Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method

Abstract Grapevine (Vitis vinifera L.) currently includes thousands of cultivars. Discrimination between these varieties, historically done by ampelography, is done in recent decades mostly by genetic analysis. However, when aiming to identify archaeobotanical remains, which are mostly charred with...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vlad Landa, Yekaterina Shapira, Michal David, Avshalom Karasik, Ehud Weiss, Yuval Reuveni, Elyashiv Drori
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bbf94284b76b428aaf208e38bf907c17
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:bbf94284b76b428aaf208e38bf907c17
record_format	dspace
spelling	oai:doaj.org-article:bbf94284b76b428aaf208e38bf907c172021-12-02T16:10:38ZAccurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method10.1038/s41598-021-92559-42045-2322https://doaj.org/article/bbf94284b76b428aaf208e38bf907c172021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92559-4https://doaj.org/toc/2045-2322Abstract Grapevine (Vitis vinifera L.) currently includes thousands of cultivars. Discrimination between these varieties, historically done by ampelography, is done in recent decades mostly by genetic analysis. However, when aiming to identify archaeobotanical remains, which are mostly charred with extremely low genomic preservation, the application of the genomic approach is rarely successful. As a result, variety-level identification of most grape remains is currently prevented. Because grape pips are highly polymorphic, several attempts were made to utilize their morphological diversity as a classification tool, mostly using 2D image analysis technics. Here, we present a highly accurate varietal classification tool using an innovative and accessible 3D seed scanning approach. The suggested classification methodology is machine-learning-based, applied with the Iterative Closest Point (ICP) registration algorithm and the Linear Discriminant Analysis (LDA) technique. This methodology achieved classification results of 91% to 93% accuracy in average when trained by fresh or charred seeds to test fresh or charred seeds, respectively. We show that when classifying 8 groups, enhanced accuracy levels can be achieved using a "tournament" approach. Future development of this new methodology can lead to an effective seed classification tool, significantly improving the fields of archaeobotany, as well as general taxonomy.Vlad LandaYekaterina ShapiraMichal DavidAvshalom KarasikEhud WeissYuval ReuveniElyashiv DroriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vlad Landa Yekaterina Shapira Michal David Avshalom Karasik Ehud Weiss Yuval Reuveni Elyashiv Drori Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method
description	Abstract Grapevine (Vitis vinifera L.) currently includes thousands of cultivars. Discrimination between these varieties, historically done by ampelography, is done in recent decades mostly by genetic analysis. However, when aiming to identify archaeobotanical remains, which are mostly charred with extremely low genomic preservation, the application of the genomic approach is rarely successful. As a result, variety-level identification of most grape remains is currently prevented. Because grape pips are highly polymorphic, several attempts were made to utilize their morphological diversity as a classification tool, mostly using 2D image analysis technics. Here, we present a highly accurate varietal classification tool using an innovative and accessible 3D seed scanning approach. The suggested classification methodology is machine-learning-based, applied with the Iterative Closest Point (ICP) registration algorithm and the Linear Discriminant Analysis (LDA) technique. This methodology achieved classification results of 91% to 93% accuracy in average when trained by fresh or charred seeds to test fresh or charred seeds, respectively. We show that when classifying 8 groups, enhanced accuracy levels can be achieved using a "tournament" approach. Future development of this new methodology can lead to an effective seed classification tool, significantly improving the fields of archaeobotany, as well as general taxonomy.
format	article
author	Vlad Landa Yekaterina Shapira Michal David Avshalom Karasik Ehud Weiss Yuval Reuveni Elyashiv Drori
author_facet	Vlad Landa Yekaterina Shapira Michal David Avshalom Karasik Ehud Weiss Yuval Reuveni Elyashiv Drori
author_sort	Vlad Landa
title	Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method
title_short	Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method
title_full	Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method
title_fullStr	Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method
title_full_unstemmed	Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method
title_sort	accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bbf94284b76b428aaf208e38bf907c17
work_keys_str_mv	AT vladlanda accurateclassificationoffreshandcharredgrapeseedstothevarietallevelusingmachinelearningbasedclassificationmethod AT yekaterinashapira accurateclassificationoffreshandcharredgrapeseedstothevarietallevelusingmachinelearningbasedclassificationmethod AT michaldavid accurateclassificationoffreshandcharredgrapeseedstothevarietallevelusingmachinelearningbasedclassificationmethod AT avshalomkarasik accurateclassificationoffreshandcharredgrapeseedstothevarietallevelusingmachinelearningbasedclassificationmethod AT ehudweiss accurateclassificationoffreshandcharredgrapeseedstothevarietallevelusingmachinelearningbasedclassificationmethod AT yuvalreuveni accurateclassificationoffreshandcharredgrapeseedstothevarietallevelusingmachinelearningbasedclassificationmethod AT elyashivdrori accurateclassificationoffreshandcharredgrapeseedstothevarietallevelusingmachinelearningbasedclassificationmethod
_version_	1718384435286507520

Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method

Ejemplares similares