A comprehensive analysis of machine learning and remote sensing techniques in studying climate hazards-induced crop yield variations

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cg.contributor.affiliationInternational Water Management Instituteen_US
cg.coverage.regionAfricaen_US
cg.coverage.regionAsiaen_US
cg.creator.identifierSalomon OBAHOUNDJE: 0000-0001-8093-5241en_US
cg.creator.identifierSeifu Tilahun: 0000-0002-5219-4527en_US
cg.creator.identifierBirhanu Zemadim: 0000-0002-3497-2364en_US
cg.creator.identifierPetra Schmitter: 0000-0002-3826-7224en_US
cg.placeGeneva, Switzerland.en_US
dc.contributor.authorObahoundje, Salomonen_US
dc.contributor.authorTilahun, Seifu A.en_US
dc.contributor.authorZemadim, Birhanuen_US
dc.contributor.authorSchmitter, Petra S.en_US
dc.date.accessioned2024-12-14T09:56:36Zen_US
dc.date.available2024-12-14T09:56:36Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/163479en_US
dc.titleA comprehensive analysis of machine learning and remote sensing techniques in studying climate hazards-induced crop yield variationsen_US
dcterms.accessRightsOpen Accessen_US
dcterms.bibliographicCitationObahoundje, Salomon; Tilahun, Seifu A.; Zemadim, Birhanu; Schmitter, Petra. 2024. A comprehensive analysis of machine learning and remote sensing techniques in studying climate hazards-induced crop yield variations. Poster presented at the Drought Resilience +10 Conference, Geneva, Switzerland, 30 September – 02 October 2024.en_US
dcterms.issued2024-09-30en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.subjectanalysisen_US
dcterms.subjectmachine learningen_US
dcterms.subjectremote sensingen_US
dcterms.subjecttechniquesen_US
dcterms.subjectclimate changeen_US
dcterms.subjecthazardsen_US
dcterms.subjectclimate variabilityen_US
dcterms.subjectcrop yielden_US
dcterms.subjectcrop productionen_US
dcterms.subjectdroughten_US
dcterms.subjectindicatorsen_US
dcterms.subjectfood securityen_US
dcterms.subjectrisk managementen_US
dcterms.typePosteren_US

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