Analysis of the Impact of Data Augmentation on the Performance of Deep Learning Models in Multispectral Food Authenticity Identification

• Autorzy: Zhang Yaru , Yilmaz Arif , Popa Mirela , Brewster Christopher

Identyfikatory

DOI

10.15439/2023F3643

• Języki publikacji: EN

Abstrakty

EN

Food authenticity is a significant concern in the meat industry, demanding effective detection methods. This study explores the use of multispectral imaging (MSI) and deep learning for meat adulteration detection. We evaluate different deep learning models using transfer learning and preprocessing techniques in a multi-level adulteration classification task. In addition, we propose a novel approach called one-band mixed augmentation for band selection in MSI data, which outperforms traditional reflectance-based feature selection and enhances model robustness. Furthermore, employing the nine-crop approach for dataset augmentation improved the accuracy from 0.63 to 0.74 for DenseNet201 model without transfer learning. This research contributes to advancing food safety assessment practices and provides insights into the application of deep learning for preventing food adulteration. The proposed one-band mixed augmentation approach offers a novel strategy for handling band selection challenges in MSI data analysis.

Słowa kluczowe

EN

deep learning; industry; analytical model; multispectral imaging; transfer learning; data augmentation; data model

PL

głębokie uczenie; przemysł; model analityczny; obrazowanie wielospektralne; uczenie transferowe; rozszerzanie danych; model danych

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 823--832
• Opis fizyczny: Bibliogr. 24 poz., il., tab., wykr.

Twórcy

autor

Zhang Yaru

• Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, 6200 MD, The Netherlands

autor

Yilmaz Arif

• Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, 6200 MD, The Netherlands

autor

Popa Mirela

• Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, 6200 MD, The Netherlands

autor

Brewster Christopher

• Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, 6200 MD, The Netherlands

Bibliografia

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Uwagi

1. Thematic Tracks Regular Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).