Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)

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EfficientNet B0-Based RLDA for Beef and Pork Image Classification

Authors
Ahmad Taufiq Akbar1, *, Shoffan Saifullah1, 2, Hari Prapcoyo1, Rochmat Husaini1, Bagus Muhammad Akbar1
1Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta, Yogyakarta, 55281, Indonesia
2Faculty of Computer Science, AGH University of Krakow, Krakow, 30-059, Poland
*Corresponding author. Email: ahmadtaufiq.akbar@upnyk.ac.id
Corresponding Author
Ahmad Taufiq Akbar
Available Online 2 February 2024.
DOI
10.2991/978-94-6463-366-5_13How to use a DOI?
Keywords
Transfer Learning; Beef Classification; EfficientNet; Image Analysis; Feature Extraction
Abstract

This study employs a novel approach to enhance the classification of beef and pork images using EfficientNet B0 as a feature extractor and Regularized Linear Discriminant Analysis (RLDA) for analysis. The integration of EfficientNet B0 and RLDA significantly improves beef recognition performance. In rigorous 5-fold cross-validation, the approach achieves an impressive 98.75% accuracy for 128x128 pixel images and 99% for 256x256 pixel datasets. Additionally, a 90% training data and 10% testing data split results in an accuracy rate of 100% for 128x128 pixel images and a perfect 100% accuracy for the 256x256 pixel dataset. These results signify a substantial advancement in beef quality assessment and classification, particularly in varying lighting conditions. EfficientNet B0 is a practical feature extractor, allowing the model to capture critical characteristics of beef images. RLDA, a regularized approach, further refines the classification process, improving the model’s accuracy and robustness. This research offers promising implications for applications in beef quality assessment, focusing on accuracy and adaptability across diverse environmental conditions.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)
Series
Advances in Intelligent Systems Research
Publication Date
2 February 2024
ISBN
978-94-6463-366-5
ISSN
1951-6851
DOI
10.2991/978-94-6463-366-5_13How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ahmad Taufiq Akbar
AU  - Shoffan Saifullah
AU  - Hari Prapcoyo
AU  - Rochmat Husaini
AU  - Bagus Muhammad Akbar
PY  - 2024
DA  - 2024/02/02
TI  - EfficientNet B0-Based RLDA for Beef and Pork Image Classification
BT  - Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)
PB  - Atlantis Press
SP  - 136
EP  - 145
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-366-5_13
DO  - 10.2991/978-94-6463-366-5_13
ID  - Akbar2024
ER  -