Our project, “AI-Based Root Zone Water Dynamics Prediction and Weather-Aware Adaptive Irrigation System Using IoT,” was inspired by the problem of water wastage in conventional irrigation systems. Most existing systems depend on a single soil moisture sensor and irrigate only when the soil becomes dry. They also do not consider moisture at different root-zone depths or weather conditions such as rainfall.
We developed a smart irrigation system using ESP32, three capacitive soil moisture sensors, DHT22 temperature and humidity sensor, rain sensor, water flow sensor, relay module, and a mini water pump. The soil moisture sensors are placed at 5 cm, 15 cm, and 30 cm depths to monitor the root zone.
The collected sensor data is analyzed using AI to predict root-zone moisture, drying rate, future water stress, weather impact, and required irrigation duration. Based on the AI decision, the relay automatically controls the water pump.
Through this project, we learned about sensor integration, ESP32 programming, IoT monitoring, multi-depth soil moisture analysis, and AI-based irrigation decision-making. The main challenges were integrating multiple sensors, monitoring different soil depths, measuring water flow accurately, and considering rainfall while making irrigation decisions.
The project aims to provide efficient, predictive, and weather-aware irrigation, reduce water wastage, and improve irrigation efficiency for precision agriculture
Built With
- arduino
- artificial-intelligence
- blynk
- c++
- dht22
- esp32
- iot
- machine-learning
- rain-sensor
- relay
- soil-moisture-sensor
- water-flow-sensor
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