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Enhancing smallholder wheat yield prediction through sensor fusion and phenology with machine learning and deep learning methods


Tipo:
Article
Título:
Enhancing smallholder wheat yield prediction through sensor fusion and phenology with machine learning and deep learning methods
Creador/a:
Aklilu Tesfaye, A.;
Awoke, B.G.;
Sida, T.S.;
ORCID iD icon
Sida, T.S.
ORCID iD iconhttps://orcid.org/0000-0001-6482-2669
ScopusScopus ID
researcheridResearcher ID
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Osgood, D.
Año:
2022
URI:
https://hdl.handle.net/10883/22317
Copyright:
CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Revista:
Agriculture (Switzerland)
Volumen de la Revista:
12
No de Revista:
9
Número de artículo:
1352
DOI:
10.3390/agriculture12091352
Lugar de publicación:
Basel (Switzerland)
Editor:
MDPI
Cita:
Enhancing smallholder wheat yield prediction through sensor fusion and phenology with machine learning and deep learning methods. 2022. 12 (9) DOI: 10.3390/agriculture12091352 MDPI.

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Genetic ResourcesInstitutionalMaizeSocioeconomicsSustainable Agrifood SystemsSustainable IntensificationWheat

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