DaFiF: A complete dataset for fish's freshness problems

Eko Prasetyo*, Nanik Suciati, Ni Putu Sutramiani, Adiananda Adiananda, Ayu Putu Wiweka Krisna Dewi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The fish are incorporated with ice to preserve their freshness when sold on the market. Ordinary people can only detect its freshness with some basic freshness knowledge. Therefore, non-destructive fish freshness inspection is an innovative solution to help. This dataset provides a medium to develop a system for non-destructive detection of fish freshness. There are three data variations: sensor data, images, and organoleptic examination. This dataset includes three fish species: mackerel, tilapia, and tuna, using 21 fish of each species. Data generation was carried out for 11 days, where 800 MQ (Metal Oxide) 135 and TGS (Taguchi Gas Sensor) 2602 sensor data and 80 images were generated every day. Organoleptic examinations were carried out using the Indonesian National Standard (SNI) 2729-2013 on six parameters: eyes, gills, body surface mucus, meat, smell, and body textures. This dataset can be used to develop a fish freshness detection system, regression modeling to estimate the deterioration in fish freshness, and standard grouping of freshness classes.

Original languageEnglish
Article number111016
JournalData in Brief
Volume57
DOIs
Publication statusPublished - Dec 2024

Keywords

  • Fish
  • Freshness
  • Images
  • Organoleptic
  • Sensor Data

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