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

TL;DR

This paper introduces a frost prediction method that leverages spatial interpolation and ensemble learning to accurately forecast frost risk without relying on local historical data, using data from existing weather stations.

Contribution

It presents a novel frost prediction approach that eliminates the need for on-site historical data by integrating spatial interpolation and ensemble learning techniques.

Findings

Detection rate up to 92.55%

Effective use of existing weather station data

Reduces need for on-site data collection

Abstract

The weather phenomenon of frost poses great threats to agriculture. As recent frost prediction methods are based on on-site historical data and sensors, extra development and deployment time are required for data collection in any new site. The aim of this article is to eliminate the dependency on on-site historical data and sensors for frost prediction methods. In this article, a frost prediction method based on spatial interpolation is proposed. The models use climate data from existing weather stations, digital elevation models surveys, and normalized difference vegetation index data to estimate a target site's next hour minimum temperature. The proposed method utilizes ensemble learning to increase the model accuracy. Climate datasets are obtained from 75 weather stations across New South Wales and Australian Capital Territory areas of Australia. The results show that the proposed method reached a detection rate up to 92.55%.