Use of Leaf and Fruit Morphometric Analysis to Identify and Classify White Mulberry ( Morus alba L.) Genotypes

Riccardo Lo Bianco and Fabio Mirabella
Additional contact information
Riccardo Lo Bianco: Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo, 90128 Palermo, Italy
Fabio Mirabella: Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo, 90128 Palermo, Italy

Agriculture, 2018, vol. 8, issue 10, 1-9

Abstract: Digital image analysis and multivariate data analysis were used in this study to identify a set of leaf and fruit morphometric traits to discriminate white mulberry ( Morus alba L.) cultivars. The trial was conducted using three- to five-year-old potted cuttings of several white mulberry cultivars. 32 leaf morphometric descriptors were recorded in 2011 and 2012 from 11 mulberry cultivars using image analysis of scanned leaves, whereas six fruit descriptors were recorded in 2011 from nine mulberry cultivars. Linear discriminant analysis (LDA) was used to identify a subset of measured variables that could discriminate the cultivars in trial. Biplot analysis, followed by cluster analysis, was performed on the discriminant variables to investigate any possible cultivar grouping based on similar morphometric traits. LDA was able to discriminate the 11 cultivars with a canonical function, which included 13 leaf descriptors. Using those 13 descriptors, the Biplot showed that over 84% of the variability could be explained by the first three factors. Clustering of standardized biplot coordinates recognized three groups: the first including ‘Korinne’ and ‘Miura’ with similar leaf angles and apical tooth size; the second including ‘Cattaneo’, ‘Florio’, ‘Kokusò-21’, ‘Kokusò-27’, and ‘Kokusò Rosso’ with similar leaf size and shape; and the third including ‘Ichinose’, ‘Kayrio’, ‘Morettiana’, and ‘Restelli’, with similar leaf margin. Fruit descriptors were fewer and measured on fewer cultivars, yielding smaller discriminatory power than leaf descriptors. Use of leaf morphometric descriptors, along with image and multivariate analysis, proved to be effective for discriminating mulberry cultivars and showed promise for the implementation of a simple and inexpensive characterization and classification tool.

Keywords: biplot; descriptor; digital image analysis; linear discriminant analysis; Morus alba; multivariate analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2077-0472/8/10/157/pdf (application/pdf)
https://www.mdpi.com/2077-0472/8/10/157/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:8:y:2018:i:10:p:157-:d:174005

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().