Automated determination of watermelon ripeness based on image color segmentation and rind texture analysis

Watermelons are popularly grown and consumed in most tropical areas of agricultural countries especially in the Asian countries. Quality control is important to standardize the production especially the procedure of automatic system based on computer vision. In this paper, therefore, we objectively investigated the ripeness of watermelon based on color segmentation using k-means clustering and rind texture analysis using Laplacian of Gaussian (LoG) filter. We captured each image of 20 watermelons (Kinnaree variety), which are divided into ten ripe and unripe groups by an experienced farmer. Different experimental conditions were compared to achieve the optimal outcome. The experimental results showed that the proposed features could extract different ripeness levels statistically with p < 0.001.