Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8959782 | Computers and Electronics in Agriculture | 2018 | 8 Pages |
Abstract
Crop growth conditions and meteorological environments are observed and recorded by agro-meteorological stations, which, however, may fail to record crop yield data in some specific years. In this context, incomplete yield series data constrain their application and result in inconvenience in information mining. Accordingly, this study improves the existing spatio-temporal interpolation method and succeeds in interpolating wheat yield data observed and recorded by 56 agro-meteorological stations on the Huang-Huai-Hai Plain of China. In this study, pre-interpolation is first implemented to improve the completion rate of interpolation and eliminate the effect of absent neighboring values on the positions to be interpolated. Then, data reconstruction is performed in spatial and temporal dimensions based on spatio-temporal heterogeneity. Finally, the reconstructed results are combined with the back propagation (BP) neural network model for spatio-temporal integration. Moreover, this study analyzes the settings of key parameters and compares them with traditional interpolation methods. Corresponding results demonstrate the superiority of the proposed method in this study over traditional interpolation methods in terms of interpolation precision and the completion rate. Meanwhile, individual interpolation precision in each step of the proposed method is effectively enhanced.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Li Jiang, Xiaohu Zhang, Wenjun Zuo, Hao Xu, Jianqing Zhao, Xiaolei Qiu, Yongchao Tian, Yan Zhu,