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Sequence Segmentation of Nematodes in Atlantic Cod with Multispectral Imaging Data.

Andrea Rakel Sigurðardóttir¹, Hildur Inga Sveinsdóttir^1,2, Nette Schultz³

¹Faculty of Food Science and Nutrition, University of Iceland, Sæmundargata 12, 102 Reykjavík, Iceland.

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|September 28, 2024

Summary

This study introduces an automated method for detecting nematodes in fish fillets using multispectral imaging (MSI) and machine learning. The system achieved 88% precision and 79% recall, enhancing fish product safety and quality.

Keywords:

fish fillet inspection image processing multispectral imaging nematode detection

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Area of Science:

Food Science and Technology
Image Processing
Machine Learning

Background:

Nematode contamination is a significant issue in the fish processing industry, especially for white fish.
Current manual inspection methods for nematodes are labor-intensive and prone to errors.
Technological advancements are needed to improve the efficiency and accuracy of nematode detection in fish fillets.

Purpose of the Study:

To develop and evaluate an automated system for detecting and differentiating nematodes from other defects in fish fillets.
To investigate the potential of multispectral imaging (MSI) and machine learning for identifying nematodes based on their spectral signatures.
To improve process control and product safety in the fish processing industry.

Main Methods:

Acquisition of 270 multispectral images of Atlantic cod fillets using VideometerLab 4.
Automated segmentation of nematodes using the Segment Anything Model (SAM) with minimal labeled data.
Development of segmentation models using normalized Canonical Discriminant Analysis (nCDA) to distinguish nematodes from skin remnants and blood spots.
Evaluation of model performance using precision and recall metrics.

Main Results:

Successfully labeled 173 nematodes using the Segment Anything Model (SAM).
Achieved 88% precision and 79% recall in identifying nematodes in the annotated test data.
Demonstrated the potential of MSI and nCDA for accurate nematode differentiation in fish fillets.

Conclusions:

The developed automated system using MSI and nCDA shows promise for improving nematode detection in fish processing.
This approach can enhance product safety and quality by accurately identifying contaminated fillets.
The system can reduce unnecessary inspections, optimizing the processing workflow.