Machine learning approaches for crop improvement: Leveraging phenotypic and 
genotypic big data

Tong, H.; Nikoloski, Z.

doi:10.1016/j.jplph.2020.153354

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Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data

Tong, H., & Nikoloski, Z. (2021). Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. Journal of Plant Physiology, 257: 153354. doi:10.1016/j.jplph.2020.153354.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-A8F9-3 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-7DD0-D

Genre: Journal Article

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Creators:
Tong, H.¹, Author
Nikoloski, Z.¹, Author

Affiliations:
1Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310

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Language(s): eng - English

Dates: Date issued: 2021

Publication Status: Issued

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Identifiers: DOI: 10.1016/j.jplph.2020.153354
BibTex Citekey: Tong2021

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Title: Journal of Plant Physiology

Other : J. Plant Physiol.

Source Genre: Journal

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Publ. Info: Stuttgart : Urban & Fischer

Pages: - Volume / Issue: 257 Sequence Number: 153354 Start / End Page: - Identifier: ISSN: 0176-1617
CoNE: https://pure.mpg.de/cone/journals/resource/954928517792