The application of fluorescence spectroscopy and machine learning as non-destructive approach to distinguish two different varieties of greenhouse tomatoes

The 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.