StellaCrop

Satellite-Guided Agentic AI for Smart Farming

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🌱 StellaCrop: Satellite-Guided Agentic AI for Smart Farming

💡 Inspiration

Agriculture is the backbone of our society, yet millions of farmers suffer from unpredictable weather, undetected crop stress, and lack of timely advisory support. I was inspired to build StellaCrop after learning how much yield loss (up to 30–40% annually) could be prevented with better monitoring and early action.
With affordable satellite imagery and the rise of agentic AI systems that can reason and act, I envisioned StellaCrop as a personal AI assistant for farmers, powered by space and intelligence.

⚙️ How We Built It

I developed StellaCrop in four stages:

  1. Data Collection 🌍

    • Downloaded and preprocessed Sentinel-2 multispectral imagery.
    • Applied cloud masking and spatial cropping to isolate farm regions.

  2. AI Models 🤖

    • Computed vegetation indices (NDVI, EVI) for crop health:

$$ NDVI = \frac{(NIR - RED)}{(NIR + RED)} $$

  • Fine-tuned a CNN on crop disease datasets.
  • Integrated an LLM agent to combine weather, soil, and imagery insights into farmer-friendly recommendations.

  1. System Architecture 🏗️

    • Backend: Python (FastAPI) for data processing and AI inference.
    • Frontend: React dashboard with heatmaps and alert reports.
    • APIs: Weather + soil data integrated for contextual insights.

  2. Deployment 🚀

    • Packaged with Docker.
    • Hosted prototype on a cloud VM for demonstration.

🚧 Challenges We Ran Into

  • Satellite image noise: Frequent cloud cover degraded quality → solved with temporal averaging + cloud masks.
  • Limited dataset: Scarcity of labeled crop disease images → mitigated with transfer learning + augmentation.
  • Agent reliability: Ensuring the AI provided actionable advice rather than vague responses → used prompt engineering + safety rules.
  • Solo execution: Handling all roles (data scientist, backend, frontend, and deployment) → managed with GitHub projects and strict timeboxing.

🏆 Accomplishments That We’re Proud Of

  • Built a working prototype that combines satellite imagery + agentic AI in just one hackathon.
  • Translated complex AI outputs into simple, farmer-friendly insights.
  • Designed StellaCrop as scalable – it can be extended to new regions and crop types.
  • Proved that even as a solo hacker, it’s possible to build an end-to-end impactful project.

📚 What We Learned

  • How to process multi-spectral satellite data for agriculture.
  • The power of vegetation indices like NDVI for crop health monitoring.
  • How agentic AI systems can dynamically choose tools (weather API, ML model, etc.) and deliver explainable outputs.
  • The importance of UI/UX design in making AI outputs actually usable for non-technical stakeholders.

🔮 What’s Next for StellaCrop

  • Mobile App Integration 📱 → bringing StellaCrop directly into farmers’ hands with SMS/WhatsApp support.
  • Real-time Satellite Streams 🌍 → automating updates as new images arrive.
  • Expanded Crop Coverage 🌾 → training on multiple crops and regional variations.
  • Partnerships with Agriculture Boards 🏢 → to pilot StellaCrop in real-world farms.
  • Explainable AI Reports 📊 → so farmers and policymakers understand why a decision was made.

✨ StellaCrop shows how space-tech + AI + human-centered design can empower farmers, improve food security, and bring innovation from satellites all the way down to the soil.

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