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Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning

Author

Listed:
  • Ewa Ropelewska

    (Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland)

  • Kadir Sabanci

    (Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey)

  • Muhammet Fatih Aslan

    (Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey)

Abstract

The aim of this study was to develop models based on linear dimensions or shape factors, and the sets of combined linear dimensions and shape factors for discrimination of sour cherry pits of different cultivars (‘Debreceni botermo’, ‘Łutówka’, ‘Nefris’, ‘Kelleris’). The geometric parameters were calculated using image processing. The pits of different sour cherry cultivars statistically significantly differed in terms of selected dimensions and shape factors. The discriminative models built based on linear dimensions produced average accuracies of up to 95% for distinguishing the pit cultivars in the case of ‘Nefris’ vs. ‘Kelleris’ and 72% for all four cultivars. The average accuracies for the discriminative models built based on shape factors were up to 95% for the ‘Nefris’ and ‘Kelleris’ pits and 73% for four cultivars. The models combining the linear dimensions and shape factors produced accuracies reaching 96% for the ‘Nefris’ vs. ‘Kelleris’ pits and 75% for all cultivars. The geometric parameters with high discriminative power may be used for distinguishing different cultivars of sour cherry pits. It can be of great importance for practical applications. It may allow avoiding the adulteration and mixing of different cultivars.

Suggested Citation

  • Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan, 2021. "Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning," Agriculture, MDPI, vol. 11(12), pages 1-12, December.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1212-:d:693300
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    References listed on IDEAS

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    1. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Agriculture, MDPI, vol. 11(5), pages 1-13, May.
    2. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Papers 2104.14286, arXiv.org.
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    Citations

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    Cited by:

    1. Ewa Ropelewska & Xiang Cai & Zhan Zhang & Kadir Sabanci & Muhammet Fatih Aslan, 2022. "Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum ( Prunus domestica L.) Kernels," Agriculture, MDPI, vol. 12(2), pages 1-12, February.
    2. Younés Noutfia & Ewa Ropelewska, 2022. "Comprehensive Characterization of Date Palm Fruit ‘Mejhoul’ ( Phoenix dactylifera L.) Using Image Analysis and Quality Attribute Measurements," Agriculture, MDPI, vol. 13(1), pages 1-12, December.
    3. Kadir Sabanci & Muhammet Fatih Aslan & Vanya Slavova & Stefka Genova, 2022. "The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line," Agriculture, MDPI, vol. 12(10), pages 1-11, October.

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