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

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cg.contributor.affiliationUniversity of KwaZulu-Natalen_US
cg.contributor.affiliationUniversity of the Western Capeen_US
cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.donorWater Research Commission of South Africaen_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.initiativeDiversification in East and Southern Africaen_US
cg.contributor.initiativeExcellence in Agronomyen_US
cg.coverage.countrySouth Africaen_US
cg.coverage.iso3166-alpha2ZAen_US
cg.coverage.regionSouthern Africaen_US
cg.coverage.subregionKwaZulu-Natalen_US
cg.coverage.subregionSwayimaneen_US
cg.creator.identifierMabhaudhi T: 0000-0002-9323-8127en_US
cg.identifier.doihttps://doi.org/10.1016/j.heliyon.2024.e26913en_US
cg.identifier.iwmilibraryH052587en_US
cg.identifier.urlhttps://www.cell.com/action/showPdf?pii=S2405-8440%2824%2902944-Xen_US
cg.issn2405-8440en_US
cg.issue5en_US
cg.journalHeliyonen_US
cg.reviewStatusPeer Reviewen_US
cg.volume10en_US
dc.contributor.authorGokool, S.en_US
dc.contributor.authorMahomed, M.en_US
dc.contributor.authorBrewer, K.en_US
dc.contributor.authorNaiken, V.en_US
dc.contributor.authorClulow, A.en_US
dc.contributor.authorSibanda, M.en_US
dc.contributor.authorMabhaudhi, Tafadzwanasheen_US
dc.date.accessioned2024-03-31T05:22:43Zen_US
dc.date.available2024-03-31T05:22:43Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/140679en_US
dc.titleCrop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructureen_US
dcterms.abstractSmallholder 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.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.available2024-02-25en_US
dcterms.bibliographicCitationGokool, 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]en_US
dcterms.extent10(5):E26913.en_US
dcterms.issued2024-03-15en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-NC-ND-4.0en_US
dcterms.publisherElsevieren_US
dcterms.subjectcropsen_US
dcterms.subjectmappingen_US
dcterms.subjectunmanned aerial vehiclesen_US
dcterms.subjectimageryen_US
dcterms.subjectmachine learningen_US
dcterms.subjectsmallholdersen_US
dcterms.subjectfarmersen_US
dcterms.subjectland useen_US
dcterms.subjectland coveren_US
dcterms.typeJournal Articleen_US

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