Determination of olive cultivars by deep learning and ISSR markers

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Sesli M., Yeğenoğlu E. D., Altıntaş V.

Journal of Environmental Biology, vol.41, no.2, pp.426-431, 2020 (SCI-Expanded, ESCI, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.22438/jeb/41/2(si)/jeb-22
  • Journal Name: Journal of Environmental Biology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Emerging Sources Citation Index (ESCI), Scopus, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Environment Index, Geobase, Pollution Abstracts, Veterinary Science Database
  • Page Numbers: pp.426-431
  • Keywords: Deep learning, ISSR markers, Neural networks, Olive cultivars
  • Open Archive Collection: AVESIS Open Access Collection
  • Manisa Celal Bayar University Affiliated: Yes

Abstract

Aim: The aim of the study was to make accurate estimation of olive varieties by using morphologic characters through deep learning and genetic characters through ISSR (Inter Simple Sequence Repeats) markers. Methodology: In this study, 800 leaf samples were collected from olive varieties and training and testing was performed; 600 samples were assessed for the training process and 200 samples were assessed for the testing process. Convolution of neural networks is a component of deep learning which is used frequently in image processing was used in this study. Results: Based on the results of such classification, the designed model was successful at a rate of 89.57% and it was also determined that this structure can be used in the area of problem. Interpretation: The success of convolution neural networks in terms of classification was exhibited. In ISSR method, the evaluation was performed on the basis of DNAs, i.e., genetic properties of varieties by means of ISSR markers.