The E-Tongue (Electronic Tongue) for Dravya Identification is an AI-powered system designed to analyze, classify, and identify herbal liquid samples based on their chemical properties. Inspired by the human taste mechanism, the system utilizes multiple sensors and machine learning techniques to generate unique taste profiles for different herbal formulations.
This project aims to provide a reliable, cost-effective, and portable solution for herbal medicine authentication, quality control, and research applications.
- Identify and classify different herbal liquids (Dravyas).
- Analyze liquid properties using sensor arrays.
- Generate unique taste profiles for each sample.
- Improve quality assessment and authentication processes.
- Integrate embedded systems with machine learning for intelligent classification.
- Real-time liquid analysis
- Multi-sensor data acquisition
- AI/ML-based classification
- Herbal medicine identification
- Portable embedded system
- Accurate quality assessment
- User-friendly monitoring interface
- ESP32 Microcontroller
- pH Sensor
- TDS Sensor
- Conductivity Sensor
- Temperature Sensor
- Power Supply Module
- Connecting Wires and Breadboard
- Arduino IDE
- Python
- Scikit-Learn
- Pandas
- NumPy
- Matplotlib
- Google Colab / Jupyter Notebook
- Git & GitHub
- The herbal liquid sample is introduced to the sensor array.
- Sensors measure various chemical and physical parameters.
- ESP32 collects sensor readings.
- Data is transmitted for processing and analysis.
- Machine learning algorithms identify patterns in the collected data.
- The system classifies and identifies the corresponding Dravya.
- Results are displayed and stored for future reference.
- Herbal Medicine Authentication
- Ayurvedic Research
- Quality Control Laboratories
- Pharmaceutical Industry
- Food & Beverage Testing
- Educational and Research Institutions
E-Tongue-for-Dravya-Identification
│
├── Hardware
│ ├── Circuit_Diagram.png
│ └── Sensor_Setup
│
├── Software
│ ├── ESP32_Code
│ └── ML_Model
│
├── Documentation
│ ├── Report.pdf
│ └── Presentation.pptx
│
├── Images
│ └── Prototype.jpg
│
└── README.md
- Mobile application integration
- Cloud-based monitoring
- Enhanced sensor arrays
- Deep learning-based classification
- Wireless data analytics dashboard
- Larger herbal sample database
Project images and prototype photographs will be added soon.
SIPHER
Smart Innovators for Programming and Hardware Engineering Resources
Dedicated to developing innovative solutions through embedded systems, IoT, AI, and software engineering.
This project is developed for academic and research purposes. Feel free to use, modify, and improve it with proper attribution.
If you find this project useful, please consider giving it a star on GitHub.