.