Image Classification of Red Dragon Fruit Ripeness Levels Using HSV Color Moments and the K-NN Algorithm

Accurately determining the ripeness level of red dragon fruit (Hylocereus polyrhizus) is crucial for ensuring post-harvest quality and distribution efficiency. This study proposes a method for classifying red dragon fruit ripeness using color moment features in the HSV color space combined with the K-Nearest Neighbor (K-NN) algorithm. The dataset consists of 2,881 images of dragon fruit with a resolution of 800×800 pixels, categorized into three classes: ripe (886 images), unripe (1,241 images), and rotten (754 images). All images were captured under natural lighting conditions and underwent pre-processing to enhance color value consistency. Color features were extracted by calculating the mean, standard deviation, and skewness of the Hue, Saturation, and Value channels. The K-NN model was trained and tested on data randomly split in an 80:20 ratio. The testing results showed that the model achieved 100% accuracy in classifying the ripeness levels, demonstrating the effectiveness of this non-destructive method in distinguishing fruit ripeness. This approach holds strong potential to support efficient and consistent decision-making in the agricultural sector.