One-minute average data for solar radiation, air and soil temperatures, wind-speed, and relative humidity were recorded for two growing seasons (May to November of 2015 and 2016) in a screened and unscreened high tunnel. A one-dimensional 10-layer lumped capacitance model was created using conduction, convection, and radiation equations to model the energy transfer between the different layers to predict air and soil temperatures inside the high tunnel for select time periods. A sensitivity study was conducted and results were analyzed based the magnitude of the errors. The lumped capacitance model error ranged from 0˚C – 9.7˚C for predicted high resolution air and soil temperatures inside the unscreened high tunnel with sides closed. The data was also used in an Artificial Neural Network (ANN) to predict high tunnel air and soil temperatures. The ANN errors were between 0.1 ˚C – 2.0 ˚C for both screened and unscreened tunnels for both growing seasons.