Traditional food knowledge of Indonesia: a new high-quality food dataset and automatic recognition system

Ari Wibisono, Hanif Arief Wisesa, Zulia Putri Rahmadhani, Puteri Khatya Fahira, Petrus Mursanto, Wisnu Jatmiko

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Traditional food knowledge (TFK) is an essential aspect of human life. In terms of sociocultural aspects, TFK is necessary to protect ancestral culture. In terms of health, traditional foods contain better and more natural ingredients compared to the ingredients of processed foods. Considering this background, in this study, data acquisition and automatic food recognition were performed for traditional food in Indonesia. The food images were captured in a professional mini studio. The food image data were captured under the same light intensity, camera settings, and shooting distance from the camera. The parameters were precisely measured and configured with a light intensity meter, adjustable lighting, and a laser distance measurement device. The data of 1644 traditional food images were successfully obtained in the data acquisition process. These images corresponded to 34 types of traditional foods, and 30–50 images were obtained for each type of food. The size of the raw food image data was 53 GB. The data were divided into sets for training, testing, and validation. An automatic recognition system was developed to classify the traditional food of Indonesia. Training was performed using several types of convolutional neural network (CNN) models such as Densenet121, Resnet50, InceptionV3, and Nasnetmobile. The evaluation results indicated that when using a high quality dataset, the automatic recognition system could realize satisfactory area under the receiver operating characteristics (AUROC) and high accuracy, precision, and recall values of more than 0.95.

Original languageEnglish
Article number69
JournalJournal of Big Data
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • Convolutional neural network
  • Food recognition
  • High-quality dataset
  • Traditional food knowledge

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