A quantitative multivariate methodology for unsupervised class identification in pistachio (Pistacia vera L.) plant leaves size
| Autor: | Maria A. Palombi, Corrado Costa, Luciano Ortenzi, Virgilio Irione, Rossella Manganiello, Francesca Antonucci |
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| Přispěvatelé: | Italian Ministry of Agriculture, MiPAAF (DM 21076/2017) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Genetic diversity Pistacia biology k-means Agricultural engineering lcsh:S Expansion phase artificial neural network morphological analysis clustering germplasm collection biology.organism_classification lcsh:Agriculture Horticulture Leaf width Stalk Statistical analysis Cultivar Agronomy and Crop Science |
| Zdroj: | Spanish Journal of Agricultural Research; Vol 18, No 4 (2020); e0208 Spanish Journal of Agricultural Research, Vol 18, Iss 4, Pp e0208-e0208 (2021) Spanish Journal of Agricultural Research; Vol. 18 No. 4 (2020); e0208 Spanish Journal of Agricultural Research; Vol. 18 Núm. 4 (2020); e0208 SJAR. Spanish Journal of Agricultural Research instname |
| ISSN: | 2171-9292 |
| Popis: | 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.Area of study: Worldwide.Material and methods: The unsupervised non-hierarchic clustering technique was adopted to the entire samples of pistachio leaves from k=2 to k=15 for both four morphological variables (i.e., leaf stalk length, terminal leaf length, terminal leaf width and terminal leaf ratio) and three morphological variables (i.e., terminal leaf length, terminal leaf width and terminal leaf ratio).Main results: A classification model only on the three morphological variables (for results of statistical analysis in which the groups resulted to be more separated and different for all the variables), with k= 5 (five groups), was constructed using a non-linear artificial neural network approach. The percentages of bad prediction in both training and testing resulted equal to 0%. The “terminal leaf length” returned the higher impact (44.89%).Research highlights: The contemporary combination of different morphological leaf traits, allowed to create an automatic classification of size classes of great importance for cultivar identification and comparison. |
| Databáze: | OpenAIRE |
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