Skip to content
Research Article Open access CC BY 4.0

Digitization in Detecting Fish Freshness Quality: Literature Review

Fittrie Meyllianawaty Pratiwy, Aisyah ., Yuniar Mulyani

Asian Journal of Fisheries and Aquatic Research · pp. 141–147 · Published 26 Dec 2021

10.9734/ajfar/2021/v15i630359

Abstract

Fish contain a lot of nutrients that are very good for the body, but fish are often traded in a postmortem state with poor quality. One of the reasons is the difficulty of detecting the quality of fish freshness. Many studies currently make applications and special tools to see the fish quality in the digitalization of tools to facilitate human work. The method used is by identifying digital images, acoustic techniques, and electronic noses. In digital image identification, the fish’s eye image is applied by taking pictures of the condition of the fish on a smartphone device and then processing it using the small square method or curve fitting. After that, the identification of the acoustic form utilizes sound waves emitted to the observed target. In addition, the electric nose method is carried out by using the nose's working principle, which receives a response to odors that arise from rotting fish.

Digitization fish freshness fish quality fishery.

References (15)

  1. 1 Modelling Techniques to Improve the Quality of Food Using Artificial Intelligence [DOI]
  2. 2 The Use of Different Image Recognition Techniques in Food Safety: A Study [DOI]
  3. 3 Artificial Bee Colony Algorithm for Fresh Food Distribution without Quality Loss by Delivery Route Optimization [DOI]
  4. 4 Profil Asam Amino, Asam Lemak dan Komponen Volatil Ikan Gurame Segar (Osphronemus gouramy) dan Kukus [DOI]
  5. 5 Safety of Food and Food Warehouse Using VIBHISHAN [DOI]
  6. 6 Sistem Pendeteksi Kesegaran Ikan Bandeng Menggunakan Citra [DOI]
  7. 7 Dualistic feeding pattern of Nile tilapia (Oreochromis niloticus, L.) reared under different self-feeding system conditions [DOI]
  8. 8 STUDI KUALITAS IKAN SEGAR SECARA ORGANOLEPTIK YANG DIPASARKAN DI KABUPATEN JENEPONTO [DOI]
  9. 9 PERANCANGAN SISTEM PENENTUAN TINGKAT KESEGARAN IKAN CAKALANG MENGGUNAKAN METODE CURVE FITTING BERBASIS CITRA DIGITAL MATA IKAN [DOI]
  10. 10 Aplikasi Penentuan Tingkat Kesegaran Ikan Selar Berbasis Citra Digital Dengan Metode Kuadrat Terkecil [DOI]
  11. 11 Aplikasi Metode Akustik untuk Uji Kesegaran Ikan [DOI]
  12. 12 Pembentukan Formaldehida Alami dan Penurunan Mutu Ikan Kerapu Cantik (Epinephelus fuscoguttatus × E. microdon) selama Penyimpanan pada Suhu Beku [DOI]
  13. 13 Effectiveness of the self-feeding method on growth performance of Nile tilapia, Oreochromis niloticus [DOI]
  14. 14 PENENTUAN RUMUS PEMBUSUKAN IKAN MENGGUNKAN METODE CURVE FITTING DENGAN PENDEKATAN PENGOLAHAN CITRA TERHADAP CITRA DIGITAL INSANG IKAN [DOI]
  15. 15 10.53842/juki.v2i1 [DOI]

Cited by 2

FishID+: A Smart Explainable AI and Multi-Sensor IoT Framework for Sustainable Fish Species Identification and Freshness Prediction

Lisna Thomas, Saviya Varghese · International Journal of Technology and Emerging Research · 2026

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

26

Page views

0

PDF downloads

3

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.