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

Vista sencilla de ítem
dc.creatorObahoundje, Salomon
dc.creatorTilahun, Seifu A.
dc.creatorZemadim, Birhanu
dc.creatorSchmitter, Petra S.
dc.date2024-09-30
dc.date2024-12-14T09:56:36Z
dc.date2024-12-14T09:56:36Z
dc.date.accessioned2026-06-27T18:34:53Z
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/163479
dc.identifier.urihttp://hdl.handle.net/123456789/160000
dc.languageen
dc.rightsOpen Access
dc.sourceObahoundje, 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.
dc.subjectanalysis
dc.subjectmachine learning
dc.subjectremote sensing
dc.subjecttechniques
dc.subjectclimate change
dc.subjecthazards
dc.subjectclimate variability
dc.subjectcrop yield
dc.subjectcrop production
dc.subjectdrought
dc.subjectindicators
dc.subjectfood security
dc.subjectrisk management
dc.titleA comprehensive analysis of machine learning and remote sensing techniques in studying climate hazards-induced crop yield variations
dc.typePoster

Archivos

Colecciones

Instituto Internacional para el Manejo del Agua (IWMI)