COMPUTATIONAL VISION FOR TOMATO CLASSIFICATION USING A DECISION TREE ALGORITHM

Abstract

Computer vision systems combined with machine learning techniques have demonstrated success as alternatives to empirical methods for classification and selection. This study aimed to classify tomatoes based on their colorimetric characteristics, which influence consumer purchasing potential, using the decision tree algorithm. Tomatoes were categorized into two classes based on ripeness: Higher Purchasing Potential (20 fruits) and Lower Purchasing Potential (40 fruits). Images were captured in the RGB color model and converted to HSI and CIELab models. Principal component analysis was employed to evaluate the influence of colorimetric characteristics within each class, and the decision tree algorithm was applied to classify the fruits into the respective categories. Tomatoes in the Higher Purchasing Potential class were primarily influenced by red intensity and chromaticity a and b, while tomatoes in the Lower Purchasing Potential class were influenced by green intensity and hue. The decision tree achieved an accuracy of 83.6% and an F1-score of 90.9%, demonstrating its potential for classifying tomatoes based on colorimetric characteristics linked to consumer preferences.