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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9404810/ |
| _version_ | 1818739104068665344 |
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| author | Mengjia Qiao Xiaohui He Xijie Cheng Panle Li Haotian Luo Zhihui Tian Hengliang Guo |
| author_facet | Mengjia Qiao Xiaohui He Xijie Cheng Panle Li Haotian Luo Zhihui Tian Hengliang Guo |
| author_sort | Mengjia Qiao |
| collection | DOAJ |
| description | 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 to capture hierarchical features for predicting crop yield. First, a full 3-D convolutional neural network is constructed to maximally explore deep spatial–spectral features from multispectral images. Then, a multikernel learning (MKL) approach is proposed for fusion of intraimage deep spatial–spectral features and intersample spatial consistency features. Specifically, we assign a group of nonlinear kernels for each feature in the MKL framework, which provides a robust way to fit features extracted from different domains. Finally, the probability distribution of prediction results is obtained by a kernel-based method. We evaluate the performance of the proposed method on China wheat yield prediction and offer detailed and systematic analyses of the performance of the proposed method. In addition, our method is compared with several competing methods. Experimental results demonstrate that the proposed method has certain advantages and can provide better prediction performance than the competitive methods. |
| first_indexed | 2024-12-18T01:19:31Z |
| format | Article |
| id | doaj.art-94dfa733de9f4df689211d58825c7320 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-18T01:19:31Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-94dfa733de9f4df689211d58825c73202022-12-21T21:25:52ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01144476448910.1109/JSTARS.2021.30731499404810Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian ProcessMengjia Qiao0https://orcid.org/0000-0001-8919-8647Xiaohui He1https://orcid.org/0000-0001-6694-0183Xijie Cheng2https://orcid.org/0000-0001-9963-4422Panle Li3https://orcid.org/0000-0002-1077-8831Haotian Luo4Zhihui Tian5Hengliang Guo6School of Information Engineering, Zhengzhou University, Zhengzhou, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaSchool of Information Engineering, Zhengzhou University, Zhengzhou, ChinaSchool of Information Engineering, Zhengzhou University, Zhengzhou, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaSchool of Geoscience and Technology, Zhengzhou University, Zhengzhou, ChinaAccurate 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 to capture hierarchical features for predicting crop yield. First, a full 3-D convolutional neural network is constructed to maximally explore deep spatial–spectral features from multispectral images. Then, a multikernel learning (MKL) approach is proposed for fusion of intraimage deep spatial–spectral features and intersample spatial consistency features. Specifically, we assign a group of nonlinear kernels for each feature in the MKL framework, which provides a robust way to fit features extracted from different domains. Finally, the probability distribution of prediction results is obtained by a kernel-based method. We evaluate the performance of the proposed method on China wheat yield prediction and offer detailed and systematic analyses of the performance of the proposed method. In addition, our method is compared with several competing methods. Experimental results demonstrate that the proposed method has certain advantages and can provide better prediction performance than the competitive methods.https://ieeexplore.ieee.org/document/9404810/Crop yieldmultikernel learning (MKL)spatial consistency3-D convolutional neural network (CNN) |
| spellingShingle | Mengjia Qiao Xiaohui He Xijie Cheng Panle Li Haotian Luo Zhihui Tian Hengliang Guo Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Crop yield multikernel learning (MKL) spatial consistency 3-D convolutional neural network (CNN) |
| title | Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process |
| title_full | Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process |
| title_fullStr | Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process |
| title_full_unstemmed | Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process |
| title_short | Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process |
| title_sort | exploiting hierarchical features for crop yield prediction based on 3 d convolutional neural networks and multikernel gaussian process |
| topic | Crop yield multikernel learning (MKL) spatial consistency 3-D convolutional neural network (CNN) |
| url | https://ieeexplore.ieee.org/document/9404810/ |
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