Apple Classification Based on MRI Images Using VGG16 Convolutional Deep Learning Model

DOI

10.2991/978-94-6463-196-8_10How to use a DOI?

Keywords

Apple; VGG16; Magnetic Resonance Imaging (MRI); Deep learning

Abstract

Apples are considered one of the healthiest fruits worldwide. With the increase in demand for apple, internal quality checking is a most challenging task. In Digital Image Processing different computer vision technologies are used to identify external defects like color, shape, and texture. MRI of apple fruit is the most effective non-destructive and non-invasive method to identify internal defects. In our present study, we have used our own dataset of 196 MRI images of apples. Further, these images are divided into 80:20 for training and testing. These images are classified by using the pre-trained deep learning model VGG16 and with this model, we got 66.21% of validation accuracy and 62.5% of testing accuracy.

Copyright

© 2023 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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TY  - CONF
AU  - D. Vidya
AU  - Shivanand Rumma
AU  - Mallikarjun Hangargi
PY  - 2023
DA  - 2023/08/10
TI  - Apple Classification Based on MRI Images Using VGG16 Convolutional Deep Learning Model
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 114
EP  - 121
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_10
DO  - 10.2991/978-94-6463-196-8_10
ID  - Vidya2023
ER  -