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Öğe The application of fluorescence spectroscopy and machine learning as non-destructive approach to distinguish two different varieties of greenhouse tomatoes(Springer, 2023) Slavova, Vanya; Ropelewska, Ewa; Sabanci, KadirThe application of interdisciplinary non-invasive diagnostic methods combining fluorescence spectroscopy with multiple machine learning algorithms as tools for rapid application in tomato breeding programs is essential when crossing specific genotypes or parental samples to obtain representatives with better performance. Non-destructive distinguishing tomato species is of great importance for the preservation of product quality. This study aimed at combining fluorescence spectroscopic data and machine learning algorithms for distinguishing greenhouse tomatoes. The models for the discrimination of greenhouse tomato samples were built based on selected spectroscopic data using different machine learning algorithms from the groups of Meta, Functions, Bayes, Trees, Rules, and Lazy. The confusion matrices with accuracy for each sample, average accuracy, time taken to build the model, Kappa statistic, mean absolute error, root mean squared error and relative absolute error were determined. The greenhouse tomato samples were discriminated with an accuracy reaching 100% for the models built using Multi-Class Classifier (Meta), Logistic (Function), Bayes Net (Bayes), PART (Rules), and J48 (Trees). In the case of these algorithms, Kappa statistic was 1.0 and mean absolute error, root mean squared error and relative absolute error were equal to 0.Öğe The classification of leek seeds based on fluorescence spectroscopic data using machine learning(Springer, 2023) Ropelewska, Ewa; Sabanci, Kadir; Slavova, Vanya; Genova, StefkaThe 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.Öğe A comparative evaluation of Bayes, functions, trees, meta, rules and lazy machine learning algorithms for the discrimination of different breeding lines and varieties of potato based on spectroscopic data(Springer Science and Business Media Deutschland GmbH, 2022) Slavova, Vanya; Ropelewska, Ewa; Sabancı, Kadir; Aslan, Muhammet Fatih; Nacheva, Emilia K.The objective of this study was to compare the usefulness of machine learning algorithms for distinguishing the potato lines and varieties based on selected fluorescence spectroscopic data. The potato tubers belonging to two breeding lines S 617 and S 716 and two varieties Trezor and Sante were examined. The discrimination analysis was performed using machine learning algorithms from different groups. The average accuracies, confusion matrices, and the F-Measure, Precision, PRC (Precision-Recall) Area, ROC (Receiver Operating Characteristic) Area and MCC (Matthews Correlation Coefficient) values obtained for models built using different algorithms were compared. The breeding lines and varieties of potato were discriminated with very high average accuracies equal up to 95% for the SMO (Sequential Minimal Optimization) algorithms (group of Functions), Naive Bayes (group of Bayes), Hoeffding Tree (group of Trees), Multi Class Classifier (group of Meta), PART (group of Rules), IBk (Instance-Based Learning with parameter k) (group of Lazy). Models developed with the use of selected algorithms allowed for distinguishing some potato lines and varieties with an accuracy of up to 100% and the values of the F-Measure, Precision, PRC Area, ROC Area and MCC reaching 1.000.Öğe Differentiation of yeast-inoculated and uninoculated tomatoes using fluorescence spectroscopy combined with machine learning(MDPI, 2022) Ropelewska, Ewa; Slavova, Vanya; Sabancı, Kadir; Aslan, Muhammet Fatih; Masheva, Veselina; Petkova, MarianaArtificial-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.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, StefkaDrought 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.Öğe The use of fluorescence spectroscopic data and machine-learning algorithms to discriminate red onion cultivar and breeding line(MDPI, 2022) Sabancı, Kadir; Aslan, Muhammet Fatih; Slavova, VanyaThe 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 nondestructive 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.
