AgroGuide is a real-time research project aimed at assisting farmers by providing personalized agricultural guidance through an advanced chatbot. This project, undertaken as part of our Real-Time Research Project (RTRP) in the second year of engineering studies, addresses common challenges such as language barriers and the overwhelming amount of data available to farmers, thereby empowering them to make informed decisions.
- Features
- System Architecture
- Technologies Used
- Usage
- Contributors
- Future Improvements
- Acknowledgements
- Images
- Personalized Assistance: Tailored guidance to meet the specific needs of individual farmers, ensuring that advice is relevant and actionable.
- Real-time Predictions: Employs random forest regressor models to provide real-time predictions and guidance.
- Utilization of LLM Model: The system leverages a Large Language Model (LLM) for the generative agro guide chatbot.
- Data-Driven Training: Utilizes user-defined datasets to train the chatbot model.
- Crop-Specific Expertise: Designed specifically for crops, with a comprehensive agricultural guide about rice fed into the system.
- Machine Learning Algorithms: For data analysis and real-time predictions.
- Random Forest Regressor Model: For providing real-time predictions.
- LLM: The specific chatbot model used for training.
Once the application is running, farmers can interact with the chatbot through a user-friendly interface. The chatbot provides real-time, personalized agricultural guidance based on various data inputs.
- Vitesh Balusu
- Balivada Tarun Sandilya
- Godavarthi Venkat Vamsi
- Pankaj Singh
- Chittari Ashok
- Machine Learning and NLP Enhancements: Future improvements in machine learning and natural language processing will enable more precise guidance.
- Expanded Database: Expanding the chatbot's database to include more crop-specific information.
- Integration with IoT Devices: Integrating with IoT devices for real-time monitoring.
- Collaboration with Research Institutions: Collaborating with research institutions to keep advice current and scientifically sound.
Special thanks to all the contributors and supporters who made this project possible.
