The ontology model for selecting quality melons uses hidden semantic data based on melon knowledge domains

A quality-oriented melon selection process is an important factor in increasing consumers willingness to pay. However, unlike most fruits, melons are usually sold whole, making it difficult to choose a perfectly ripe and sweet melon, as skin color and texture play an important role in this. From a marketing perspective, the selling power of melons is influenced by demand and supply. This paper presents a new approach by utilizing hidden semantic data on melon images derived from digital images. Semantic information on the surface of the melon is described, extracted, and shared as domain knowledge using an ontology approach that allows the relationship between parameters to produce descriptions and actions in determining the quality of the melon. Using K-Nearest Neighbor (KNN) to model the data that has been obtained from the visual data extraction process An information description approach using an ontology guided by KNN in determining the quality of the proposed melons can assist farmers in classifying quality melons at reasonable prices with limited human involvement.