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The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line

Author

Listed:
  • 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)

  • Vanya Slavova

    (Department of Plant Breeding, Maritsa Vegetable Crops Research Institute, Agricultural Academy Bulgaria, 32, Brezovsko Shosse St., 4003 Plovdiv, Bulgaria)

  • Stefka Genova

    (Department of Plant Breeding, Maritsa Vegetable Crops Research Institute, Agricultural Academy Bulgaria, 32, Brezovsko Shosse St., 4003 Plovdiv, Bulgaria)

Abstract

The objective of this study was to evaluate differences between the red onion cultivar and breeding line using models based on selected fluorescence spectroscopic data built using machine-learning algorithms from different groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The combination of fluorescence spectroscopy and machine learning is an original approach to the non-destructive and objective discrimination of red onion samples. The selected fluorescence spectroscopic data were used to build models using algorithms from the groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The most satisfactory results were obtained using J48 and LMT (Logistic Model Tree) from the group of Trees, Multilayer Perceptron, and QDA (Quadratic Discriminant Analysis) from Functions, Naive Bayes from Bayes, Logit Boost from Meta, JRip from Rules, and LWL (Locally Weighted Learning) from Lazy. The average accuracy of discrimination of onion bulbs belonging to ‘Asenovgradska kaba’ and a red breeding line equal to 100% was found in the case of models developed using the LMT, Multilayer Perceptron, Naive Bayes, Logit Boost, and LWL algorithms. The TPR (True Positive Rate), Precision, and F-Measure of 1.000 and FPR (False Positive Rate) of 0.000, as well as the Kappa statistic of 1.0, were determined. The results revealed the usefulness of the approach combining fluorescence spectroscopy and machine learning to distinguish red onion cultivars and breeding lines.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1652-:d:937262
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    References listed on IDEAS

    as
    1. 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.
    2. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdoulghafor & Samir Brahim Belhaouari & Normahira Mamat & Shamsul Faisal Mohd Hussein, 2022. "Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review," Agriculture, MDPI, vol. 12(7), pages 1-35, July.
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    Cited by:

    1. Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan & Necati Çetin, 2023. "Rapid Detection of Changes in Image Textures of Carrots Caused by Freeze-Drying using Image Processing Techniques and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(8), pages 1-14, April.

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