IoT- Based Smart Crop Health Monitoring System Using Machine Learning

Agricultural productivity depends on various environmental factors, making real-time monitoring of soil and atmospheric conditions crucial for crop health assessment. This paper presents an IoT-based Smart Crop Health Monitoring System utilizing Raspberry Pi and multiple sensors to determine whether a crop is healthy or not. The system integrates a moisture sensor, DHT11 sensor (for temperature and humidity), rain sensor and a light intensity sensor to monitor key environmental parameters affecting plant growth. The collected sensor data is processed on Raspberry Pi and stored both on an SD card for local logging and in Thingspeak an IoT cloud platform, for remote monitoring and analysis. To enhance decision-making, machine learning algorithms analyze the sensor data, predicting crop health status based on environmental conditions. If abnormalities such as low soil moisture, extreme temperatures, or insufficient sunlight are detected, the system provides real-time alerts and recommendations for corrective actions. This IoT-enabled precision farming solution allows farmers to remotely access crop health data via Thingspeak, ensuring timely intervention, resource optimization, and sustainable agricultural practices. By leveraging machine learning for predictive analysis, the system enhances crop productivity, minimizes manual monitoring efforts, and promotes smart farming techniques.