Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure
Smallholder farms are major contributors to agricultural production, food security, and socioeconomic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lowercost solutions to opt...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/140679 |
| _version_ | 1855518059354128384 |
|---|---|
| author | Gokool, S. Mahomed, M. Brewer, K. Naiken, V. Clulow, A. Sibanda, M. Mabhaudhi, Tafadzwanashe |
| author_browse | Brewer, K. Clulow, A. Gokool, S. Mabhaudhi, Tafadzwanashe Mahomed, M. Naiken, V. Sibanda, M. |
| author_facet | Gokool, S. Mahomed, M. Brewer, K. Naiken, V. Clulow, A. Sibanda, M. Mabhaudhi, Tafadzwanashe |
| author_sort | Gokool, S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Smallholder farms are major contributors to agricultural production, food security, and socioeconomic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lowercost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers. |
| format | Journal Article |
| id | CGSpace140679 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1406792025-10-26T12:51:40Z Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure Gokool, S. Mahomed, M. Brewer, K. Naiken, V. Clulow, A. Sibanda, M. Mabhaudhi, Tafadzwanashe crops mapping unmanned aerial vehicles imagery machine learning smallholders farmers land use land cover Smallholder farms are major contributors to agricultural production, food security, and socioeconomic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lowercost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers. 2024-03 2024-03-31T05:22:43Z 2024-03-31T05:22:43Z Journal Article https://hdl.handle.net/10568/140679 en Open Access Elsevier Gokool, S.; Mahomed, M.; Brewer, K.; Naiken, V.; Clulow, A.; Sibanda, M.; Mabhaudhi, Tafadzwanashe. 2024. Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure. Heliyon, 10(5):E26913. [doi: https://doi.org/10.1016/j.heliyon.2024.e26913] |
| spellingShingle | crops mapping unmanned aerial vehicles imagery machine learning smallholders farmers land use land cover Gokool, S. Mahomed, M. Brewer, K. Naiken, V. Clulow, A. Sibanda, M. Mabhaudhi, Tafadzwanashe Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure |
| title | Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure |
| title_full | Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure |
| title_fullStr | Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure |
| title_full_unstemmed | Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure |
| title_short | Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure |
| title_sort | crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure |
| topic | crops mapping unmanned aerial vehicles imagery machine learning smallholders farmers land use land cover |
| url | https://hdl.handle.net/10568/140679 |
| work_keys_str_mv | AT gokools cropmappinginsmallholderfarmsusingunmannedaerialvehicleimageryandgeospatialcloudcomputinginfrastructure AT mahomedm cropmappinginsmallholderfarmsusingunmannedaerialvehicleimageryandgeospatialcloudcomputinginfrastructure AT brewerk cropmappinginsmallholderfarmsusingunmannedaerialvehicleimageryandgeospatialcloudcomputinginfrastructure AT naikenv cropmappinginsmallholderfarmsusingunmannedaerialvehicleimageryandgeospatialcloudcomputinginfrastructure AT clulowa cropmappinginsmallholderfarmsusingunmannedaerialvehicleimageryandgeospatialcloudcomputinginfrastructure AT sibandam cropmappinginsmallholderfarmsusingunmannedaerialvehicleimageryandgeospatialcloudcomputinginfrastructure AT mabhaudhitafadzwanashe cropmappinginsmallholderfarmsusingunmannedaerialvehicleimageryandgeospatialcloudcomputinginfrastructure |