Started with a question: can you predict whether OJ futures will go up or down before the harvest, using only satellite data? Built a full data pipeline pulling NASA MODIS MOD13Q1 satellite imagery (250m resolution) over the Florida citrus belt every January through March via Google Earth Engine. The core signal is NDVI surprise, how much greener or browner the groves are compared to their 3-year rolling average. Brown groves = stressed trees = lower yield = price goes up.Added five more data sources on top: USDA NASS for Florida orange production and bearing acreage, ICE OJ futures prices via yfinance, NOAA GHCN-Daily for hard freeze days and rainfall, and USDA FAS for Brazil production as a global supply check.Built a Streamlit dashboard with 6 tabs: Overview, NDVI Trend, Yield vs Price, Backtest, Signal, and Glossary. The Backtest tab lets you adjust a threshold slider and see cumulative P&L, hit rate, and a 3-tier signal comparison (NDVI only, plus Brazil confirmation, plus drought confirmation).Backtested across 2005 to 2025: 62% directional accuracy (13/21 years correct). Most commodity price prediction models rely on historical price patterns or economic indicators and typically land around 52 to 54% directional accuracy on out-of-sample data. Starvest gets its edge from reading the physical world directly via satellite before the supply shock shows up in any price data. The signal is issued in March, months before the market fully prices in the harvest outcome.The 2026 forward signal is LONG. Hard freeze in January (24.1°F, 2 days) plus drought stress pushed NDVI surprise to -0.0423. Outcome resolves in September.