An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery
Proof of concept delivered. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks (NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri).
| Autor principal: | |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/122306 |
| _version_ | 1855525241118261248 |
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| author | CGIAR Research Program on Rice |
| author_browse | CGIAR Research Program on Rice |
| author_facet | CGIAR Research Program on Rice |
| author_sort | CGIAR Research Program on Rice |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Proof of concept delivered. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks (NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri). |
| format | Informe técnico |
| id | CGSpace122306 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| record_format | dspace |
| spelling | CGSpace1223062023-03-14T11:57:34Z An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery CGIAR Research Program on Rice rice crops technology development rural development methods learning networks systems agrifood systems machine learning imagery ripening multispectral imagery neural networks vectors Proof of concept delivered. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks (NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri). 2020-12-31 2022-10-06T13:56:04Z 2022-10-06T13:56:04Z Report https://hdl.handle.net/10568/122306 en Open Access application/pdf CGIAR Research Program on Rice. 2020. An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery. Reported in Rice Annual Report 2020. Innovations. |
| spellingShingle | rice crops technology development rural development methods learning networks systems agrifood systems machine learning imagery ripening multispectral imagery neural networks vectors CGIAR Research Program on Rice An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery |
| title | An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery |
| title_full | An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery |
| title_fullStr | An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery |
| title_full_unstemmed | An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery |
| title_short | An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery |
| title_sort | unmanned aerial vehicle uav technology for estimating leaf n content in rice crops from multispectral imagery |
| topic | rice crops technology development rural development methods learning networks systems agrifood systems machine learning imagery ripening multispectral imagery neural networks vectors |
| url | https://hdl.handle.net/10568/122306 |
| work_keys_str_mv | AT cgiarresearchprogramonrice anunmannedaerialvehicleuavtechnologyforestimatingleafncontentinricecropsfrommultispectralimagery AT cgiarresearchprogramonrice unmannedaerialvehicleuavtechnologyforestimatingleafncontentinricecropsfrommultispectralimagery |