Modelling daily plant growth response to environmental conditions in Chinese solar greenhouse using Bayesian neural network

Mohmed, G, Heynes, X ORCID: https://orcid.org/0000-0001-5752-8111, Naser, A ORCID: https://orcid.org/0000-0001-5969-1756, Sun, W, Hardy, K ORCID: https://orcid.org/0000-0002-7619-2374, Grundy, S and Lu, C ORCID: https://orcid.org/0000-0002-0064-4725, 2023. Modelling daily plant growth response to environmental conditions in Chinese solar greenhouse using Bayesian neural network. Scientific Reports, 13 (1): 4379. ISSN 2045-2322

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Item Type:	Journal article
Publication Title:	Scientific Reports
Creators:	Mohmed, G., Heynes, X., Naser, A., Sun, W., Hardy, K., Grundy, S. and Lu, C.
Publisher:	Springer Science and Business Media LLC
Date:	2023
Volume:	13
Number:	1
ISSN:	2045-2322
Identifiers:	Number Type 10.1038/s41598-023-30846-y DOI 1742467 Other
Divisions:	Schools > School of Animal, Rural and Environmental Sciences Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	20 Mar 2023 12:04
Last Modified:	21 Mar 2023 08:48
URI:	https://irep.ntu.ac.uk/id/eprint/48555

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