Smart Agriculture Systems: A Machine Learning and IoT Approach to Sustainable Farming

The integration of Internet of Things (IoT) technology into agriculture has the potential to revolutionize traditional farming practices, enabling more efficient, sustainable, and data driven approaches. This paper presents a Smart Agriculture System leveraging IoT sensors (soil moisture, temperature, humidity, pH, rainfall) and ML analytics to automate irrigation, optimize resources, and enhance crop management. Real-time sensor data is processed via a Raspberry Pi 3, triggering actuators (pumps, fans) to maintain optimal conditions. A crop recommendation framework evaluates four ML models, with Random Forest achieving 99.4% accuracy by correlating environmental parameters with crop suitability. The system reduces water usage by 30% through adaptive irrigation and enables remote monitoring via GSM alerts and a mobile interface. By minimizing manual intervention and harmonizing precision farming with sustainability goals, the system offers a scalable solution for enhancing yield, conserving resources, and mitigating climate risks. This paper highlights the potential of IoT in addressing the challenges of food security and sustainable farming, laying the groundwork for future innovations in precision agriculture.