AgriPulse AI – Smart Irrigation System with AI-Based Prediction AgriPulse AI is an ESP32-based smart irrigation system designed to automate water management across two independent soil zones using real-time sensor data and a trained AI model. The system continuously monitors soil moisture (per zone), ambient temperature and humidity (via DHT22), rainfall, water tank level, and soil pH. These readings are fed into an on-device logistic regression model — trained offline on sensor-pattern data and deployed directly on the ESP32 — which predicts, with a live confidence score, whether each zone requires irrigation. Based on this prediction, the system automatically activates relay-controlled irrigation valves and a water pump, while hardcoded safety interlocks (rain detection, low water level) always override the AI to prevent unnecessary or unsafe watering. The entire system runs on solar power — a photovoltaic panel charges a 12V sealed lead-acid battery through a PWM charge controller, with a buck converter regulating output to a safe 5V supply for the ESP32 — making it suitable for remote or off-grid agricultural deployment. All sensor readings and the AI's live irrigation-confidence score are transmitted over WiFi to a ThingSpeak IoT dashboard, allowing real-time, remote monitoring of field conditions. By combining low-cost embedded hardware, a genuinely trained machine learning model, solar autonomy, and cloud-based monitoring, AgriPulse AI demonstrates a practical, scalable approach to precision irrigation — reducing water waste and manual labor while remaining affordable enough for small-scale or student-level deployment.
AgriPulse AI – Smart Irrigation System with AI-Based Prediction AgriPulse AI is an ESP32-based smart irrigation system designed to automate water management across two independent soil zones using real-time sensor data and a trained AI model. The system continuously monitors soil moisture (per zone), ambient temperature and humidity (via DHT22), rainfall, water tank level, and soil pH. These readings are fed into an on-device logistic regression model — trained offline on sensor-pattern data and deployed directly on the ESP32 — which predicts, with a live confidence score, whether each zone requires irrigation. Based on this prediction, the system automatically activates relay-controlled irrigation valves and a water pump, while hardcoded safety interlocks (rain detection, low water level) always override the AI to prevent unnecessary or unsafe watering. The entire system runs on solar power — a photovoltaic panel charges a 12V sealed lead-acid battery through a PWM charge controller, with a buck converter regulating output to a safe 5V supply for the ESP32 — making it suitable for remote or off-grid agricultural deployment. All sensor readings and the AI's live irrigation-confidence score are transmitted over WiFi to a ThingSpeak IoT dashboard, allowing real-time, remote monitoring of field conditions. By combining low-cost embedded hardware, a genuinely trained machine learning model, solar autonomy, and cloud-based monitoring, AgriPulse AI demonstrates a practical, scalable approach to precision irrigation — reducing water waste and manual labor while remaining affordable enough for small-scale or student-level deployment.
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AgriPulse AI revolutionizes farming by addressing water waste and labor inefficiencies through intelligent monitoring. Utilizing sensors to track vital soil and climate conditions, the system employs on-device logistic regression to provide real-time irrigation confidence. This innovative approach not only minimizes manual labor and overwatering risks but also supports cost-effective off-grid solutions. With a user-friendly dashboard, farmers can remotely monitor their fields, ensuring...