Low temperature, fixed bed grain drying was investigated as an alternative to high temperature, fossil fuel-based drying. Drying data was collected in a full-scale on-farm storage bin drying system over three harvest seasons. A one-transient model of the grain drying process was developed, implemented, and evaluated against the three years of drying data. The drying model was shown to predict most aspects of the drying process with reasonable accuracy. A machine learning model was also trained against the same data. Preliminary calculations were completed to investigate the potential benefits of an air source heat pump as the low temperature heat source. Drying simulations were completed using typical meteorological year data. It was shown that low temperature grain drying could reduce greenhouse gas emissions by as much as 90% at a similar operating cost relative to high temperature drying fueled by natural gas or propane.