A cutting-edge IoT-based solution for precision agriculture, integrating real-time crop monitoring, weed detection, and robotic weed removal on a single robotic platform. This project leverages sensors, machine learning, and robotics to optimize farming efficiency and sustainability.
🚀 Features
-
Real-Time Monitoring: Sensors track soil moisture, temperature, and humidity, streaming data to Firebase and a live web server with AI-driven recommendations.
-
Weed Detection: ESP32-CAM captures plant images, processed locally by an ML model to identify weeds.
-
Robotic Weed Removal: A manipulator arm, controlled via ESP32-CAM live feed, removes weeds, with Gazebo simulation for remote operation.
-
Integrated System: All components run on a single bot for a compact, efficient solution.
📖 Project Components
-
Crop Monitoring & AI Recommendations Sensors collect environmental data and send it to Firebase. A web server displays real-time values and provides AI suggestions (e.g., irrigation, fertilization) to improve crop health.
-
Weed Detection with ESP32-CAM The ESP32-CAM captures plant images, which a local server processes using a machine learning model to detect weeds. Snapshots are stored for analysis.
-
Robotic Weed Removal A manipulator arm removes weeds, guided by the ESP32-CAM's live feed. The system is simulated in Gazebo for remote control and testing.
🎮 Usage
-
Monitor Crops: Access the web server to view sensor data and AI recommendations.
-
Detect Weeds: Use the ESP32-CAM to capture images; the ML model will identify weeds.
-
Remove Weeds: Control the manipulator via live feed or simulate in Gazebo for weed removal.
🔮 Future Improvements
-
Add sensors for pH and nutrient levels.
-
Enhance weed detection with larger datasets.
-
Implement autonomous navigation using SLAM.
-
Improve web interface with historical data and alerts.