文献类型： 外文期刊
作者： Wang, Yuhan 1 ; Zhang, Qian 2 ; Yu, Feng 2 ; Zhang, Na 1 ; Zhang, Xining 2 ; Li, Yuchen 1 ; Wang, Ming 2 ; Zhang, Jinmeng 2 ;
作者机构： 1.Beijing Agr Univ, Coll Intelligent Sci & Engn, Beijing 102206, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Inst Data Sci & Agr Econ, Beijing 102206, Peoples R China
3.Beijing Rural Remote Informat Serv Engn Technol Re, Beijing 102206, Peoples R China
关键词： deep learning; prediction model; crop yield prediction
期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）
ISSN：
年卷期： 2024 年 14 卷 10 期
页码：
收录情况： SCI
摘要： In recent years, crop yield prediction has become a research hotspot in the field of agricultural science, playing a decisive role in the economic development of every country. Therefore, accurate and timely prediction of crop yields is of great significance for the national formulation of relevant economic policies and provides a reasonable basis for agricultural decision-making. The results obtained through prediction can selectively observe the impact of factors such as crop growth cycles, soil changes, and rainfall distribution on crop yields, which is crucial for predicting crop yields. Although traditional machine learning methods can obtain an estimated crop yield value and to some extent reflect the current growth status of crops, their prediction accuracy is relatively low, with significant deviations from actual yields, and they fail to achieve satisfactory results. To address these issues, after in-depth research on the development and current status of crop yield prediction, and a comparative analysis of the advantages and problems of domestic and foreign yield prediction algorithms, this paper summarizes the methods of crop yield prediction based on deep learning. This includes analyzing and summarizing existing major prediction models, analyzing prediction methods for different crops, and finally providing relevant views and suggestions on the future development direction of applying deep learning to crop yield prediction research.
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