Recognition of Multiple Panamanian Watermelon Varieties Based on Feature Extraction Analysis

Abstract

In this paper we present a multi-watermelon varieties recognition analysis using the color and texture information. The goal is to classify the watermelon taking into account its features, despite its variety. To improve the efficacy of the proposed method the images were preprocessed using morphological and adaptive threshold methods. Also, to extract the candidate region for feature extraction, vertical and horizontal histograms were used. Finally, the model was tested on a group of watermelons to prove the effectiveness of the implementation; the result shows it is capable of classifying watermelon varieties by just considering the presence or not of stripes, as well as the color information. The importance of this study is that it uses as test subjects local varieties (Panamanian) of watermelons, thus helping the knowledge of national export products with computer vision.