High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery
This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approac...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/100348 |
| _version_ | 1855543298363490304 |
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| author | Devia, Carlos Andres Rojas, Juan P. Petro Páez, Eliel Enrique Martínez, Carol Mondragon, Iván Fernando Patino, D. Rebolledo, María Camila Colorado, J. |
| author_browse | Colorado, J. Devia, Carlos Andres Martínez, Carol Mondragon, Iván Fernando Patino, D. Petro Páez, Eliel Enrique Rebolledo, María Camila Rojas, Juan P. |
| author_facet | Devia, Carlos Andres Rojas, Juan P. Petro Páez, Eliel Enrique Martínez, Carol Mondragon, Iván Fernando Patino, D. Rebolledo, María Camila Colorado, J. |
| author_sort | Devia, Carlos Andres |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approach calculates 7 vegetation indices that are combined in the form of multivariable regressions depending on the stage of rice growth: vegetative, reproductive or ripening. We model the relationship of these vegetation indices to estimate the biomass of a certain crop area. The methods are calibrated by using a minimum sampling area of 1 linear meter of the crop. Comprehensive experimental tests have been carried out over two different rice varieties under upland and lowland rice production systems. Results show that the proposed approach is able to estimate the biomass of large areas of the crop with an average correlation of 0.76 compared with the traditional manual destructive method. To our knowledge, this is the first work that uses a small sampling area of 1 linear meter to calibrate and validate NIR image-based estimations of biomass in rice crops. |
| format | Journal Article |
| id | CGSpace100348 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1003482025-11-12T06:00:23Z High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery Devia, Carlos Andres Rojas, Juan P. Petro Páez, Eliel Enrique Martínez, Carol Mondragon, Iván Fernando Patino, D. Rebolledo, María Camila Colorado, J. rice phaseolus vulgaris vegetation biomass This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approach calculates 7 vegetation indices that are combined in the form of multivariable regressions depending on the stage of rice growth: vegetative, reproductive or ripening. We model the relationship of these vegetation indices to estimate the biomass of a certain crop area. The methods are calibrated by using a minimum sampling area of 1 linear meter of the crop. Comprehensive experimental tests have been carried out over two different rice varieties under upland and lowland rice production systems. Results show that the proposed approach is able to estimate the biomass of large areas of the crop with an average correlation of 0.76 compared with the traditional manual destructive method. To our knowledge, this is the first work that uses a small sampling area of 1 linear meter to calibrate and validate NIR image-based estimations of biomass in rice crops. 2019-12 2019-03-19T15:00:14Z 2019-03-19T15:00:14Z Journal Article https://hdl.handle.net/10568/100348 en Open Access application/pdf Springer Devia, Carlos A.; Rojas, Juan P.; Petro, Eliel; Martinez, Carol; Mondragon, Ivan F.; Patino, D.; Rebolledo, Maria Camila & Colorado, J. (2019). High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery, Journal of Intelligent & Robotic Systems. 1-17 p. |
| spellingShingle | rice phaseolus vulgaris vegetation biomass Devia, Carlos Andres Rojas, Juan P. Petro Páez, Eliel Enrique Martínez, Carol Mondragon, Iván Fernando Patino, D. Rebolledo, María Camila Colorado, J. High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery |
| title | High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery |
| title_full | High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery |
| title_fullStr | High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery |
| title_full_unstemmed | High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery |
| title_short | High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery |
| title_sort | high throughput biomass estimation in rice crops using uav multispectral imagery |
| topic | rice phaseolus vulgaris vegetation biomass |
| url | https://hdl.handle.net/10568/100348 |
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