Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for FarmOS

FarmOS — Building Resilience for an Unpredictable Climate

The Problem

Agriculture depends heavily on environmental conditions, yet farmers increasingly have to make decisions in a world where weather and seasonal patterns are becoming harder to predict.

For small-scale farmers, a poor decision about planting, water, or crop management can have significant consequences. Weather forecasts alone are not enough—they need to be translated into information that is relevant to a specific farm and actionable at the right time.

Our Idea

FarmOS is a climate-resilient agricultural decision-support platform designed to help small-scale farmers make better decisions under uncertainty.

The system combines:

• Farm information
• Weather data
• Seasonal climate signals
• Agricultural knowledge
• Risk assessment
• Explainable recommendations
• Farmer feedback

Instead of simply telling a farmer what the weather will be, FarmOS aims to answer a more useful question:

"Given what we know and how uncertain the future is, what should I consider doing next?"

The system is designed around probabilities and uncertainty rather than pretending that the future can be predicted perfectly.

Why We Built It

We were inspired by a simple observation: technology changes extremely quickly, but fundamental human problems do not.

Agriculture will continue to depend on water, climate conditions, resources, food production, and good decision-making.

We therefore wanted to design something that is not tied permanently to one technology.

AI models will improve. New weather services will appear. Sensors will become cheaper. Satellite data will become more accessible. New interfaces will replace today's applications.

FarmOS is designed so these technologies can evolve independently of the core system.

Our goal is to build a foundation that can adapt instead of becoming obsolete.

How It Works

At a high level:

Farm Data
    +
Weather Data
    +
Seasonal Climate Signals
    +
Agricultural Knowledge
          ↓
   Decision Engine
          ↓
   Risk Assessment
          ↓
Explainable Recommendation
          ↓
       Farmer
          ↓
       Feedback
          ↓
    Improved Data

The decision engine is intentionally separated from the user interface and external data providers. This allows future technologies to be integrated without redesigning the entire platform.

Our Approach

We are building FarmOS as an engineering project rather than simply generating an AI application.

Our development process focuses on:

• Understanding the problem before coding
• Designing the architecture before implementation
• Building a functional MVP
• Testing individual components
• Validating recommendations
• Designing for uncertainty
• Considering security and privacy
• Documenting our decisions
• Using version control throughout development

AI, where appropriate, will be treated as a component of the system—not the entire product.

What We Hope to Learn

This project gives us an opportunity to bring together several areas of technology:

Linux → Programming → Git → APIs → Databases → Web Development → Data → System Design → Cybersecurity

More importantly, we want to learn how to turn a real-world problem into a working technological system.

The Long-Term Vision

The hackathon version is only the beginning.

Our long-term vision is a modular agricultural intelligence platform capable of integrating future technologies such as:

• Better climate models
• Satellite observations
• IoT sensors
• Agricultural datasets
• Improved AI models
• Mobile applications
• SMS and voice interfaces
• New agricultural services

The interface and technology can change while the underlying decision-support architecture continues to evolve.

We are not trying to predict the future perfectly.

We are trying to help farmers make better decisions in an uncertain future.

Built With

• 3.13
• api
• argon2
• css
• fastapi
• git
• github
• html
• javascript
• jwt
• open-meteo
• pwdlib
• pydantic
• pytest
• python
• sqlalchemy
• sqlite