Agrivo

Solution
Try AGRIVO yourself
System Architecture
Weather Forecast Page
Home Page
Home Page
AI Irrigation Advice Page
Field Analysis Page
Dashboard Page

Inspiration

"What happens if the rain never comes?"

For many Indonesian rice farmers, this is no longer a hypothetical question. Climate change has made rainfall increasingly unpredictable, turning irrigation into a difficult decision. One wrong choice can waste scarce water, reduce crop yields, and threaten farmers' livelihoods.

This challenge is deeply personal to us. One of our team members grew up in a farming family in Central Java, and through conversations with local farmers, we found that irrigation decisions are still largely based on routine rather than field-specific guidance.

We realized that farmers don't need more data—they need confidence in making the right irrigation decision at the right time.

That's why we built AGRIVO.

What it does

AGRIVO is an AI-powered irrigation decision support platform that helps rice farmers adapt to climate change through smarter irrigation decisions.

The platform analyzes weather forecasts, field conditions, and crop growth stages to generate field-specific irrigation recommendations. Farmers also receive real-time notifications and can monitor water use, crop performance, and environmental impact through an interactive dashboard.

To ensure technology is adopted in the field, AGRIVO Grow complements the platform with farmer training, field demonstrations, and continuous feedback through strategic partnerships.

How we built it

We started by understanding the real challenges faced by Indonesian rice farmers through research, field observations, and discussions with farmers.

Using these insights, we designed AGRIVO with a human-centered approach and developed a web platform powered by AI. The system combines weather data, crop growth stages, and field conditions to generate practical irrigation recommendations through an intuitive interface.

Tech Stack

Frontend: Next.js, TypeScript, Vanilla CSS Backend: FastAPI, Python, PostgreSQL AI: XGBoost + Rule Engine Weather: Open-Meteo API (Forecast + Historical) Map: Leaflet JS, OpenStreetMap Tile API Deployment: Vercel, GitHub

Tools Visual Studio Code Github Copilot Antigravity IDE V0 by Vercel Github

Challenges we ran into

One of our biggest challenges was avoiding the creation of just another agriculture dashboard. We realized that farmers don't need more data—they need clear recommendations that help them decide what to do next.

Another challenge was making AI recommendations simple and practical while maintaining scientific accuracy. This led us to develop AGRIVO Grow, ensuring that technology is supported by education and real-world implementation.

Accomplishments that we're proud of

Developed an AI-powered irrigation decision support platform tailored for rice farmers.
Combined AI technology with AGRIVO Grow to encourage real-world adoption.
Designed a solution that supports water efficiency, food security, and methane emission reduction.
Created a scalable concept that can be implemented through partnerships with governments, universities, and farmer organizations.

What we learned

This project taught us that technology alone cannot solve agricultural challenges.

We learned the importance of designing solutions around farmers' real needs, combining AI with practical implementation, and working closely with communities to encourage adoption.

What's next for Agrivo

Our next step is to validate AGRIVO through pilot projects with farmer groups and agricultural stakeholders.

We aim to improve our AI model using real field data, expand AGRIVO Grow through strategic partnerships, and scale the platform to more rice-producing regions across Indonesia.