Progress in Research on Deep Learning-Based Crop Yield Prediction

被引:0
|
作者
Wang, Yuhan [1 ,2 ]
Zhang, Qian [2 ]
Yu, Feng [2 ]
Zhang, Na [1 ,3 ]
Zhang, Xining [2 ]
Li, Yuchen [1 ]
Wang, Ming [2 ]
Zhang, Jinmeng [2 ]
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
来源
AGRONOMY-BASEL | 2024年 / 14卷 / 10期
关键词
deep learning; prediction model; crop yield prediction; NEURAL-NETWORKS; AGRICULTURE; MODEL;
D O I
10.3390/agronomy14102264
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
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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页数:26
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