  Corn yield prediction is beneficial as it provides valuable information about
production and prices prior the harvest. Publicly available high-quality corn
yield prediction can help address emergent information asymmetry problems and
in doing so improve price efficiency in futures markets. This paper is the
first to employ Long Short-Term Memory (LSTM), a special form of Recurrent
Neural Network (RNN) method to predict corn yields. A cross sectional time
series of county-level corn yield and hourly weather data made the sample space
large enough to use deep learning technics. LSTM is efficient in time series
prediction with complex inner relations, which makes it suitable for this task.
The empirical results from county level data in Iowa show promising predictive
power relative to existing survey based methods.
