Fruits (Banana and Guava) datasets for non-destructive quality classifications

This article provides fruit (banana and guava) image dataset for non-destructive quality classifications. The images were captured with a Redmi Note 10-Pro mobile camera in natural sunlight. All the images were captured at different angles and saved in JPG format. A total of 1738 images were collected. The images were classified into three different classes; Class A, Class B, and Defect Quality, according to the maturity stages of fruits. Further, the image datasets were saved into their respective folder. This dataset allows researchers to study different machine learning and deep learning algorithms for the quality classification of fruits. The dataset provides the foundation for the future study of fruit physiological behaviors underlying postharvest enzymatic browning. Because, in the later stage of the ripening the degradation of fruit peel color from yellow to brown is the major issue in the supply chain and the storage industries.