Prediction of fish quality level with machine learning

Summary In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was 7 l...

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Bibliographic Details
Published in:International journal of food science & technology 2022-08, Vol.57 (8), p.5250-5255
Main Authors: Yavuzer, Emre, Köse, Memduh
Format: Article
Language:English
Subjects:
Cold storage
Digital cameras
Fish
Food identification
fresh fish
Freshness
Learning algorithms
Machine learning
quality changes
Sea bass
Software
Species
teachable machine
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Summary:Summary In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was 7 log cfu per g. They were uploaded on a web‐based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.
ISSN:0950-5423
1365-2621
DOI:10.1111/ijfs.15853