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🌍 About Bloomee

Bloomee is an interactive Earth-observation platform that detects, visualizes, and explains flowering events across space and time—putting the "where, when, and why" of blooms in one place. From tracking cherry blossoms in Japan to monitoring wildflower superblooms in California, Bloomee harnesses the power of NASA's Earth observation data to understand global flowering phenology.

🏆 Competition Achievement

Bloomee was built for the NASA Space Apps Challenge 2025 — the world's largest annual global hackathon, where thousands of teams across hundreds of cities use NASA's open Earth and space data to build solutions for real-world challenges over a single weekend.

Competing in the "BloomWatch: An Earth Observation Application for Global Flowering Phenology" challenge, Team Ganespace built Bloomee to harness NASA Earth observation data for a dynamic visual tool that displays and detects plant blooming events around the globe.

At the Bandung, Indonesia local event, Bloomee earned:

• 🥇 Winner — NASA Space Apps Challenge 2025, Bandung Chapter
• 🌟 Best Innovation Award — NASA Space Apps Challenge 2025, Bandung Chapter
• 🌍 Global Finalist Nominee — advanced to global nomination as a result of the Bandung Chapter win

✨ Key Features

• 🌐 Interactive 3D Globe: Explore blooming events across the planet with intuitive navigation
• ⏰ Time-Series Analysis: Scrub through seasons with temporal sliders to observe phenological changes
• 📊 Regional Detail Panel: Deep-dive into NDVI trends, species events, intensity, and weather correlations
• 🤖 Agentic AI-Powered Assistant: Natural-language queries with contextual recommendations
• 🔮 Predictive Modeling: Advanced ML forecasting with confidence intervals
• 📈 Multi-Model Ensemble: Automatic selection of best-performing algorithms per region

🛰️ Data Sources

Bloomee integrates multiple NASA Earth observation datasets:

• MODIS NDVI (16-day, 1 km resolution) - Vegetation index monitoring
• Landsat-9 - High-resolution NDVI detection and quality validation
• Land Surface Temperature - Environmental correlation analysis
• GPM IMERG Precipitation - Weather pattern integration

🧠 AI & Machine Learning

Under the hood, Bloomee evaluates multiple forecasting models per region:

• ARIMA & SARIMA - Time series analysis
• Prophet - Trend decomposition and seasonality
• LSTM Neural Networks - Deep learning patterns
• Gradient Boosting - Ensemble learning
• Random Forest - Feature importance analysis

The platform auto-selects the best-performing model and outputs bloom/NDVI forecasts with confidence intervals.

📸 Platform Screenshots

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🏗️ Architecture & Repositories

Bloomee is built as a modular ecosystem of specialized repositories:

On the fine-tuned models: they are self-hosting artifacts, not what the platform runs today. bloomee-chatbot defaults to ibm-granite/granite-4.1-8b — the teacher the corpus was distilled from — and the 3B fine-tune is a drop-in alternative for running the agent on your own hardware.

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🌐 Deployment

Live Applications

• 🌍 Web Platform: bloomee.earth
• 🤖 AI API: ai.bloomee.earth

📋 Resources

• 📊 Presentation Deck: cdn.bloomee.earth/deck.pdf
• 🎥 Demo Video: YouTube Presentation

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🚀 Getting Started

Quick Demo

Visit bloomee.earth to explore:

1. Navigate the 3D globe to find regions of interest
2. Use the time slider to observe seasonal changes
3. Click on bloom markers to open detailed analysis
4. Ask the AI assistant natural language questions about flowering patterns

API Access

The AI forecasting engine is available at ai.bloomee.earth with endpoints for:

• Regional NDVI prediction
• Multi-day bloom forecasting
• Model performance metrics

Local Development

Each repository contains detailed setup instructions:

• bloomee-web/README.md - Frontend development
• bloomee-ai/README.md - ML model deployment
• bloomee-chatbot/README.md - AI assistant configuration

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🎯 Impact & Applications

🌾 Agricultural Monitoring

• Crop flowering prediction for harvest optimization
• Pollinator activity correlation with bloom timing
• Disease management through phenological pattern analysis

🌿 Conservation Efforts

• Invasive species detection through anomalous bloom patterns
• Climate change impact assessment via long-term phenological shifts
• Biodiversity monitoring across protected ecosystems

🏙️ Urban Planning

• Allergen forecasting for public health initiatives
• Tourism optimization for bloom-dependent destinations
• Green infrastructure planning and assessment

🔬 Scientific Research

• Phenological research with automated data collection
• Climate correlation studies across multiple datasets
• Species distribution modeling enhancement

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🤝 Contributing

We welcome contributions to advance global flowering phenology research! Each repository has specific contribution guidelines:

• 🐛 Bug Reports: Submit issues with detailed reproduction steps
• 💡 Feature Requests: Propose enhancements with use-case descriptions
• 📊 Data Contributions: Help expand regional datasets and validation
• 🔬 Research Collaboration: Partner on phenological studies and publications

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📄 License

This project is part of the NASA Space Apps Challenge 2025. Please refer to individual repository licenses for specific terms.

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🌟 Acknowledgments

• NASA Earth Science Division for comprehensive Earth observation data
• NASA Space Apps Challenge for fostering innovation in space technology
• Google Earth Engine for powerful geospatial analysis capabilities
• Dify.ai for conversational AI infrastructure
• Open Source Community for foundational frameworks and libraries

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