AgriCLIP – Multilingual AI System
AI-powered agriculture platform that identifies crop diseases from images and provides instant diagnosis, treatments, and farming guidance.
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This project includes complete source code, detailed documentation, AI model integration, a modern React frontend, FastAPI backend, MongoDB database, multilingual report generation, authentication, and deployment-ready architecture. Buyers also receive setup documentation, future updates, and standard technical support, making it a complete end-to-end AI solution for agriculture, livestock, and fishery applications.
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Customizations are available upon request, including UI/UX improvements, feature additions, AI model integration, multilingual support, authentication enhanceme...
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AgriClip is an AI-powered agriculture platform designed to help farmers, students, researchers, and agricultural professionals identify crop diseases quickly and accurately using image-based analysis.
Users can upload or capture an image of a crop leaf, and the system uses a deep learning model to analyze the image and predict the most likely disease. Along with the prediction, AgriClip provides detailed information including disease symptoms, possible causes, recommended treatments, and preventive measures to help users make informed decisions.
The application features a modern React.js frontend, a FastAPI backend for high-performance API services, and MongoDB for storing disease-related information. The frontend communicates with the backend through REST APIs, enabling fast and reliable image processing and result delivery. The platform is deployed using Vercel and Render, making it accessible from anywhere.
Key Highlights
- AI-powered crop disease detection from images
- Instant disease prediction using deep learning
- Detailed disease descriptions, symptoms, causes, and treatments
- User-friendly and responsive interface
- FastAPI-based backend with REST APIs
- MongoDB database integration
- Cloud deployment using Render and Vercel
- Scalable architecture suitable for future expansion
Technologies Used
- Frontend: React.js, HTML, CSS, JavaScript
- Backend: FastAPI, Python
- Database: MongoDB
- AI/ML: Deep Learning, Computer Vision
- Deployment: Vercel, Render
- Version Control: Git, GitHub
AgriClip demonstrates how artificial intelligence and modern web technologies can be combined to create practical solutions for agriculture, helping users detect plant diseases efficiently and support better crop management.
Future Enhancements
- Support for additional crop, livestock, and fish species.
- Offline AI inference for use in remote farming areas.
- Mobile application for Android and iOS.
- Voice-based multilingual interaction for farmers.
- Integration with IoT sensors for real-time farm monitoring.
- Weather-aware disease prediction and preventive recommendations.
- Enhanced AI models for higher prediction accuracy and faster inference.
- Cloud-based analytics dashboard for agricultural insights and reporting.
Known Issues
- Prediction accuracy depends on the quality and clarity of uploaded images.
- Limited support for some rare plant diseases, livestock breeds, and fish species.
- AI inference may take longer on systems without GPU acceleration.
- Requires an active internet connection for API-based AI services.
- Large image files may increase processing time.
Installation
Prerequisites:
- Node.js (v18 or later)
- Python 3.10+
- MongoDB
- Git
1. Clone the repository:
git clone https://github.com/shabarigirishmeela/AgriLiv-T5-
cd AgriLiv-T5-
2. Install backend dependencies:
cd crop-cure-chat-backend
npm install
3. Configure environment variables:
Create a .env file in the backend directory and add the required environment variables such as MongoDB connection string, JWT secret, API keys, and FastAPI service URL.
4. Start the backend server:
npm run dev
5. Install and start the AI service:
Install the required Python dependencies using:
pip install -r requirements.txt
Start the FastAPI server:
uvicorn app:app --reload
6. Install frontend dependencies:
Open a new terminal and run:
cd crop-cure-chat-frontend
npm install
7. Start the frontend:
npm run dev
8. Ensure MongoDB is running.
9. Open the application in your browser:
http://localhost:5173
Usage
1. Open the application in your browser after starting the frontend and backend services.
2. Register a new account or log in using your credentials.
3. From the dashboard, choose the analysis category:
- Plant Disease Detection
- Livestock Analysis
- Fish Species Analysis
4. Upload a supported image (JPG, JPEG, or PNG).
5. Click the Analyze button to start the AI prediction.
6. The system processes the image using the AgriCLIP model and displays:
- Predicted class
- Confidence score
- AI-generated diagnosis
- Cause, Prevention, and Cure recommendations
7. Use the integrated AI chatbot to ask questions related to the prediction or seek additional agricultural guidance.
8. View previous analyses from the History section.
9. Download or save the generated report for future reference.
Expected Output:
- Accurate image classification for plants, livestock, and fish species.
- AI-generated diagnostic report with Cause, Prevention, and Cure.
- Confidence score for each prediction.
- Interactive chatbot assistance.
- Stored prediction history for authenticated users.
System Requirements
Operating System:
- Windows 10/11, Ubuntu 20.04+ (Linux), or macOS
Hardware Requirements:
- Processor: Intel Core i5 (or equivalent) or higher
- RAM: Minimum 8 GB (16 GB recommended)
- Storage: Minimum 10 GB free disk space
- GPU: NVIDIA GPU (optional, recommended for faster AI inference)
Software Requirements:
- Node.js v18 or later
- Python 3.10 or later
- MongoDB Community Server or MongoDB Atlas
- Git
- npm (comes with Node.js)
- Visual Studio Code (recommended)
Web Browser:
- Google Chrome (recommended)
- Microsoft Edge
- Mozilla Firefox
Network:
- Internet connection required for API access and downloading dependencies.
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