Frost prediction using Artificial Neural Networks: a classification approach

Air temperatures below freezing can damage plants. Irrigation is the most widely practiced frost protection measure. However, growers need information about when to start irrigating, as the process has to be commenced prior to the temperature dropping below freezing. The goal of this study was to develop Artificial Neural Networks (ANNs) to predict if frost would occur during the near future. A classification approach to develop the ANNs was used. This would require a method to predict frosts, but a model for frost prediction would typically require access to local weather. Many locations that could potentially benefit from frost prediction do not have historical weather data, or even a local weather station. An additional goal was to develop ANNs to predict frost for any given location in the state of Georgia. ANNs were developed using weather data from multiple locations and were evaluated for other locations.