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Pollen performance modelling with an artificial neural network on commercial stone fruit cultivars

  • Sultan Filiz GüçlüEmail author
  • Ziya Öncü
  • Fatma Koyuncu
Research Report
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Abstract

Pollen tube growth and pollen germination percentage are key factors for successful fruit set. Pollen performance is critical for the production and breeding of flowering plants and in agricultural systems in terms of fruit development. This study was carried out to predict pollen tube growth and pollen germination percentage in four stone fruits species (cherry (Prunus avium), apricot (Prunus armeniaca), plum (Prunus domestica), and peach (Prunus persica)) using a neural network. For this purpose, we measured pollen tube length and pollen germination rates under in vitro conditions. For the in vitro test, pollen grains of four stone fruit cultivars were sown in three different media and incubated at seven different temperatures for four incubation periods. A layered neural network was used for estimating the pollen germination rate and pollen tube length related to the in vitro condition. This study suggests a method for estimating the pollen germination rate and pollen tube length using artificial neural networks. The performed artificial neural networks produced an efficient prediction from in vitro data. The determination coefficients obtained between the observed and predicted data sets are 0.86 (for germination rate) and 0.81 (for tube length), indicating an accurate estimation of the in vitro data. In our case, the network that produced the best result had a 4:9:2 architecture.

Keywords

Artificial neural networks Pollen germination Pollen tube length Prunus Prediction 

Notes

Acknowledgements

The authors would like to thanks the two anonymous reviewers for their constructive comments which significantly improved the quality of this paper.

Author’s contribution

We, the authors (names and orders of appearance are as the title: SFG, ZÖ, FK), by awareness of the nonchangeability of the names, orders of appearance and information of authors (no authors can be added or removed at all) declare that we all have contributed in producing this article (doing the researches or writing the article) and no names have been added without having an effective role to the article.

Compliance with ethical standards

Conflict of interest

This article is not extracted from a dissertation or research project. An individual research. The authors declare that they have no conflict of interest.

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Copyright information

© Korean Society for Horticultural Science 2020

Authors and Affiliations

  1. 1.Atabey Vocational School AtabeyIsparta University of Applied SciencesIspartaTurkey
  2. 2.Department of Horticulture, Faculty of AgricultureIsparta University of Applied SciencesIspartaTurkey

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