Developing an ornamental fish warehousing system based on big video data

Chao Lieh Chen, Chia Chun Chang, Chao Chun Chen, Ting Shuo Chang, Xu Hua Zeng, Jing Wen Liu, Zhu Wei Wang, Wei Cheng Lu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


We have developed an ornamental fish warehousing (OFWare) system based on big video data. The system is an application paradigm of information and communication technologies for traditional industries, specifically in the fields of aquaculture and agriculture. Live creatures are the main products of these industries, raising challenges for warehouse management. Warehousing of high unit-price ornamental fishes such as koi, stingray, and arowana is even more difficult since, in addition to counting and classification, such warehousing requires the identification of individual animals whose shapes and texture patterns vary as they grow . Therefore, rather than using invasive RFID-based systems, we combine mobile cloud computing and big data analytics techniques including image and video collection and transmission using handheld mobile devices, unsupervised texture pattern classification of fish tank videos, fish image retrieval, and statistical analysis. The proposed system is scalable based on a Hadoop framework and a small set of a single name-nodes and data-nodes can identify a particular fish among 500,000 koi in 7 seconds. The proposed warehousing system can form the basis for the development of breeding histories, anti-forgery certificate, and aquaculture business intelligence.

Original languageEnglish
Pages (from-to)79-83
Number of pages5
JournalInternational Journal of Automation and Smart Technology
Issue number2
Publication statusPublished - 2018

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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