Enhancing Fish Auction with Deep Learning and Computer Vision: Automated Caliber and Species Classification

The accurate labeling of species and size of specimens plays a pivotal role in fish auctions conducted at fishing ports. These labels, among other relevant information, serve as determinants of the objectivity of the auction preparation process, underscoring the indispensable nature of a reliable la...

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Bibliographic Details
Published inFishes Vol. 9; no. 4; p. 133
Main Authors Jareño, Javier, Bárcena-González, Guillermo, Castro-Gutiérrez, Jairo, Cabrera-Castro, Remedios, Galindo, Pedro L.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2024
Subjects
Automation
Biological research
Classification
Datasets
Deep learning
Fish
fish auction
Fish industry
fish size
fish species
Fisheries
Fishing
Machine learning
Machine vision
Neural networks
Technology application
transfer learning
Spain
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Summary:The accurate labeling of species and size of specimens plays a pivotal role in fish auctions conducted at fishing ports. These labels, among other relevant information, serve as determinants of the objectivity of the auction preparation process, underscoring the indispensable nature of a reliable labeling system. Historically, this task has relied on manual processes, rendering it vulnerable to subjective interpretations by the involved personnel, therefore compromising the value of the merchandise. Consequently, the digitization and implementation of an automated labeling system are proposed as a viable solution to this ongoing challenge. This study presents an automatic system for labeling species and size, leveraging pre-trained convolutional neural networks. Specifically, the performance of VGG16, EfficientNetV2L, Xception, and ResNet152V2 networks is thoroughly examined, incorporating data augmentation techniques and fine-tuning strategies. The experimental findings demonstrate that for species classification, the EfficientNetV2L network excels as the most proficient model, achieving an average F-Score of 0.932 in its automatic mode and an average F-Score of 0.976 in its semi-automatic mode. Concerning size classification, a semi-automatic model is introduced, where the Xception network emerges as the superior model, achieving an average F-Score of 0.949.
ISSN:2410-3888
2410-3888
DOI:10.3390/fishes9040133