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
cg.coverage.regionAfrica
cg.coverage.regionAsia
cg.creator.identifierSalomon OBAHOUNDJE: 0000-0001-8093-5241en
cg.creator.identifierSeifu Tilahun: 0000-0002-5219-4527en
cg.creator.identifierBirhanu Zemadim: 0000-0002-3497-2364en
cg.creator.identifierPetra Schmitter: 0000-0002-3826-7224en
cg.placeGeneva, Switzerland.en
dc.contributor.authorObahoundje, Salomonen
dc.contributor.authorTilahun, Seifu A.en
dc.contributor.authorZemadim, Birhanuen
dc.contributor.authorSchmitter, Petra S.en
dc.date.accessioned2024-12-14T09:56:36Zen
dc.date.available2024-12-14T09:56:36Zen
dc.identifier.urihttps://hdl.handle.net/10568/163479
dc.titleA comprehensive analysis of machine learning and remote sensing techniques in studying climate hazards-induced crop yield variationsen
dcterms.accessRightsOpen Access
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
dcterms.issued2024-09-30en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.subjectanalysisen
dcterms.subjectmachine learningen
dcterms.subjectremote sensingen
dcterms.subjecttechniquesen
dcterms.subjectclimate changeen
dcterms.subjecthazardsen
dcterms.subjectclimate variabilityen
dcterms.subjectcrop yielden
dcterms.subjectcrop productionen
dcterms.subjectdroughten
dcterms.subjectindicatorsen
dcterms.subjectfood securityen
dcterms.subjectrisk managementen
dcterms.typePoster

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