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Upadhyay, Aniruddh Dev, Choudhury, Tanupriya, Sarkar, Tanmay, Bansal, Nikunj and Khurana, Madhu 
ORCID: 0000-0003-3976-1256
(2023)
Image analysis aided freshness classification of pool barb fish (Puntius sophore).
In: International Conference on Emerging Trends in Expert Applications & Security, 17-19 February 2023, Jaipur, India.
ISSN 2367-3370
ISBN 9789819919451
Abstract
Fish is a very nutritious dish that is consumed worldwide as a complete meal. This causes a rise in fish production and storage. Freshness of fish that is stored in ice boxes and deep freezers can degrade very quickly. A stale fish can cause great harm to a human ingesting it as it may carry many diseases. This paper aims to present a hybrid model that can recognise Puntius (commonly Puti Fish) as fresh or stale by using an image and certain values graded on scale of 10 like color, texture, etc. This mainly aims to a non-destructive approach to classify fishes as fresh or stale. The model comprises of a CNN for processing images and a Dense network that extracts information from the numerical data. These features are combined and are then further passed onto another dense neural network that performs the final classification task. We were able to achieve 96–98% accuracy with this model.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Food safety; Image analysis; Artificial intelligence; Food technology |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software Q Science > QL Zoology |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Rhiannon Goodland |
| Date Deposited: | 31 Oct 2023 15:46 |
| Last Modified: | 30 Nov 2023 15:36 |
| URI: | https://eprints.glos.ac.uk/id/eprint/12968 |
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