Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process

Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process

Accurate and timely prediction of crop yield based on remote sensing data is important for food security. However, crop growth is a complex process, which makes it quite difficult to achieve better performance. To address this problem, a novel 3-D convolutional neural multikernel network is proposed...

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Bibliographic Details
Main Authors: Mengjia Qiao, Xiaohui He, Xijie Cheng, Panle Li, Haotian Luo, Zhihui Tian, Hengliang Guo
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Crop yield
multikernel learning (MKL)
spatial consistency
3-D convolutional neural network (CNN)
Online Access:https://ieeexplore.ieee.org/document/9404810/

Internet

https://ieeexplore.ieee.org/document/9404810/

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