SUFID: Sliced and Unsliced Fruits Images Dataset
Given the recent surge in online images of fruit, ever more sophisticated models and algorithms are required to organize, index, retrieve, and interact such data. However, the relative immaturity of the processes in current use is holding back development in the field. The current paper explains how images of ten classes of fruit namely apples, bananas, kiwis, lemons, oranges, pears, pineapples, coconuts, mangos and watermelons, presented both sliced and unsliced, were sourced from the Fruits360, FIDS30, and ImageNet datasets to create a single database, “SUFID”, containing 7,500 high-res images. Pre-processing was based on using pixel color distribution to determine whether each image was corrupt, or would fit the database. The paper describes the unique opportunities, principally within computer vision, presented by SUFID’s hierarchical structure, accuracy, diversity, and scale, all of which can be of use to food researchers. As the dataset and benchmarks are believed to be of benefit to all researchers in the field, they are offered gratis to support future study.
KeywordsSliced fruits Unsliced fruits Multiclass fruits Balanced fruits dataset
The financial support by the Deanship of Scientific Research at the Princess Nourah Bint Abdulrahman University. The authors would like to thank anonymous reviewers for their helpful comments. A version of this dataset with multiclass fruits was presented during in the 6th International Visual Informatics Conference 2019 (IVIC’19).
- 1.Bhargava, A., Bansal, A.: Fruits and vegetables quality evaluation using computer vision: a review. J. King Saud Univ. Inf. Sci. (2018)Google Scholar
- 3.Fruits 360 dataset. https://www.kaggle.com/moltean/fruits#fruits-360_dataset.zip
- 4.FIDS30 DataSet. http://www.vicos.si/Downloads/FIDS30
- 5.ImageNet. http://www.image-net.org
- 6.Ergun, H., Sert, M.: Fusing deep convolutional networks for large scale visual concept classification. In: 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), pp. 210–213 (2016)Google Scholar