Non-destructive classification of melon sweetness levels using segmented rind properties based on semantic segmentation models

Melon is one of the most consumed crops worldwide and has high marketability. Consumers prefer sweet melons. However, the nondestructive determination of melon sweetness is challenging because of its thick rind. In this study, we presented a novel approach for predicting melon sweetness levels using features extracted from segmented rind images and machine learning techniques. We extracted various features from melon rinds images, such as the net density, net thickness, and rind color, using a semantic segmentation model. These features were used as factors in grading melon quality. Experiments on various machine learning models showed that the one-dimensional convolutional neural network model achieved the best performance with 85.71% accuracy, 96.00% precision, and 87.27% F-score. Moreover, It indicated that the sweetness classification performance over a binary class (combining sweet and ‘very sweet’ classes into one class) achieved better result than over multiple classes.