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The weather phenomenon of frost poses great threats to agriculture. As recentfrost prediction methods are based on on-site historical data and sensors,extra development and deployment time are required for data collection in anynew site. The aim of this article is to eliminate the dependency on on-sitehistorical data and sensors for frost prediction methods. In this article, afrost prediction method based on spatial interpolation is proposed. The modelsuse climate data from existing weather stations, digital elevation modelssurveys, and normalized difference vegetation index data to estimate a targetsite's next hour minimum temperature. The proposed method utilizes ensemblelearning to increase the model accuracy. Climate datasets are obtained from 75weather stations across New South Wales and Australian Capital Territory areasof Australia. The results show that the proposed method reached a detectionrate up to 92.55%.

Intelligent Spatial Interpolation-based Frost Prediction Methodology using Artificial Neural Networks with Limited Local Data