Tomato classification according to organoleptic maturity (coloration) using machine learning algorithms K-NN, MLP, and K-Means Clustering

This article presents the design, development, implementation and evaluation of different machine learning type algorithms, for Milano and Chonto tomatoes classification, based on the fruit physical characteristics, such as coloring (maturity degree), taking as reference national and international standards (NTC-1103-1 and USDA, respectively). Different digital image processing techniques are shown, used to describe and extract the characteristics of color statistics of the tomatoes images. For data analysis, supervised and /or trained classification algorithms were implemented with databases and features in the RGB, HSI and L*a*b* color spaces. The techniques for classification used and valued were: K-NN (K-Nearest Neighbors), MLP type Neuronal Networks (Multilayer Perceptron) and unsupervised learning algorithms like K-Means. The evaluation of each classification algorithms is shown, using the global confusion matrix, together with performance indices such as accuracy, precision, sensitivity, and specificity.