Project Info
Inspiration
Agriculture is the backbone of Pakistan's economy, yet many farmers still rely on intuition or fragmented information when making critical decisions about crop selection, irrigation, and resource allocation. We wanted to explore how AI could simplify these decisions by combining weather intelligence, profitability analysis, and optimization into a single platform. KissanAI was built to demonstrate how modern AI can empower farmers with data-driven insights that improve productivity while reducing risk.
What it does
KissanAI is an AI-powered agricultural intelligence platform that helps farmers make informed farming decisions. It recommends suitable crops based on environmental conditions, forecasts expected profitability, analyzes weather-related risks, provides irrigation recommendations, and optimizes land allocation for maximum returns. The platform presents these insights through an interactive dashboard with easy-to-understand visualizations and exportable reports.
How we built it
We built the frontend using React, TypeScript, Vite, Tailwind CSS, Recharts, and Framer Motion to create a modern and responsive user experience. The backend is powered by FastAPI, PostgreSQL, SQLAlchemy, Alembic, and Pydantic, providing a scalable REST API architecture. AI-driven analytics workflows combine weather information, crop suitability analysis, profitability estimation, and optimization logic to generate practical recommendations for farmers.
Challenges we ran into
The biggest challenge was designing a recommendation system that balances multiple agricultural factors simultaneously. Crop suitability, weather conditions, irrigation requirements, and profitability often influence one another, making it difficult to produce recommendations that are both accurate and practical. Another challenge was presenting complex agricultural analytics in a way that remains simple and intuitive for end users.
Accomplishments we're proud of
We're proud of building an end-to-end AI-powered platform that brings together multiple agricultural intelligence capabilities into a unified system. Instead of solving a single problem, KissanAI provides comprehensive decision support through crop recommendations, profitability forecasting, weather analysis, irrigation intelligence, land allocation optimization, and interactive analytics within one application.
What we learned
Building KissanAI strengthened our understanding of full-stack AI application development, scalable API design with FastAPI, database modeling using PostgreSQL, and creating intuitive data visualizations with React. More importantly, we learned that effective AI solutions are not just about predictions—they must deliver actionable insights that users can easily understand and trust.
What's next
for KissanAI Our roadmap includes satellite imagery integration, machine learning-based yield forecasting, GIS-enabled farm visualization, multilingual support for regional farmers, AI-powered seasonal forecasting, market price prediction, and a mobile application. We also plan to incorporate explainable AI techniques so farmers can better understand the reasoning behind every recommendation.
KissanAI 🌾
AI-powered agricultural intelligence platform for Punjab farmers featuring crop recommendation, profitability forecasting, weather-risk analysis, irrigation intelligence, and smart land allocation optimization.
Built using React, FastAPI, PostgreSQL, and AI-driven analytics workflows.
Features
- AI crop recommendation engine
- Weather-aware agricultural analysis
- Profitability prediction
- Irrigation intelligence
- Smart land allocation optimization
- Interactive analytics dashboard
- Real-time weather integration
- Recommendation risk analysis
- Exportable reports
Tech Stack
Frontend
- React
- TypeScript
- Vite
- TailwindCSS
- Recharts
- Framer Motion
Backend
- FastAPI
- PostgreSQL
- SQLAlchemy
- Alembic
- Pydantic
Project Structure
KissanAi/
├── backend/
├── frontend/
├── docs/
└── README.md
Setup Instructions
Backend
cd backend
pip install -r requirements.txt
python -m alembic upgrade head
python scripts/seed_data.py
python -m uvicorn app.main:app --reload
Frontend
cd frontend
npm install
npm run dev
API Endpoints
| Method | Endpoint |
|---|---|
| POST | /api/recommendations/generate |
| GET | /api/weather/{district} |
| GET | /api/health |
Future Roadmap
- Satellite imagery integration
- Machine learning yield forecasting
- Multilingual farmer assistant
- Mobile application
- Historical trend analysis
- AI-powered seasonal forecasting
License
MIT License
Analysis
View
Metric
No commits on this project resolved to a GitHub account.
Technology
- CSSIn code
- FastAPIIn code
- HTMLIn code
- JavaScriptIn code
- PythonIn code
- ReactIn code
- Tailwind CSSIn code
- TypeScriptIn code
- OpenAIClaimed
8 of 9 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
292 KB
Source files
112
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
msami-ullah-ai/KissanAi
123 files · 419 KB · @ fe55349
Structure
Interface
59 files · 48%Screens, components and styles rendered to the user.
API & routing
5 files · 4%Request entry points: routes, handlers and controllers.
Application logic
23 files · 19%Domain rules, services and shared utilities.
Data & schema
20 files · 16%Schema definitions, migrations and data access.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- TypeScript66%
- Python32%
- Markdown1%
- CSS1%
- JavaScript0%
- HTML0%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
frontend/package.json
npm · 19- axios
- framer-motion
- html2canvas
- jspdf
- lucide-react
- react
- react-dom
- react-router-dom
- recharts
- +10 more
backend/requirements.txt
pypi · 12- alembic
- fastapi
- httpx
- pandas
- psycopg2-binary
- pydantic
- pydantic-settings
- python-dotenv
- requests
- scikit-learn
- sqlalchemy
- uvicorn
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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