Building a Near Real-Time Farm Intelligence Platform Using Google Earth Engine & Sentinel Hub
Can we build a precision agriculture platform without asking farmers to buy expensive IoT devices?
That single question became the starting point of our hackathon project.
While exploring ideas, we noticed that many precision agriculture solutions depend on hardware sensors installed inside farms. Although these systems work well, they are often expensive to deploy, require maintenance, and are difficult to scale for small and marginal farmers.
So we asked ourselves a different question.
What if satellites could become the sensors?
Instead of collecting data from physical devices, could we use freely available Earth Observation data, weather intelligence, and AI to provide field-level recommendations through a simple mobile application?
This article documents our current architecture, the technologies we're exploring, and how we're approaching this problem as a hackathon MVP.
🌾 The Problem
Every day, farmers make decisions that directly affect crop yield and water usage.
Questions like:
- Should I irrigate today?
- Has yesterday's rainfall already provided enough moisture?
- Is my crop showing signs of stress?
- Will heavy rainfall arrive this week?
- Is the crop entering a new growth stage?
Most existing solutions either provide only general weather forecasts or require dedicated IoT devices installed in every field.
For many farmers, both options have limitations.
We wanted to explore whether freely available satellite data could become an affordable alternative.
💡 Our Approach
Rather than downloading massive satellite datasets, our platform retrieves information only for the registered farm boundary.
The workflow is designed to remain lightweight while still providing meaningful insights.
Farmer registers field
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Google Earth Engine
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Sentinel Hub APIs
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Latest Satellite Observation
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Weather & Forecast APIs
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Feature Extraction
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Decision Engine
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Sarvam AI
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Farmer Mobile Application
Instead of processing entire satellite archives, the system requests only the required geographic region for a specific farm.
🛰️ Why Google Earth Engine & Sentinel Hub?
Our goal is to avoid maintaining large satellite datasets ourselves.
Instead, we're exploring:
- Google Earth Engine
- Sentinel Hub APIs
- Sentinel-1 SAR
- Sentinel-2 Optical Imagery
- Weather APIs
- Rainfall Forecast APIs
Google Earth Engine provides access to large collections of Earth Observation data without requiring us to download terabytes of imagery.
Sentinel Hub allows us to efficiently retrieve processed imagery and indices for specific farm boundaries.
This architecture keeps the platform scalable while remaining practical for a hackathon prototype.
🌦️ Near Real-Time Updates
One challenge we quickly discovered is that satellite imagery isn't continuously available.
Fortunately, farming decisions don't require second-by-second updates.
Our approach combines multiple sources of information.
Whenever new satellite observations become available, the platform refreshes the farm analysis.
Between satellite observations, we continue updating recommendations using:
- Weather forecasts
- Rainfall predictions
- Temperature
- Humidity
- Wind conditions
This hybrid approach creates an experience that feels close to real time while remaining technically realistic.
🤖 AI Decision Support
Satellite imagery is useful, but raw satellite indices aren't meaningful to most farmers.
Instead of displaying technical values like:
NDVI = 0.72
our goal is to convert them into simple recommendations such as:
"Your field has received enough rainfall. Irrigation can be delayed for another three days."
For our MVP, we're exploring predictions for:
- 🌱 Crop Health
- 🌾 Growth Stage
- 💧 Moisture Stress
- 🚰 Irrigation Requirement
- 🌦️ Weather-Based Advisories
- 📈 Crop Health Trends
- ⚠️ Climate Risk Alerts
Rather than training large deep learning models during the hackathon, we're designing a modular pipeline that can gradually incorporate more advanced machine learning models as additional datasets become available.
🗣️ Local Language First
Technology should adapt to farmers—not the other way around.
We're planning to integrate Sarvam AI so farmers can communicate naturally using their preferred Indian language.
Instead of navigating complicated dashboards, they could simply ask:
"मेरे खेत को पानी कब देना चाहिए?"
or
"माझ्या शेतात काही समस्या आहे का?"
The assistant would respond with clear, personalized recommendations through both voice and text.
📱 Offline-First Experience
Internet connectivity remains inconsistent across many rural regions.
For that reason, the application is being designed with an offline-first architecture.
Even without an internet connection, farmers should still be able to access:
- Previous advisories
- Cached satellite summaries
- Farm boundaries
- Crop history
- Downloaded maps
- Voice recommendations
Whenever connectivity returns, the application synchronizes automatically.
🏗️ MVP System Architecture
Our current architecture focuses on simplicity and modularity.
Flutter Mobile App
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FastAPI Backend
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Google Sentinel Hub Weather APIs
Earth Engine
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Feature Extraction
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Decision Engine
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Sarvam AI
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Personalized Farmer Advisory
Each component is independent, making it easier to improve the system over time.
🚀 What We're Building
We're not trying to build another agriculture dashboard.
Our goal is to explore whether free Earth Observation data, weather intelligence, and multilingual AI can work together to provide an affordable digital farming assistant.
Instead of replacing farmers' experience, we hope technology can support better day-to-day decisions while reducing the need for expensive field hardware.
This project is still evolving, and we're excited to continue learning throughout the hackathon.
🌱 We'd Love Your Feedback
If you've worked with any of the following technologies, we'd love to hear your thoughts:
- Google Earth Engine
- Sentinel Hub
- Remote Sensing
- GIS
- Agricultural AI
- Flutter Maps
- Weather APIs
- FastAPI
Suggestions, feedback, or even architectural improvements are always welcome.
Building in public is one of the best ways to learn, and we'd love to hear from the community. 🚀
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