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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Raseeda Hamzah
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering Engineering

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Multi-stage hybrid YOLO-driven and MobileNetV2-CNN variants for robust fish freshness classification Raseeda Hamzah; Rosniza Roslan; Amni Munira Khidir; Lala Septem Riza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3660-3671

Abstract

This study presents a multi-stage hybrid representation learning framework for robust fish freshness classification to address the critical challenge of reliable quality assessment in unpreserved food supply chains. The proposed pipeline operates in two stages: stage-1 employs you only look once (YOLO)v8n as feature-gated detector to validate inputs and eliminate non-fish images, while stage-2 leverages transfer-learned MobileNetV2 variants enhanced with convolutional neural network (CNN) layers, fine-tuning, adaptive learning rate schedulers, and expanded fully connected layers for hierarchical classification into three freshness classes i.e., highly fresh, fresh, and not fresh. The framework has been trained and evaluated on two curated datasets comprising 4,500 fish and non-fish images and 11,111 freshness-labeled including augmented dataset to increase diversity. The experimental results showed significant improvements. The best performance model transfer learning (TL)-MobileNetV2 + CNN + fine-tuning achieved 98.07% training accuracy and 67.72% validation accuracy on 80:20 split, and training accuracy of 97.57%, validation accuracy of 97.21% through 10-fold cross-validation. The comparative benchmarking confirmed that dual-stage design outperformed baseline MobileNetV2 and YOLOv5s models across precision, recall, and F1-score. The findings highlighted significant value of integrating detection-driven validation with transfer-learning classification, and propose new benchmark for intelligent freshness monitoring. For future work, this study aims to explore attention-based models, data integration, and species diversity.
Co-Authors Ali Abd Almisreb Alia Mohd Azri Aliya Hasanah Binti Mohd Mushin Amir Hamzah Jaafar Amni Munira Khidir Fatin Natasya Shuhaimi Jibril Adamu Khyrina Airin Fariza Abu Samah Lala Septem Riza Marshima Mohd Rosli Mastura Md Saad Megat Nabil Irwan Megat Amerudin Mohamad Soffi Abd Razak Mohd Suffian Sulaiman N. Md Din Nasiroh Omar Nor Aiza Moketar Nordin Abu Bakar Nurqueen Sayang Dinnie Wirakarnain Nursuriati Jamil Rosniza Roslan Shuzlina Abdul-Rahman Siti Khatijah Nor Abdul Rahim Syed Mohd Zahid Syed Zainal Ariffin Zainab Othman Zalilah Abd Aziz