Please use this identifier to cite or link to this item:
http://hdl.handle.net/2067/49838
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Antonucci, Francesca | it |
dc.contributor.author | Manganiello, Rossella | it |
dc.contributor.author | Costa, Corrado | it |
dc.contributor.author | Irione, Virgilio | it |
dc.contributor.author | Ortenzi, Luciano | it |
dc.contributor.author | Palombi, Maria A. | it |
dc.date.accessioned | 2023-05-26T10:10:51Z | - |
dc.date.available | 2023-05-26T10:10:51Z | - |
dc.date.issued | 2020 | it |
dc.identifier.issn | 2171-9292 | it |
dc.identifier.uri | http://hdl.handle.net/2067/49838 | - |
dc.description.abstract | Aim of study: Genetic diversity of pistachio, can be evaluated by using different descriptors, as adopted in international certification systems. Mainly the descriptors are morphological traits as leaf, which represents an important organ for its sensibility to growth conditions during the expansion phase. This study adopted a rapid and quantitative non-hierarchic clustering classification (k-means), to extract size classes basing on the contemporary combination of different morphological traits (i.e., leaf stalk length, terminal leaf length, terminal leaf width and terminal leaf ratio) of a varietal collection composed by 21 pistachio cultivars. | it |
dc.format.medium | STAMPA | it |
dc.language.iso | eng | it |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | A quantitative multivariate methodology for unsupervised class identification in pistachio (Pistacia vera L.) plant leaves size | it |
dc.type | article | * |
dc.identifier.doi | 10.5424/sjar/2020184-16904 | it |
dc.identifier.scopus | 85100835253 | it |
dc.identifier.url | https://revistas.inia.es/index.php/sjar/article/view/16904 | it |
dc.relation.journal | SPANISH JOURNAL OF AGRICULTURAL RESEARCH | it |
dc.relation.article | e0208 | it |
dc.relation.project | MiPAAF- Risorse Genetiche Vegetali-Trattato FAO, V Triennio 2017-2019 (DM 21076/2017) | it |
dc.relation.volume | 18 | it |
dc.relation.issue | 4 | it |
dc.subject.scientificsector | INF/01 INFORMATICA | it |
dc.subject.keywords | artificial neural network; morphological analysis; clustering; germplasm collection; k-means. | it |
dc.subject.ercsector | PE6_7, PE6_11, | it |
dc.description.international | no | it |
dc.contributor.country | ITA | it |
dc.type.referee | REF_1 | it |
dc.type.miur | 262 | * |
item.fulltext | With Fulltext | - |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | restricted | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.orcid | 0000-0002-1245-8882 | - |
crisitem.journal.journalissn | 2171-9292 | - |
crisitem.journal.ance | E219462 | - |
Appears in Collections: | A1. Articolo in rivista |
Files in This Item:
File | Description | Size | Format | |
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16904-Article Text-68644-1-10-20210209.pdf | 636.11 kB | Adobe PDF | View/Open |
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