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Differentiation of Yeast-Inoculated and Uninoculated Tomatoes Using Fluorescence Spectroscopy Combined with 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)

  • Vanya Slavova

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

  • Kadir Sabanci

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

  • Muhammet Fatih Aslan

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

  • Veselina Masheva

    (Department of Plant Genetic Resources, Institute of Plant Genetic Resources “Konstantin Malkov”—Sadovo, Agricultural Academy Bulgaria, 2, Drouzhba Str., 4122 Sadovo, Bulgaria)

  • Mariana Petkova

    (Department of Microbiology and Environmental Biotechnology, Agricultural University, 12 Mendeleev St, 4002 Plovdiv, Bulgaria)

Abstract

Artificial-intelligence-based analysis methods can provide objective and accurate results. This study aimed to evaluate the performance of machine learning algorithms to classify yeast-inoculated and uninoculated tomato samples using fluorescent spectroscopic data. For this purpose, three different tomato types were used: ‘local dwarf’, ‘Picador’, and ‘Ideal’. Discrimination analysis was applied with six different machine learning (ML) algorithms. Confusion matrices, average accuracies, F-Measure, Precision, ROC (receiver operating characteristic) Area, MCC (Matthews Correlation Coefficient), and precision-recall area values obtained as a result of the application of different ML algorithms were compared. Based on the fluorescence spectroscopic data, the application of six ML algorithms showed that the first two tomato types were classified with 100% accuracy and the last type was classified with 95% accuracy. The results of the study show that the fluorescence spectroscopy data are strongly representative of tomato species. ML methods fed with these data provide high-performance discrimination.

Suggested Citation

  • Ewa Ropelewska & Vanya Slavova & Kadir Sabanci & Muhammet Fatih Aslan & Veselina Masheva & Mariana Petkova, 2022. "Differentiation of Yeast-Inoculated and Uninoculated Tomatoes Using Fluorescence Spectroscopy Combined with Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-12, November.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:11:p:1887-:d:968155
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    Cited by:

    1. Dasen Li & Zhendong Yin & Yanlong Zhao & Wudi Zhao & Jiqing Li, 2023. "MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization," Agriculture, MDPI, vol. 13(6), pages 1-15, May.

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