Categorization of Dehydrated Food through Hybrid Deep Transfer Learning Techniques

  • Sm Nuruzzaman Nobel Bangladesh University of Business and Technology, Dhaka, Bangladesh
  • Md. Anwar Hussen Wadud Bangladesh University of Business and Technology, Dhaka, Bangladesh
  • Anichur Rahman National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350, Bangladesh
  • Dipanjali Kundu National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350, Bangladesh
  • Airin Afroj Aishi Daffodil International University, Dhaka, Bangladesh
  • Sadia Sazzad National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350, Bangladesh
  • Muaz Rahman National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350, Bangladesh
  • Md Asif Imran Bangladesh University of Business and Technology, Dhaka, Bangladesh
  • Omar Faruque Sifat Bangladesh University of Business and Technology, Dhaka, Bangladesh
  • Mohammad Sayduzzaman National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350, Bangladesh
  • T M Amir Ul Haque Bhuiyan Bangladesh University of Business and Technology, Dhaka, Bangladesh
Keywords: Dry Food, VGG16, ResNet50, Classification, Datasets, Hybrid, Deep Learning, Transfer Learning

Abstract

The essentiality of categorizing dry foods plays a crucial role in maintaining quality control and ensuring food safety for human consumption. The effectiveness and precision of classification methods are vital for enhanced evaluation of food quality and streamlined logistics. To achieve this, we gathered a dataset of 11,500 samples from Mendeley and proceeded to employ various transfer learning models, including VGG16 and ResNet50. Additionally, we introduce a novel hybrid model, VGG16-ResNet, which combines the strengths of both architectures. Transfer learning involves utilizing knowledge acquired from one task or domain to enhance learning and performance in another. By fusing multiple Deep Learning techniques and transfer learning strategies, such as VGG16-ResNet50, we developed a robust model capable of accurately classifying a wide array of dry foods. The integration of Deep Learning (DL) and transfer learning techniques in the context of dry food classification signifies a drive towards automation and increased efficiency within the food industry. Notably, our approach achieved remarkable results, achieving a classification accuracy of 99.78% for various dry food images, even when dealing with limited training data for VGG16-ResNet50.
Published
2024-02-28
How to Cite
Nobel , S. N., Md. Anwar Hussen Wadud, Rahman, A., Dipanjali Kundu, Airin Afroj Aishi, Sadia Sazzad, Muaz Rahman, Md Asif Imran, Omar Faruque Sifat, Mohammad Sayduzzaman, & T M Amir Ul Haque Bhuiyan. (2024). Categorization of Dehydrated Food through Hybrid Deep Transfer Learning Techniques. Statistics, Optimization & Information Computing, 12(4), 1004-1018. https://doi.org/10.19139/soic-2310-5070-1896
Section
Research Articles