Combining satellite data and artificial intelligence with a crop growth model to enhance rice yield estimation and crop management practices
Journal
Applied Geomatics
Journal Volume
16
Journal Issue
3
Start Page
639
End Page
654
ISSN
1866-9298
Date Issued
2024-09
Author(s)
Nguyen-Thanh Son
Chi-Farn Chen
Youg-Sin Cheng
Cheng-Ru Chen
農業部農業試驗所
農業部農業試驗所
Shu-Ling Chen
Shih-Hsiang Chen
Abstract
Rice is the staple food of more than half of the world’s population, especially in Asia, where rice provides more than 50% of the caloric supply for at least 520 million people, most of them are either extremely impoverished or poor. Information on rice production is thus essential for agricultural management and the formulation of food security policies. The objective of this research is to develop an approach combining remote sensing and artificial intelligence (AI) techniques with a crop growth model for enhancing yield estimation and crop management in Taiwan. The data processing involves three main steps: (1) data pre-processing to generate model inputs, (2) crop yield modeling through assimilating satellite-derived leaf area index (LAI) into a crop growth model using the AI particle swarm optimization (PSO) algorithm, and (3) model validation. The assimilation process was performed using a cost function based on the difference between remotely-sensed and simulated LAI values. The optimization process began with an initial parameterization and appropriately adjusted input parameters in the model. The fitness value derived from a cost function was determined using the PSO. The results of yield estimates obtained from the crop growth model based on optimized inputs were evaluated using the government’s yield statistics, revealing close agreement between these two datasets. The root mean square percentage error (RMSPE) and the mean absolute percentage error (MAPE) for the first crop were 19.8% and 17.1%, and the values for the second crop were 8.4% and 6.3%, respectively. The relative percentage error (RPE) values of 18.5% and − 5.1%, respectively, showed a slight overestimate and underestimate for the first and second crops.
Subjects
Artificial intelligence
Crop growth model
Remote sensing
Rice yield
Publisher
Springer
Type
journal article