A deep learning crop model for adaptive yield estimation in large areas
Estimating crop yield in large areas is essential for ensuring food security and sustainable development. Accounting for variations in the temporal cumulative growth of crops across regions (i.e., spatial heterogeneity of crop growth) can improve the accuracy of yield estimation in large areas. Howe...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222000309 |
| _version_ | 1811192834999451648 |
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| author | Yilin Zhu Sensen Wu Mengjiao Qin Zhiyi Fu Yi Gao Yuanyuan Wang Zhenhong Du |
| author_facet | Yilin Zhu Sensen Wu Mengjiao Qin Zhiyi Fu Yi Gao Yuanyuan Wang Zhenhong Du |
| author_sort | Yilin Zhu |
| collection | DOAJ |
| description | Estimating crop yield in large areas is essential for ensuring food security and sustainable development. Accounting for variations in the temporal cumulative growth of crops across regions (i.e., spatial heterogeneity of crop growth) can improve the accuracy of yield estimation in large areas. However, current spatial heterogeneity learning methods have limitations such as cutting off inherent correlations among regions, difficulty obtaining accurate prior knowledge, and high subjectivity. Therefore, this study proposed a novel deep learning adaptive crop model (DACM) to accomplish adaptive high-precision yield estimation in large areas, which emphasizes adaptive learning of the spatial heterogeneity of crop growth based on fully extracting crop growth information. Results showed that the DACM achieved an average root mean squared error (RMSE) of 4.406 bushels·acre−1 (296.304 kg ha−1), with an average coefficient of determination (R2) of 0.805. Compared with other state-of-the-art machine learning and deep learning methods, DACM improves the large-area yield estimation accuracy and performs more robustly in space. The analyses on attention values and estimation stability demonstrate that DACM can learn the spatial heterogeneity of crop growth and adopt adaptive strategies to optimize yield estimation. Considering both performance stability and interpretability, DACM provides a practical approach for estimating large-area crop yields by adaptively learning the spatial heterogeneity patterns of crop growth. |
| first_indexed | 2024-04-11T23:58:50Z |
| format | Article |
| id | doaj.art-c298f444bf91485db93288c7e01be5fe |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-11T23:58:50Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-c298f444bf91485db93288c7e01be5fe2022-12-22T03:56:17ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-06-01110102828A deep learning crop model for adaptive yield estimation in large areasYilin Zhu0Sensen Wu1Mengjiao Qin2Zhiyi Fu3Yi Gao4Yuanyuan Wang5Zhenhong Du6School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, ChinaSchool of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, ChinaSchool of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China; Ocean Academy, Zhejiang University, 1 Zheda Road, Zhoushan 316021, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China; Corresponding author at: School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China. Ocean Academy, Zhejiang University, 1 Zheda Road, Zhoushan 316021, China.School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, ChinaEstimating crop yield in large areas is essential for ensuring food security and sustainable development. Accounting for variations in the temporal cumulative growth of crops across regions (i.e., spatial heterogeneity of crop growth) can improve the accuracy of yield estimation in large areas. However, current spatial heterogeneity learning methods have limitations such as cutting off inherent correlations among regions, difficulty obtaining accurate prior knowledge, and high subjectivity. Therefore, this study proposed a novel deep learning adaptive crop model (DACM) to accomplish adaptive high-precision yield estimation in large areas, which emphasizes adaptive learning of the spatial heterogeneity of crop growth based on fully extracting crop growth information. Results showed that the DACM achieved an average root mean squared error (RMSE) of 4.406 bushels·acre−1 (296.304 kg ha−1), with an average coefficient of determination (R2) of 0.805. Compared with other state-of-the-art machine learning and deep learning methods, DACM improves the large-area yield estimation accuracy and performs more robustly in space. The analyses on attention values and estimation stability demonstrate that DACM can learn the spatial heterogeneity of crop growth and adopt adaptive strategies to optimize yield estimation. Considering both performance stability and interpretability, DACM provides a practical approach for estimating large-area crop yields by adaptively learning the spatial heterogeneity patterns of crop growth.http://www.sciencedirect.com/science/article/pii/S1569843222000309Adaptive yield estimationLarge areasSpatial heterogeneityCrop growth |
| spellingShingle | Yilin Zhu Sensen Wu Mengjiao Qin Zhiyi Fu Yi Gao Yuanyuan Wang Zhenhong Du A deep learning crop model for adaptive yield estimation in large areas International Journal of Applied Earth Observations and Geoinformation Adaptive yield estimation Large areas Spatial heterogeneity Crop growth |
| title | A deep learning crop model for adaptive yield estimation in large areas |
| title_full | A deep learning crop model for adaptive yield estimation in large areas |
| title_fullStr | A deep learning crop model for adaptive yield estimation in large areas |
| title_full_unstemmed | A deep learning crop model for adaptive yield estimation in large areas |
| title_short | A deep learning crop model for adaptive yield estimation in large areas |
| title_sort | deep learning crop model for adaptive yield estimation in large areas |
| topic | Adaptive yield estimation Large areas Spatial heterogeneity Crop growth |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222000309 |
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