Journal of Environmental Biology

UPGMA and artificial neural networks applications on wild type olives

M. Sesli¹*, E.D. Yeğen oğlu², V. Altıntaş³ and Y. Gevrek?i⁴

¹Department of Tobacco Breeding, School of Tobacco Expertise, Manisa Celal Bayar University, Akhisar, Manisa-45210, Turkey

²Department of Plant and Animal Production, Alasehir Vocational School, Manisa Celal Bayar University, Alasehir, Manisa-45400, Turkey

³Department of Computer Programming, Akhisar Vocational School, Manisa Celal Bayar University, Akhisar, Manisa-45210, Turkey

⁴Department of Animal Science, Unit of Biometry-Genetics, Bornova, Agriculture Faculty, Ege University, Izmir-35100, Turkey

*Corresponding Author E-mail: meltem.sesli@bayar.edu.tr

Key words

Artificial neural networks,

ISSR, UPGMA,

Wild olives

Publication Data

Paper received : 18.09.2016

Revised received : 20.05.2017

Accepted : 27.06.2017

Abstract

Aim: Plant genetic sources are important to study genetic variability and richness of hereditary knowledge of plant species in gene pool. Local varieties, rural populations, wild types and old varieties are the primary ones. In this respect, wild type olives (Olea europaea oleaster) are valuable in terms of olive breeding, cultivation and ecosystem. The aim of the study was to determine genetic distances between olive varieties.

Methodology: Artificial Neural Networks intuitive algorithm application was performed on seven wild type olives grown in different regions of Turkey by using data obtained from twenty-two ISSR primers.

Results: UPGMA dendrograms were developed through Jaccard, simple matching coefficients, and similarity matrices; and genetic similarities and dissimilarities were exhibited.

Interpretation: It was concluded that Artificial Neural Networks would be beneficial for estimating olive types accurately based on the results obtained from earlier studies performed with genetic markers.

Copyright ? 2017 Triveni Enterprises. All rights reserved. No part of the Journal can be reproduced in any form without prior permission. Responsibility regarding the authenticity of the data, and the acceptability of the conclusions enforced or derived, rest completely with the author(s).