IoT and Machine Learning-Based Smart Soil Irrigation Farming Systems

Interfacing a Raspberry Pi with various sensors and integrating their data into a machine learning model to control pumps and provide feedback through an LCD and GSM module exemplifies an advanced application of the Internet of Things (IoT) in smart agriculture. This system uses a DHT11 sensor for temperature and humidity, a water level sensor, two soil moisture sensors, pumps, an LCD for visual feedback, and a GSM module for critical condition alerts. Each component is vital for monitoring and maintaining optimal plant growth conditions. The DHT11 sensor measures ambient temperature and humidity using a capacitive humidity sensor and a thermistor, providing a digital signal crucial for understanding environmental conditions affecting soil moisture and plant health. The water level sensor ensures there is adequate water. Two soil moisture sensors measure the soil's moisture content at different locations. The data from these sensors is essential for determining soil moisture levels, directly influencing the irrigation schedule. This moisture data is fed into a machine learning model that analyses it to predict optimal irrigation times and amounts.

The integration of a machine learning model enhances the system's intelligence. Trained on historical data, this model predicts soil moisture levels based on current and past readings, weather conditions, and other environmental factors. By analysing data from the soil moisture sensors, the model determines when the soil needs more water and activates the pumps accordingly. This predictive capability ensures plants receive the right amount of water, avoiding both under-watering and over-watering, which can harm plant health. An LCD provides visual feedback, in addition to visual feedback, the system is equipped with a GSM module to send text messages in critical conditions.