An IoT-Based Smart Agriculture System for Real-Time Field Monitoring and Crop Prediction

This work presents a smart agriculture system integrating multi-sensor data acquisition (soil moisture, temperature, humidity, and light) with Arduino Mega 2560 and ESP8266 for real-time monitoring and irrigation control. Machine learning (Random Forest Classifier) achieved 87.5% accuracy in crop prediction, improving water efficiency and supporting sustainable farming practices.