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


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.


aquaculture business intelligence; big video data; image retrieval; mobile cloud computing; machine learning; ornamental fish warehousing system

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