SUFID: Sliced and Unsliced Fruits Images Dataset

  • Latifah Abdullah Bin TuraykiEmail author
  • Nirase Fathima Abubacker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11870)


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.


Sliced 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).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Latifah Abdullah Bin Turayki
    • 1
    Email author
  • Nirase Fathima Abubacker
    • 2
  1. 1.DCU @ Princess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia
  2. 2.Dublin City UniversityDublinIreland

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