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

cg.contributor.affiliationUniversity of KwaZulu-Natalen
cg.contributor.affiliationUniversity of the Western Capeen
cg.contributor.affiliationInternational Water Management Instituteen
cg.contributor.donorWater Research Commission of South Africaen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeDiversification in East and Southern Africa
cg.contributor.initiativeExcellence in Agronomy
cg.coverage.countrySouth Africa
cg.coverage.iso3166-alpha2ZA
cg.coverage.regionSouthern Africa
cg.coverage.subregionKwaZulu-Natal
cg.coverage.subregionSwayimane
cg.creator.identifierMabhaudhi T: 0000-0002-9323-8127
cg.identifier.doihttps://doi.org/10.1016/j.heliyon.2024.e26913en
cg.identifier.iwmilibraryH052587
cg.identifier.urlhttps://www.cell.com/action/showPdf?pii=S2405-8440%2824%2902944-Xen
cg.isijournalISI Journalen
cg.issn2405-8440en
cg.issue5en
cg.journalHeliyonen
cg.reviewStatusPeer Reviewen
cg.volume10en
dc.contributor.authorGokool, S.en
dc.contributor.authorMahomed, M.en
dc.contributor.authorBrewer, K.en
dc.contributor.authorNaiken, V.en
dc.contributor.authorClulow, A.en
dc.contributor.authorSibanda, M.en
dc.contributor.authorMabhaudhi, Tafadzwanasheen
dc.date.accessioned2024-03-31T05:22:43Zen
dc.date.available2024-03-31T05:22:43Zen
dc.identifier.urihttps://hdl.handle.net/10568/140679
dc.titleCrop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructureen
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
dcterms.accessRightsOpen Access
dcterms.available2024-02-25
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
dcterms.extent10(5):E26913.en
dcterms.issued2024-03-15
dcterms.languageen
dcterms.licenseCC-BY-NC-ND-4.0
dcterms.publisherElsevieren
dcterms.subjectcropsen
dcterms.subjectmappingen
dcterms.subjectunmanned aerial vehiclesen
dcterms.subjectimageryen
dcterms.subjectmachine learningen
dcterms.subjectsmallholdersen
dcterms.subjectfarmersen
dcterms.subjectland useen
dcterms.subjectland coveren
dcterms.typeJournal Article

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