Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa
This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 - 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ra...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Copernicus GmbH
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/136075 |
| _version_ | 1855521748307410944 |
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| author | Mbulisi Sibanda Buthelezi, S. Mutanga, O. Odindi, J. Clulow, A.D. Chimonyo, Vimbayi Grace Petrova Gokool, S. Naiken, V. Magidi, J. Mabhaudhi, Tafadzwanashe |
| author_browse | Buthelezi, S. Chimonyo, Vimbayi Grace Petrova Clulow, A.D. Gokool, S. Mabhaudhi, Tafadzwanashe Magidi, J. Mbulisi Sibanda Mutanga, O. Naiken, V. Odindi, J. |
| author_facet | Mbulisi Sibanda Buthelezi, S. Mutanga, O. Odindi, J. Clulow, A.D. Chimonyo, Vimbayi Grace Petrova Gokool, S. Naiken, V. Magidi, J. Mabhaudhi, Tafadzwanashe |
| author_sort | Mbulisi Sibanda |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 - 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 = 0.85, RMSE = 0.1, RRMSE = 5.08%) and proportional yield (R2 = 0.92, RMSE = 0.06, RRMSE = 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms – a previously challenging task with coarse spatial resolution satellite sensors. |
| format | Journal Article |
| id | CGSpace136075 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Copernicus GmbH |
| publisherStr | Copernicus GmbH |
| record_format | dspace |
| spelling | CGSpace1360752025-10-26T12:51:33Z Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa Mbulisi Sibanda Buthelezi, S. Mutanga, O. Odindi, J. Clulow, A.D. Chimonyo, Vimbayi Grace Petrova Gokool, S. Naiken, V. Magidi, J. Mabhaudhi, Tafadzwanashe agricultural productivity maize unmanned aerial vehicles remote sensing smallholders This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 - 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 = 0.85, RMSE = 0.1, RRMSE = 5.08%) and proportional yield (R2 = 0.92, RMSE = 0.06, RRMSE = 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms – a previously challenging task with coarse spatial resolution satellite sensors. 2023-12-13 2023-12-31T23:49:06Z 2023-12-31T23:49:06Z Journal Article https://hdl.handle.net/10568/136075 en Open Access Copernicus GmbH Sibanda, M., Buthelezi, S., Mutanga, O., Odindi, J., Clulow, A. D., Chimonyo, V. G. P., Gokool, S., Naiken, V., Magidi, J., and Mabhaudhi, T. 2023. Exploring the prospects of UAV-remotely sensed data in estimating productivity of maize crops in typical smallholder farms of Southern Africa, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1/W1-2023, 1143–1150, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1143-2023 |
| spellingShingle | agricultural productivity maize unmanned aerial vehicles remote sensing smallholders Mbulisi Sibanda Buthelezi, S. Mutanga, O. Odindi, J. Clulow, A.D. Chimonyo, Vimbayi Grace Petrova Gokool, S. Naiken, V. Magidi, J. Mabhaudhi, Tafadzwanashe Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa |
| title | Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa |
| title_full | Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa |
| title_fullStr | Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa |
| title_full_unstemmed | Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa |
| title_short | Exploring the prospects of UAV-Remotely sensed data in estimating productivity of Maize crops in typical smallholder farms of Southern Africa |
| title_sort | exploring the prospects of uav remotely sensed data in estimating productivity of maize crops in typical smallholder farms of southern africa |
| topic | agricultural productivity maize unmanned aerial vehicles remote sensing smallholders |
| url | https://hdl.handle.net/10568/136075 |
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