AI-Based Paddy Rice Yield Prediction Using Satellite Images, Meteorological Data, and Digital Elevation Model: Case Study of South Korea, 2000–2023
Year: 2024
Type: article
Abstract: The increasing uncertainty in crop production caused by climate change underscores the necessity of accurate yield predictions for staple crops like rice.This study developed a rice yield prediction model that accounts for climate change by integrating satellite imagery and artificial intelligence.Key variables, including temperature, precipitation, and solar radiation, were derived from Korea Meteorological Administration Automated Synoptic Observing System (ASOS) data and combined with NASA's Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) to incorporate topographic factors.A comprehensive database integrating vegetation indices from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) was established, and Automated Machine Learning (AutoML) was employed to optimize the rice yield prediction model.Validation results demonstrated high predictive accuracy, with the model utilizing ASOS data achieving a MAPE of 4.091% in 2023.This study contributes to data-driven decision-making for climate-resilient agricultural policies and enhanced food security.
Source: 대한원격탐사학회지
Authors: Jaeung Sim, Jaeil Cho, Kyungdo Lee, Yangwon Lee
Institutions: Chonnam National University, Pukyong National University
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406077880