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355026

CLASSIFICATION OF DATES QUALITY USING DEEP LEARNING TECHNOLOGY BASED ON CAPTURED IMAGES

Article

Last updated: 26 Dec 2024

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Subjects

-

Tags

Processing Engineering of Agricultural Products

Abstract

Dates are a common fruit in many Middle Eastern and African nations and have religious and cultural value. one of the key elements in judging the quality of dates is sorting according to their health state. Combining rejected dates with accepted ones causes significant economic losses in both storage and exportation. Despite being a crucial stage for obtaining high-quality dates and reducing losses, this sorting process is still conducted using traditional methods. Thus, this study aims to classify date fruit quality (accepted or rejected) with machine learning technology to reduce cost, time, and improve the quality of final product. In this study, several Convolutional Neural Network architectures (Inception-v3, Inception-ResNet-v2, VGG19) were used to classify three varieties of date fruit (Mejdool, Saiedi, El-Wadi). These varieties were classified into accepted and rejected samples to build the dataset im-ages. An Arduino Automatic mobile camera shutter controller captured the dataset images. In addition to the Kaggle dataset which was added to the accepted images. The total dataset consisted of 5,945 images, comprising 3,142 accepted images and 2,803 rejected images. By comparing the results of different architectures, Inception-ResNet-v2 demonstrated the best performance, achieving an accuracy of 98.99% and a loss of 0.0344. Therefore, it can be concluded that the Inception-ResNet-v2 model could be utilized to develop a suitable computer vision system, thereby enhancing the date sorting process and facilitating the packaging of high-quality dates.

DOI

10.21608/mjae.2024.286079.1137

Keywords

AI techniques, Convolutional neural network (CNN), Transfer Learning, Date Fruits Handling, Rejected Fruits

Authors

First Name

Waleed

Last Name

ElHelew

MiddleName

Kamel

Affiliation

Assoc. Prof., Ag. Eng. Dept., Fac. of Ag., Ain Shams U., Al Qalyubiyah, Egypt.

Email

walid.kamel.elhelew@agr.asu.edu.eg

City

القاهرة

Orcid

0000-0002-2904-2568

First Name

Dalia

Last Name

Abo-Bbakr

MiddleName

-

Affiliation

Head of Res. at the Plant Pathology Research Institute., Ag. Res. Center (A. R. C)., Giza, Egypt.

Email

dalia_abobbakr@agr.asu.edu.eg

City

Cairo

Orcid

-

First Name

Sahar

Last Name

Zayan

MiddleName

-

Affiliation

Head of Res. at the Plant Pathology Research Institute., Ag. Res. Center (A. R. C)., Giza, Egypt.

Email

drsahar.abdo@gmail.com

City

-

Orcid

-

First Name

Muhammad

Last Name

Mayhoub

MiddleName

Ahmad

Affiliation

Assist. Prof., Ag. Eng. Dept., Fac. of Ag., Ain Shams U., Al Qalyubiyah, Egypt.

Email

mohamedmayhoub@agr.asu.edu.eg

City

Cairo

Orcid

0000-0002-1422-289X

Volume

41

Article Issue

3

Related Issue

48718

Issue Date

2024-07-01

Receive Date

2024-04-29

Publish Date

2024-07-01

Print ISSN

1687-384X

Online ISSN

2636-3062

Article File

MJAE3550261719781200.pdf

PDF . 1.1MB

Link

https://mjae.journals.ekb.eg/article_355026.html

Detail API

https://mjae.journals.ekb.eg/service?article_code=355026

Order

355,026

Type

Original Article

Type Code

1,326

Publication Type

Journal

Publication Title

Misr Journal of Agricultural Engineering

Publication Link

https://mjae.journals.ekb.eg/

MainTitle

CLASSIFICATION OF DATES QUALITY USING DEEP LEARNING TECHNOLOGY BASED ON CAPTURED IMAGES

Details

Type

Article

Created At

26 Dec 2024