International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 10, Issue 12 (December 2023), Pages: 203-210

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 Original Research Paper

Deep transfer learning CNN based for classification quality of organic vegetables

 Author(s): 

 Suksun Promboonruang, Thummarat Boonrod *

 Affiliation(s):

 Digital Technology Department, Faculty of Administrative Science, Kalasin University, Nuea, Thailand

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0009-0006-6014-2803

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2023.12.022

 Abstract

This study introduces a system based on a Convolutional Neural Network (CNN) with deep transfer learning for classifying organic vegetables. It aims to evaluate their quality through artificial intelligence. The approach involves three key steps: collecting data, preparing data, and creating data models. Initially, the data collection phase involves gathering images of organic vegetables from packing facilities, organizing these images into training, testing, and validation datasets. In the preparation phase, image processing techniques are applied to adjust the images for training and testing, resizing each to 224 x 224 pixels. The modeling phase involves using these prepared datasets, which include 3,239 images of two types of organic vegetables, to train the model. The study tests the model's effectiveness using three CNN architectures: Inception V3, VGG16, and ResNet50. It finds that the Inception V3 model achieves the highest accuracy at 85%, VGG16 follows with 82% accuracy, and ResNet50 has the lowest accuracy at 50%. The results suggest that Inception V3 is the most effective at accurately classifying organic vegetables, while VGG16 shows some limitations in certain categories, and ResNet50 is the least effective.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Deep learning, Transfer learning, Classification, Organic vegetables

 Article history

 Received 17 July 2023, Received in revised form 25 November 2023, Accepted 9 December 2023

 Acknowledgment 

This research project was allocated subsidies from the science supported by "Fundamental Fund: FF2023" and Kalasin University.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Promboonruang S and Boonrod T (2023). Deep transfer learning CNN based for classification quality of organic vegetables. International Journal of Advanced and Applied Sciences, 10(12): 203-210

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5

 Tables

 Table 1 Table 2 

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