orangeshield-ai

orangeshield-ai / orangeshield

Public

AI-powered weather prediction for orange crop supply disruptions. Predicts USDA damage reports 48-72 hours early using Claude Sonnet 4, NOAA data, and satellite imagery. 70% accuracy, 3-year backtest.

14
1
100% credibility
Found Feb 04, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

OrangeShield AI monitors weather patterns to predict damage to Florida's orange crops from events like freezes and hurricanes, delivering forecasts 48-72 hours before USDA reports.

How It Works

1
🔍 Discover OrangeShield

You hear about this helpful tool that watches Florida weather to predict orange crop troubles days before official news arrives.

2
🛠️ Set up your watch station

You easily prepare it by connecting free weather updates and a smart thinking helper so it can analyze risks.

3
⚡ Run your first prediction

With one simple start, it grabs the latest weather picture and creates its first forecast of possible crop damage, feeling exciting as results appear instantly.

4
▶️ Start round-the-clock monitoring

You switch it to automatic mode to quietly check key orange-growing areas every 15 minutes without you lifting a finger.

5
🔔 Receive smart alerts

When serious threats like freezes or storms show up, clear messages arrive telling you the expected damage and how sure it is.

🎉 Make better decisions ahead

Your early warnings prove accurate against real reports, helping you spot supply shortages and stay one step ahead in the market.

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AI-Generated Review

What is orangeshield?

OrangeShield is a Python-based AI powered weather forecasting system that predicts orange crop damage in Florida 48-72 hours before USDA reports, using NOAA forecasts, satellite imagery, and Claude Sonnet 4 for analysis. It monitors freezes, hurricanes, and disease risks every 15 minutes, delivering probabilistic damage estimates with 70% backtested accuracy over three years. Users get CLI commands like `python main.py continuous` for real-time alerts, performance reports, and verification against USDA data.

Why is it gaining traction?

Unlike generic ai powered weather apps, it targets commodity supply chains with actionable signals for orange juice futures, blending free NOAA data with optional paid satellite feeds for precise ag forecasts. The backtest script proves real-world edge, and setup is straightforward with Postgres integration and optional FastAPI endpoints for predictions. Developers dig the niche focus amid broader ai powered projects github noise.

Who should use this?

Commodity traders tracking OJ futures, agribusiness risk managers hedging Florida citrus exposure, or quant devs building ai powered weather models for supply disruption trading. Ideal for those needing early USDA predictors without custom data pipelines.

Verdict

Worth forking for OJ traders—solid docs, CLI, and 70% backtest make it immediately useful despite 43 stars and 1.0% credibility score signaling early-stage risks. Monitor for maintenance as it matures.

(187 words)

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