文献类型： 外文期刊
作者： Yang, Feng 1 ; Zhang, Dongfeng 1 ; Zhang, Yuqing 2 ; Zhang, Yong 2 ; Han, Yanyun 1 ; Zhang, Qiusi 1 ; Zhang, Qi 1 ; Zhang, Chenghui 5 ; Liu, Zhongqiang 1 ; Wang, Kaiyi 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China
2.Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
4.Minist Agr, Key Lab Agriinformat, Beijing, Peoples R China
5.Tianjin Agr Univ, Coll Hort & Landscape, Tianjin, Peoples R China
关键词： Corn variety yield prediction; Graph neural network; Data imputation; Adversarial learning
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）
ISSN： 0168-1699
年卷期： 2023 年 211 卷
页码：
收录情况： SCI
摘要： The crop variety yield prediction is widely used to select new varieties and select suitable planting areas for them, but it still suffers from multiple grand challenges, including sparse data and complex interaction mechanisms between the variety and different environments. Moreover, accurately predicting the yield of corn varieties often faces the problem of missing trait data, making it even more difficult. This study reports a corn variety yield prediction model based on generative adversarial network and graph attention network (YPM-GANGAT) to fill in the missing trait attributes and achieve corn variety yield prediction by establishing spatial and temporal correlations for multi-environment testing samples. Specifically, we first construct a variety spatiotemporal graph to utilize the structural features of the graph to effectively and fully express information such as relationships between different corn varieties planting data from different locations. Then, we make a sharedlatent space assumption between the structural features of the graph and the attribute features of each node in the graph. This helps in developing a distribution matching based on graph neural network (GNN) and adversarial strategy to generate new data to fill in the missing attributes. Finally, we predict the corn variety yield based on the filled node attributes and two different types of GNN models. The experimental results show that the proposed missing trait imputation method outperforms the other five common data imputation methods, and the proposed GNN-based corn yield prediction model has better performance as compared to the other five machine learning-based models and two deep learning-based models. This study demonstrates a novel solution for improving the accuracy of large-scale corn variety yield prediction.
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