Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production
Use of this method is still exploratory, and the paper explores different approaches that could be used. It is the first step towards a process of weather forecasting using machine learning and deep learning algorithms.
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| Format: | Informe técnico |
| Language: | Inglés |
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2019
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| Online Access: | https://hdl.handle.net/10568/122550 |
| _version_ | 1855525923838754816 |
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| author | CGIAR Research Program on Wheat |
| author_browse | CGIAR Research Program on Wheat |
| author_facet | CGIAR Research Program on Wheat |
| author_sort | CGIAR Research Program on Wheat |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Use of this method is still exploratory, and the paper explores different approaches that could be used. It is the first step towards a process of weather forecasting using machine learning and deep learning algorithms. |
| format | Informe técnico |
| id | CGSpace122550 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| record_format | dspace |
| spelling | CGSpace1225502023-03-14T11:58:50Z Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production CGIAR Research Program on Wheat production crop production drought remote sensing development rural development data forecasting learning systems weather weather forecasting agrifood systems machine learning algorithms approaches paper satellite Use of this method is still exploratory, and the paper explores different approaches that could be used. It is the first step towards a process of weather forecasting using machine learning and deep learning algorithms. 2019-12-31 2022-10-06T14:01:00Z 2022-10-06T14:01:00Z Report https://hdl.handle.net/10568/122550 en Open Access application/pdf CGIAR Research Program on Wheat. 2019. Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production. Reported in Wheat Annual Report 2019. Innovations. |
| spellingShingle | production crop production drought remote sensing development rural development data forecasting learning systems weather weather forecasting agrifood systems machine learning algorithms approaches paper satellite CGIAR Research Program on Wheat Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production |
| title | Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production |
| title_full | Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production |
| title_fullStr | Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production |
| title_full_unstemmed | Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production |
| title_short | Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production |
| title_sort | innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production |
| topic | production crop production drought remote sensing development rural development data forecasting learning systems weather weather forecasting agrifood systems machine learning algorithms approaches paper satellite |
| url | https://hdl.handle.net/10568/122550 |
| work_keys_str_mv | AT cgiarresearchprogramonwheat innovativeuseofsatellitedatafromremotesensingandsupervisedlearningtoreducetheimpactofdroughtoncropproduction |