Participatory AI for inclusive crop improvement

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dc.creatorLasdun, Violet
dc.creatorGuerena, David Tonatiuh
dc.creatorOrtiz-Crespo, Berta
dc.creatorMutuvi, Stephen Mutisya
dc.creatorSelvaraj, Michael Gomez
dc.creatorAssefa, Teshale
dc.date2024-10
dc.date2025-01-29T15:38:58Z
dc.date2025-01-29T15:38:58Z
dc.date.accessioned2026-06-27T13:35:51Z
dc.descriptionCrop breeding in the Global South faces a 'phenotyping bottleneck' due to reliance on manual visual phenotyping, which is both error-prone and challenging to scale across multiple environments, inhibiting selection of germplasm adapted to farmer production environments. This limitation impedes rapid varietal turnover, crucial for maintaining high yields and food security under climate change. Low adoption of improved varieties results from a top-down system in which farmers have been more passive recipients than active participants in varietal development.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/172411
dc.identifier.urihttp://hdl.handle.net/123456789/64384
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceLasdun, V.; Guerena, D.T.; Ortiz-Crespo, B.; Mutuvi, S.M.; Selvaraj, M.G.; Assefa, T. (2024) Participatory AI for inclusive crop improvement. Agricultural Systems 220: 104054. ISSN: 0308-521X
dc.subjecton-farm research
dc.subjectevaluation
dc.subjectdata collection
dc.subjectvarieties
dc.subjectartificial intelligence
dc.subjectphenotyping
dc.subjectparticipatory plant breeding
dc.subjectimagery
dc.titleParticipatory AI for inclusive crop improvement
dc.typeJournal Article

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