Use of machine learning approaches for quantification of red spider mite (Acari: Tetranychidae) damage in Urochloa sp.

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dc.creatorEspitia-Buitrago, Paula
dc.creatorCotes-Torres, José M.
dc.creatorMating'i Kimani, Adrian
dc.creatorChidawanyika, Frank
dc.creatorHernández, Luis Miguel
dc.creatorCardoso Arango, Juan Andrés
dc.creatorJauregui, Rosa
dc.date2023-10-23
dc.date2023-11-07T15:24:42Z
dc.date2023-11-07T15:24:42Z
dc.date.accessioned2026-06-27T13:28:45Z
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/132802
dc.identifier.urihttp://hdl.handle.net/123456789/60672
dc.languageen
dc.publisherInternational Center for Tropical Agriculture
dc.rightsOpen Access
dc.sourceEspitia-Buitrago P.; Cotes-Torres J.M.; Mating'i A.; Chidawanyika F.; Hernández L.M.; Cardoso J.; Jauregui R. (2023) Use of machine learning approaches for quantification of red spider mite (Acari: Tetranychidae) damage in Urochloa sp. Poster prepared for African Plant Breeders Association 2023 Conference - Leveraging Genetic Innovation for Resilient African Food Systems in the wake of Global Shocks. Benguerir, Morocco, 23-26 October 2023. Cali (Colombia): International Center for Tropical Agriculture. 1 p.
dc.subjectmachine learning
dc.subjecttetranychidae
dc.subjectplant pests
dc.subjectpest resistance
dc.subjecturochloa
dc.subjectgenotypes
dc.titleUse of machine learning approaches for quantification of red spider mite (Acari: Tetranychidae) damage in Urochloa sp.
dc.typePoster

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