A Deep Learning Approach to Automated Treatment Classification in Tuna Processing: Enhancing Quality Control in Indonesian Fisheries
The Indonesian maritime territory harbors a rich diversity of marine resources, making up approximately 37% of global fish species diversity. Tuna, particularly in Maluku Province, stands out as a vital economic asset with growing production and export numbers. Current practices for processing and e...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Fishes |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2410-3888/10/2/75 |
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| Summary: | The Indonesian maritime territory harbors a rich diversity of marine resources, making up approximately 37% of global fish species diversity. Tuna, particularly in Maluku Province, stands out as a vital economic asset with growing production and export numbers. Current practices for processing and evaluating tuna meat, however, face significant limitations due to basic infrastructure and reliance on manual inspection methods, leading to potential contamination risks and treatment identification errors. This research addresses these challenges by implementing an advanced deep learning solution based on convolutional neural networks (CNNs) to automatically identify three distinct treatment categories for tuna loin: No-Treatment, CO-Treatment, and CS-Treatment. Trained on a comprehensive image dataset, the model demonstrated exceptional performance with 95% accuracy. While field testing confirmed the model’s strong performance in correctly identifying treatment categories, occasional classification errors highlighted areas for improvement in data preprocessing. This study provides a significant step forward in automated fish processing assessment technology, offering a promising solution to longstanding challenges in the marine processing industry. |
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| ISSN: | 2410-3888 |