Combining satellite data and artificial intelligence with a crop growth model to enhance rice yield estimation and crop management practices

IF 2.3 Q2 REMOTE SENSING Applied Geomatics Pub Date : 2024-07-09 DOI:10.1007/s12518-024-00575-6
Nguyen-Thanh Son, Chi-Farn Chen, Youg-Sin Cheng, Cheng-Ru Chen, Chien-Hui Syu, Yi-Ting Zhang, Shu-Ling Chen, Shih-Hsiang Chen
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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.

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将卫星数据和人工智能与作物生长模型相结合,加强水稻产量估算和作物管理实践
水稻是世界上一半以上人口的主食,尤其是在亚洲,至少有 5.2 亿人的热量供应中 50%以上来自水稻,其中大多数人要么极度贫困,要么十分贫穷。因此,水稻生产信息对于农业管理和制定粮食安全政策至关重要。本研究的目标是开发一种将遥感和人工智能技术与作物生长模型相结合的方法,以提高台湾的产量估算和作物管理水平。数据处理包括三个主要步骤:(1) 数据预处理以生成模型输入;(2) 利用人工智能粒子群优化(PSO)算法将源自卫星的叶面积指数(LAI)同化到作物生长模型中,从而建立作物产量模型;(3) 模型验证。同化过程使用基于遥感和模拟 LAI 值之差的成本函数。优化过程从初始参数化和适当调整模型输入参数开始。根据成本函数得出的适应度值由 PSO 确定。使用政府的产量统计数据对基于优化输入的作物生长模型所获得的产量估算结果进行了评估,结果表明这两个数据集之间非常接近。第一茬作物的均方根误差(RMSPE)和均值绝对误差(MAPE)分别为 19.8%和 17.1%,第二茬作物的均方根误差(RMSPE)和均值绝对误差(MAPE)分别为 8.4%和 6.3%。相对百分比误差 (RPE) 值分别为 18.5% 和 -5.1%,表明第一茬作物和第二茬作物的相对百分比误差略有高估和低估。
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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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