reference/28455740

http://rdf.ncbi.nlm.nih.gov/pubchem/reference/28455740

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Predicate	Object
contentType	Journal Article
issn	1746-4811
issueIdentifier	1
pageRange	34-
publicationName	Plant Methods
startingPage	34
hasFundingAgency	http://rdf.ncbi.nlm.nih.gov/pubchem/organization/MD5_89f5fd9a960d481378ba545da1230b36 http://rdf.ncbi.nlm.nih.gov/pubchem/organization/MD5_da9ad82bd834fe3e7c8f46c85790acb4 http://rdf.ncbi.nlm.nih.gov/pubchem/organization/MD5_6b669abb338e8988c5afc2d186a57905 http://rdf.ncbi.nlm.nih.gov/pubchem/organization/MD5_dd9815efaf83485d759c940d4bc723fb http://rdf.ncbi.nlm.nih.gov/pubchem/organization/MD5_2beb19b4457db77c331577c2509e2b56 http://rdf.ncbi.nlm.nih.gov/pubchem/organization/MD5_d7c2963b5f54bdf25557d0d988b83b22
bibliographicCitation	Zhang J, Zhang W, Xiong S, Song Z, Tian W, Shi L, Ma X. Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content. Plant Methods. 2021 Mar 30;17(1):34. PMID: 33789711; PMCID: PMC8011113.
creator	http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_53d6abcadb3b8d76988f48ae82f1c3ce http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_5ff5c593de93ad0cd445bc2fd9446fb7 http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_bd8f13a1a960b7d75ae86323d4f86f9f http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_30267016987e2ee6077b1a332c96ba32 http://rdf.ncbi.nlm.nih.gov/pubchem/author/ORCID_0000-0001-8460-1601 http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_292e5cbad3a5f94cd91c9ca51d150bed http://rdf.ncbi.nlm.nih.gov/pubchem/author/MD5_2e8eb96536093d948085eb743909a098
date	2021-03-31^^<http://www.w3.org/2001/XMLSchema#date>
identifier	https://pubmed.ncbi.nlm.nih.gov/PMC8011113 https://pubmed.ncbi.nlm.nih.gov/33789711 https://doi.org/10.1186/s13007-021-00737-2
isPartOf	https://portal.issn.org/resource/ISSN/1746-4811 http://rdf.ncbi.nlm.nih.gov/pubchem/journal/32399
language	English
source	https://scigraph.springernature.com/ https://www.crossref.org/ https://pubmed.ncbi.nlm.nih.gov/
title	Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content

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