Participatory AI for inclusive crop improvement
| cg.authorship.types | CGIAR and advanced research institute | en_US |
| cg.contributor.affiliation | International Center for Tropical Agriculture | en_US |
| cg.contributor.affiliation | Bioversity International | en_US |
| cg.contributor.affiliation | London School of Economics and Political Science | en_US |
| cg.contributor.initiative | Accelerated Breeding | en_US |
| cg.creator.identifier | David Guerena: 0000-0002-3313-0798 | en_US |
| cg.creator.identifier | Berta Ortiz-Crespo: 0000-0002-6664-0815 | en_US |
| cg.creator.identifier | MICHAEL GOMEZ SELVARAJ: 0000-0003-2394-0399 | en_US |
| cg.creator.identifier | Teshale Mamo: 0000-0002-9227-0408 | en_US |
| cg.identifier.doi | https://doi.org/10.1016/j.agsy.2024.104054 | en_US |
| cg.isijournal | ISI Journal | en_US |
| cg.issn | 0308-521X | en_US |
| cg.journal | Agricultural Systems | en_US |
| cg.reviewStatus | Peer Review | en_US |
| cg.subject.alliancebiovciat | CROP PRODUCTION | en_US |
| cg.subject.alliancebiovciat | PLANT BREEDING | en_US |
| cg.subject.sdg | SDG 2 - Zero hunger | en_US |
| cg.volume | 220 | en_US |
| dc.contributor.author | Lasdun, Violet | en_US |
| dc.contributor.author | Guerena, David Tonatiuh | en_US |
| dc.contributor.author | Ortiz-Crespo, Berta | en_US |
| dc.contributor.author | Mutuvi, Stephen Mutisya | en_US |
| dc.contributor.author | Selvaraj, Michael Gomez | en_US |
| dc.contributor.author | Assefa, Teshale | en_US |
| dc.date.accessioned | 2025-01-29T15:38:58Z | en_US |
| dc.date.available | 2025-01-29T15:38:58Z | en_US |
| dc.identifier.uri | https://hdl.handle.net/10568/172411 | en_US |
| dc.title | Participatory AI for inclusive crop improvement | en_US |
| dcterms.abstract | Crop 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. | en_US |
| dcterms.accessRights | Open Access | en_US |
| dcterms.available | 2024-07-24 | en_US |
| dcterms.bibliographicCitation | Lasdun, 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 | en_US |
| dcterms.extent | 104054 | en_US |
| dcterms.issued | 2024-07-24 | en_US |
| dcterms.language | en | en_US |
| dcterms.license | CC-BY-4.0 | en_US |
| dcterms.publisher | Elsevier BV | en_US |
| dcterms.subject | on-farm research | en_US |
| dcterms.subject | evaluation | en_US |
| dcterms.subject | data collection | en_US |
| dcterms.subject | varieties | en_US |
| dcterms.subject | artificial intelligence | en_US |
| dcterms.subject | phenotyping | en_US |
| dcterms.subject | participatory plant breeding | en_US |
| dcterms.subject | imagery | en_US |
| dcterms.type | Journal Article | en_US |
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