KrishiAI

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Inspiration

Farmers often make critical decisions with incomplete information: what crop to grow, when to irrigate, how to detect soil or pest problems, where to sell produce, and how to manage farm expenses. KrishiAI was inspired by the idea that every farmer should have access to smart, simple, and practical farming intelligence, not just large agricultural businesses.

What it does

KrishiAI is an AI-powered farming companion that helps farmers manage their journey from soil to market. It supports soil analysis, crop recommendations, weather insights, land management, financial tracking, government scheme discovery, marketplace listings, and farm alerts. The goal is to help farmers make better decisions, reduce risk, and improve profitability.

How we built it

We built the frontend using React, TypeScript, Vite, and Tailwind CSS for a fast and responsive user experience. The backend is powered by FastAPI with a PostgreSQL database hosted on Neon. We deployed the frontend on Vercel and the backend on Render. The platform connects different modules like authentication, dashboard, soil analysis, marketplace, finance, weather, and recommendations into one unified farmer-focused application.

Challenges we ran into

One major challenge was connecting the deployed frontend and backend correctly, especially handling environment variables, CORS, and production API URLs. We also had to make sure the backend worked reliably on Render, with the correct dependencies and database configuration. Another challenge was designing many farming features in a way that felt useful but still simple enough for a hackathon demo.

Accomplishments that we're proud of

We are proud that KrishiAI is not just a landing page, but a working full-stack product. Users can register, log in, access a dashboard, and interact with multiple farming tools. We successfully deployed the project publicly with a live frontend, backend, and cloud database. We are also proud of building a platform that addresses a real-world problem with practical AI-driven features.

What we learned

We learned how important deployment details are in a real-world full-stack app. Small things like the wrong API URL or missing environment variable can break the user experience. We also learned how to structure a product around actual user workflows instead of isolated features. Building KrishiAI helped us understand both the technical and human side of agricultural technology.

What's next for KrishiAI

Next, we want to improve the AI soil and pest analysis, add real-time regional market prices, support more Indian languages, and integrate SMS or WhatsApp alerts for farmers. We also want to expand the marketplace, add better financial planning tools, and connect farmers with verified buyers, government schemes, and local agricultural experts.

Built With

• fastapi
• neon
• openai
• openweather
• python
• react
• typescript
• vite