Smart Agriculture System: IoT-Based Crop Health Monitoring and Analysis

This study presents the design and implementation of a cost-effective Smart Agriculture System leveraging Internet of Things (IoT) and cloud technologies to enhance crop monitoring and resource management. Traditional farming methods, often reliant on manual observation, lack real-time feedback, leading to inefficient irrigation and suboptimal yields. Our system addresses these limitations by utilizing an ESP32 microcontroller integrated with three sensors — DHT11 for temperature and humidity, a soil moisture sensor, and a light sensor.Sensor data is wirelessly transmitted to a Firebase Realtime Database and visualized through the web application Smart Sensy. The app offers dynamic graphs, historical data tracking, and realtime alerts when environmental conditions exceed predefined thresholds. This intuitive platform empowers farmers to make informed decisions, optimize irrigation schedules, and prevent crop stress.Experimental results highlight the system’s accuracy in data collection and minimal latency in wireless transmission. Compared to existing solutions, our approach reduces manual intervention, enhances data accessibility, and provides a scalable solution for precision farming. Future work will explore AI integration for predictive analytics and expand sensor support to include pH and CO2 sensors.By combining IoT, cloud computing, and data visualization, the Smart Agriculture System offers a practical solution for modernizing agriculture, fostering sustainable and efficient farming practices.