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    The classification of leek seeds based on fluorescence spectroscopic data using machine learning
    (Springer, 2023) Ropelewska, Ewa; Sabanci, Kadir; Slavova, Vanya; Genova, Stefka
    The objective of this study was to distinguish leek seeds belonging to the Starozagorski kamush variety and two breeding lines based on the selected fluorescence spectroscopic data. The classification models were developed for three classes of Starozagorski kamush vs. breeding line 4 vs. breeding line 39 and pairs of classes of Starozagorski kamush vs. breeding line 4, Starozagorski kamush vs. breeding line 39, and breeding line 4 vs. breeding line 39. The traditional machine learning algorithms, such as PART, Logistic, Naive Bayes, Random Forest, IBk, and Filtered Classifier were applied. All three classes were distinguished with an average accuracy of up to 93.33% for models built using IBk and Filtered Classifier. In the case of each model, Starozagorski kamush variety was completely different (accuracy of 100%, precision, and F-measure, MCC (Matthews correlation coefficient), and ROC (receiver operating characteristic) area of 1.000) from breeding lines, and the mixing of cases was observed between breeding line 4 and breeding line 39. The models built for pairs of leek seed classes distinguished Starozagorski kamush and breeding line 4 with an average accuracy reaching 100% (Logistic, Naive Bayes, Random Forest, IBk). The classification accuracy of Starozagorski kamush and breeding line 39 also reached 100% (Logistic, Naive Bayes, Random Forest, IBk), whereas breeding line 4 and breeding line 39 were classified with an average accuracy of up to 80% (Logistic, Naive Bayes, Random Forest, Filtered Classifier). The proposed approach combining fluorescence spectroscopy and machine learning may be used in practice to distinguish leek seed varieties and breeding lines.
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    Öğe
    Discrimination of onion subjected to drought and normal watering mode based on fluorescence spectroscopic data
    (Elsevier B.V., 2022) Ropelewska, Ewa; Slavova, Vanya; Sabancı, Kadir; Aslan, Muhammet Fatih; Cai, Xiang; Genova, Stefka
    Drought stress can affect the yield and quality of cultivated plants. The deficit of water may result in the physiological and anatomical reactions at organ, tissue and cellular levels of the plant species. The objective of this study was to discriminate different onion samples with the use of innovative models based on fluorescence spectroscopic data using different classifiers. The onion growing under drought and normal watering conditions were compared. Additionally, the five different samples of onion including three varieties (Konkurent bql, Asenovgradska kaba, Trimoncium) and two lines (white, red) subjected to both the drought mode and normal watering mode were differentiated. The results were evaluated based on confusion matrices, average accuracies, and the values of TP (True Positive) Rate, FP (False Positive) Rate, Precision, F-Measure, ROC (Receiver Operating Characteristic) Area and PRC (Precision-Recall) Area. In the case of the discrimination of two classes: drought mode and normal watering mode, an average accuracy reached 100% for white line of onion for a model built using the Naive Bayes, Multilayer Perceptron, JRip and LMT classifiers and for red line of onion for all used classifiers (Naive Bayes, Multilayer Perceptron, IBk, Multi Class Classifier, JRip, LMT). The values of TP Rate, Precision, F-Measure, ROC Area and PRC Area were equal to 1.000, and FP Rate was 0.000. For onion samples subjected to drought, five classes including the Konkurent, Asenovgradska kaba, Trimoncium varieties and the white and red lines were discriminated with an average accuracy of up to 90% for the LMT classifier. The same classes of samples but subjected to normal watering were correctly distinguished in 84% for the Naive Bayes classifier.