UAV Sensing of Rice for Precision Nitrogen Monitoring
Progression in the technological facets such as, remote sensing, machine learning, big data analytics, unmanned aerial vehicles (UAV’s) etc. have unraveled a new domain of sensor-based, non-invasive, fast, and near-real time assessment of crop conditions for efficient site-specific management. Prese...
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| Format: | Abstract |
| Language: | Inglés |
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International Rice Research Institute
2023
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| Online Access: | https://hdl.handle.net/10568/138601 |
| _version_ | 1855530235009695744 |
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| author | Gakhar, Shalini Sahoo, Rabi N. Rejith, R.G. Kondraju, Tarun Teja Ranjan, Rajeev |
| author_browse | Gakhar, Shalini Kondraju, Tarun Teja Ranjan, Rajeev Rejith, R.G. Sahoo, Rabi N. |
| author_facet | Gakhar, Shalini Sahoo, Rabi N. Rejith, R.G. Kondraju, Tarun Teja Ranjan, Rajeev |
| author_sort | Gakhar, Shalini |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Progression in the technological facets such as, remote sensing, machine learning, big data analytics, unmanned aerial vehicles (UAV’s) etc. have unraveled a new domain of sensor-based, non-invasive, fast, and near-real time assessment of crop conditions for efficient site-specific management. Present study attempts to map plant nitrogen (N) for rice crop in spatial scale in the experimental farm of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India using unmanned aerial vehicle (UAV) integrated with multispectral sensor. Five different treatments of nitrogen (No-N4 corresponding to 0, 50, 100, 150, and 200 kg N ha-1) were imposed to generate the variation in plant N content in the rice field. Four customary machine learning algorithms, namely, artificial neural network, support vector machines, random forest and multivariate adaptive regression splines regression ha red edge technology. The approach developed can be upscaled to the ve been implemented to retrieve N content. Evaluation of different machine learning algorithms led to development of optimized workflow for concurrent estimation of N content with reasonably good accuracy with the multispectral sensor using red edge technology. The approach developed can be upscaled to the farmer’s field to ensure feasible solutions for the prevailing challenges. |
| format | Abstract |
| id | CGSpace138601 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | International Rice Research Institute |
| publisherStr | International Rice Research Institute |
| record_format | dspace |
| spelling | CGSpace1386012025-12-08T09:54:28Z UAV Sensing of Rice for Precision Nitrogen Monitoring Gakhar, Shalini Sahoo, Rabi N. Rejith, R.G. Kondraju, Tarun Teja Ranjan, Rajeev rice nitrogen remote sensing Progression in the technological facets such as, remote sensing, machine learning, big data analytics, unmanned aerial vehicles (UAV’s) etc. have unraveled a new domain of sensor-based, non-invasive, fast, and near-real time assessment of crop conditions for efficient site-specific management. Present study attempts to map plant nitrogen (N) for rice crop in spatial scale in the experimental farm of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India using unmanned aerial vehicle (UAV) integrated with multispectral sensor. Five different treatments of nitrogen (No-N4 corresponding to 0, 50, 100, 150, and 200 kg N ha-1) were imposed to generate the variation in plant N content in the rice field. Four customary machine learning algorithms, namely, artificial neural network, support vector machines, random forest and multivariate adaptive regression splines regression ha red edge technology. The approach developed can be upscaled to the ve been implemented to retrieve N content. Evaluation of different machine learning algorithms led to development of optimized workflow for concurrent estimation of N content with reasonably good accuracy with the multispectral sensor using red edge technology. The approach developed can be upscaled to the farmer’s field to ensure feasible solutions for the prevailing challenges. 2023-10-16 2024-01-26T14:53:16Z 2024-01-26T14:53:16Z Abstract https://hdl.handle.net/10568/138601 en Open Access application/pdf International Rice Research Institute Shalini Gakhar, et. al. (2023). UAV Sensing of Rice for Precision Nitrogen Monitoring. Poster. Poster. 6th International Rice Congress, October 2023. Los banos: International Rice Research Institute. |
| spellingShingle | rice nitrogen remote sensing Gakhar, Shalini Sahoo, Rabi N. Rejith, R.G. Kondraju, Tarun Teja Ranjan, Rajeev UAV Sensing of Rice for Precision Nitrogen Monitoring |
| title | UAV Sensing of Rice for Precision Nitrogen Monitoring |
| title_full | UAV Sensing of Rice for Precision Nitrogen Monitoring |
| title_fullStr | UAV Sensing of Rice for Precision Nitrogen Monitoring |
| title_full_unstemmed | UAV Sensing of Rice for Precision Nitrogen Monitoring |
| title_short | UAV Sensing of Rice for Precision Nitrogen Monitoring |
| title_sort | uav sensing of rice for precision nitrogen monitoring |
| topic | rice nitrogen remote sensing |
| url | https://hdl.handle.net/10568/138601 |
| work_keys_str_mv | AT gakharshalini uavsensingofriceforprecisionnitrogenmonitoring AT sahoorabin uavsensingofriceforprecisionnitrogenmonitoring AT rejithrg uavsensingofriceforprecisionnitrogenmonitoring AT kondrajutarunteja uavsensingofriceforprecisionnitrogenmonitoring AT ranjanrajeev uavsensingofriceforprecisionnitrogenmonitoring |