<![CDATA[Watermelon (Citrullus vulgaris Schard) is a widely produced fruit due to its high nutritional value and health benefits. However, consumers often experience difficulty in distinguishing sweet and bland watermelons because quality assessment is generally conducted manually and subjectively. To address this issue, this study proposes a watermelon flavor classification system based on visual features, including color, texture, and shape, using an Artificial Neural Network approach with digital image processing. The dataset used in this study consists of 214 images collected from 55 watermelon samples, categorized into sweet and bland classes. The proposed method involves several stages, namely image acquisition, preprocessing, grayscale conversion, segmentation, morphological operations, feature extraction, and classification using a feedforward backpropagation learning algorithm. Various combinations of visual features were evaluated to determine the most effective configuration. Experimental results show that the proposed system achieves an accuracy of 93.67% on training data and 92.85% on testing data, with an average computation time of 0.319 seconds per image. The findings indicate that the integration of Hue Saturation Value color features, texture features derived from the Gray-Level Co-occurrence Matrix, and shape features significantly enhances the accuracy of watermelon flavor classification. This study contributes to the development of an objective, efficient, and non-destructive fruit quality assessment system and demonstrates potential applicability to other types of fruits using a similar approach.]]>
