Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season
Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize product...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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MDPI
2023
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| Online Access: | https://hdl.handle.net/10568/129757 |
| _version_ | 1855534963824263168 |
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| author | Buthelezi, S. Mutanga, O. Sibanda, M. Odindi, J. Clulow, A.D. Chimonyo, Vimbayi Grace Petrova Mabhaudhi, Tafadzwanashe |
| author_browse | Buthelezi, S. Chimonyo, Vimbayi Grace Petrova Clulow, A.D. Mabhaudhi, Tafadzwanashe Mutanga, O. Odindi, J. Sibanda, M. |
| author_facet | Buthelezi, S. Mutanga, O. Sibanda, M. Odindi, J. Clulow, A.D. Chimonyo, Vimbayi Grace Petrova Mabhaudhi, Tafadzwanashe |
| author_sort | Buthelezi, S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity. |
| format | Journal Article |
| id | CGSpace129757 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1297572025-12-08T10:29:22Z Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season Buthelezi, S. Mutanga, O. Sibanda, M. Odindi, J. Clulow, A.D. Chimonyo, Vimbayi Grace Petrova Mabhaudhi, Tafadzwanashe maize leaf area index vegetation index remote sensing unmanned aerial vehicles multispectral imagery small-scale farming smallholders growth stages monitoring forecasting models machine learning agricultural productivity crop yield Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity. 2023-03-15 2023-03-24T00:18:31Z 2023-03-24T00:18:31Z Journal Article https://hdl.handle.net/10568/129757 en Open Access MDPI Buthelezi, S.; Mutanga, O.; Sibanda, M.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season. Remote Sensing, 15(6):1597. (Special issue: Retrieving Leaf Area Index Using Remote Sensing) [doi: https://doi.org/10.3390/rs15061597] |
| spellingShingle | maize leaf area index vegetation index remote sensing unmanned aerial vehicles multispectral imagery small-scale farming smallholders growth stages monitoring forecasting models machine learning agricultural productivity crop yield Buthelezi, S. Mutanga, O. Sibanda, M. Odindi, J. Clulow, A.D. Chimonyo, Vimbayi Grace Petrova Mabhaudhi, Tafadzwanashe Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season |
| title | Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season |
| title_full | Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season |
| title_fullStr | Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season |
| title_full_unstemmed | Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season |
| title_short | Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season |
| title_sort | assessing the prospects of remote sensing maize leaf area index using uav derived multi spectral data in smallholder farms across the growing season |
| topic | maize leaf area index vegetation index remote sensing unmanned aerial vehicles multispectral imagery small-scale farming smallholders growth stages monitoring forecasting models machine learning agricultural productivity crop yield |
| url | https://hdl.handle.net/10568/129757 |
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