Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure

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dc.creatorGokool, S.
dc.creatorMahomed, M.
dc.creatorBrewer, K.
dc.creatorNaiken, V.
dc.creatorClulow, A.
dc.creatorSibanda, M.
dc.creatorMabhaudhi, Tafadzwanashe
dc.date2024-03
dc.date2024-03-31T05:22:43Z
dc.date2024-03-31T05:22:43Z
dc.date.accessioned2026-06-27T18:26:40Z
dc.descriptionSmallholder 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.
dc.identifierhttps://hdl.handle.net/10568/140679
dc.identifier.urihttp://hdl.handle.net/123456789/156034
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceGokool, 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]
dc.subjectcrops
dc.subjectmapping
dc.subjectunmanned aerial vehicles
dc.subjectimagery
dc.subjectmachine learning
dc.subjectsmallholders
dc.subjectfarmers
dc.subjectland use
dc.subjectland cover
dc.titleCrop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure
dc.typeJournal Article

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Instituto Internacional para el Manejo del Agua (IWMI)