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Browse files# VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting
VITA is a variational pretraining framework that learns weather representations from rich satellite data and transfers them to yield prediction tasks with limited ground-based measurements. This is the official pretrained model. Code: https://github.com/Neehan/VITA
**Paper**: [arXiv:2508.03589](https://arxiv.org/abs/2508.03589) | **AAAI 2026**
## Overview
VITA addresses the data asymmetry problem in agricultural AI: pretraining uses 31 meteorological variables from NASA POWER satellite data, while deployment relies on only 6 basic weather features. Through variational pretraining with a seasonality-aware sinusoidal prior, VITA achieves state-of-the-art performance in predicting corn and soybean yields across 763 U.S. Corn Belt counties, particularly during extreme weather years.