and artificial neural networks applications on wild type olives
Sesli1*, E.D. Yeğen oğlu2, V.
Altıntaş3 and Y. Gevrekçi4
Tobacco Breeding, School of Tobacco Expertise, Manisa Celal Bayar University,
Akhisar, Manisa-45210, Turkey
Plant and Animal Production, Alasehir Vocational School, Manisa Celal Bayar
University, Alasehir, Manisa-45400, Turkey
Computer Programming, Akhisar Vocational School, Manisa Celal Bayar
University, Akhisar, Manisa-45210, Turkey
Animal Science, Unit of Biometry-Genetics, Bornova, Agriculture Faculty, Ege
University, Izmir-35100, Turkey
Author E-mail: firstname.lastname@example.org
Artificial neural networks,
Paper received : 18.09.2016
Revised received : 20.05.2017
Accepted : 27.06.2017
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.
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.
dendrograms were developed through Jaccard, simple matching coefficients, and
similarity matrices; and genetic similarities and dissimilarities were
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.
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