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Detection of sweetness level for fruits (watermelon) with machine learning

The inspection and grading of the watermelon are done manually but it is a tedious job and it is difficult for the graders to maintain constant vigilance. Thus, the image processing has widely been used for identification, detection, grading and quality evaluation in the agricultural field. The...

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Main Authors: Wan Nazulan, Wan Nurul Suraya, Asnawi, Ani Liza, Mohd Ramli, Huda Adibah, Jusoh, Ahmad Zamani, Ibrahim, Siti Noorjannah, Mohamed Azmin, Nor Fadhillah
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2020
Subjects:
T10.5 Communication of technical information
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/86522/7/86522_Detection%20of%20Sweetness%20Level%20for%20Fruits%20_new.pdf
http://irep.iium.edu.my/86522/13/86522_Detection%20of%20Sweetness%20Level%20for%20Fruits_scopus.pdf
http://irep.iium.edu.my/86522/
https://ieeexplore.ieee.org/document/9289712
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spelling my.iium.irep.865222021-03-24T03:52:51Z http://irep.iium.edu.my/86522/ Detection of sweetness level for fruits (watermelon) with machine learning Wan Nazulan, Wan Nurul Suraya Asnawi, Ani Liza Mohd Ramli, Huda Adibah Jusoh, Ahmad Zamani Ibrahim, Siti Noorjannah Mohamed Azmin, Nor Fadhillah T10.5 Communication of technical information TK7885 Computer engineering The inspection and grading of the watermelon are done manually but it is a tedious job and it is difficult for the graders to maintain constant vigilance. Thus, the image processing has widely been used for identification, detection, grading and quality evaluation in the agricultural field. The objective of this work is to investigate the sweetness parameter for the fruit’s detection and classification algorithm in machine learnings. This study applies image processing techniques to detect the color and shape of watermelon’s skin for grading based on the sweetness level using K-means clustering method via the Python platform. 13 samples of watermelon images are used to test the functionality of the proposed detection system in this study. Then, each watermelon is grouped into Grade A (high level of sweetness), Grade B (medium level of sweetness), and Grade C (low level of sweetness) based on its color and shape detection results. At the end of this research, the proposed technique resulted in an inaccurate prediction for 2 watermelon samples out of 13 samples which indicates the system has an 84.62% accuracy in detecting the watermelon sweetness level. IEEE 2020 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/86522/7/86522_Detection%20of%20Sweetness%20Level%20for%20Fruits%20_new.pdf application/pdf en http://irep.iium.edu.my/86522/13/86522_Detection%20of%20Sweetness%20Level%20for%20Fruits_scopus.pdf Wan Nazulan, Wan Nurul Suraya and Asnawi, Ani Liza and Mohd Ramli, Huda Adibah and Jusoh, Ahmad Zamani and Ibrahim, Siti Noorjannah and Mohamed Azmin, Nor Fadhillah (2020) Detection of sweetness level for fruits (watermelon) with machine learning. In: 2020 IEEE Conference on Big Data and Analytics (ICBDA), 17-19 Nov. 2020, Kota Kinabalu, Malaysia (Online Conference). https://ieeexplore.ieee.org/document/9289712 10.1109/ICBDA50157.2020.9289712
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T10.5 Communication of technical information
TK7885 Computer engineering
spellingShingle T10.5 Communication of technical information
TK7885 Computer engineering
Wan Nazulan, Wan Nurul Suraya
Asnawi, Ani Liza
Mohd Ramli, Huda Adibah
Jusoh, Ahmad Zamani
Ibrahim, Siti Noorjannah
Mohamed Azmin, Nor Fadhillah
Detection of sweetness level for fruits (watermelon) with machine learning
description The inspection and grading of the watermelon are done manually but it is a tedious job and it is difficult for the graders to maintain constant vigilance. Thus, the image processing has widely been used for identification, detection, grading and quality evaluation in the agricultural field. The objective of this work is to investigate the sweetness parameter for the fruit’s detection and classification algorithm in machine learnings. This study applies image processing techniques to detect the color and shape of watermelon’s skin for grading based on the sweetness level using K-means clustering method via the Python platform. 13 samples of watermelon images are used to test the functionality of the proposed detection system in this study. Then, each watermelon is grouped into Grade A (high level of sweetness), Grade B (medium level of sweetness), and Grade C (low level of sweetness) based on its color and shape detection results. At the end of this research, the proposed technique resulted in an inaccurate prediction for 2 watermelon samples out of 13 samples which indicates the system has an 84.62% accuracy in detecting the watermelon sweetness level.
format Conference or Workshop Item
author Wan Nazulan, Wan Nurul Suraya
Asnawi, Ani Liza
Mohd Ramli, Huda Adibah
Jusoh, Ahmad Zamani
Ibrahim, Siti Noorjannah
Mohamed Azmin, Nor Fadhillah
author_facet Wan Nazulan, Wan Nurul Suraya
Asnawi, Ani Liza
Mohd Ramli, Huda Adibah
Jusoh, Ahmad Zamani
Ibrahim, Siti Noorjannah
Mohamed Azmin, Nor Fadhillah
author_sort Wan Nazulan, Wan Nurul Suraya
title Detection of sweetness level for fruits (watermelon) with machine learning
title_short Detection of sweetness level for fruits (watermelon) with machine learning
title_full Detection of sweetness level for fruits (watermelon) with machine learning
title_fullStr Detection of sweetness level for fruits (watermelon) with machine learning
title_full_unstemmed Detection of sweetness level for fruits (watermelon) with machine learning
title_sort detection of sweetness level for fruits (watermelon) with machine learning
publisher IEEE
publishDate 2020
url http://irep.iium.edu.my/86522/7/86522_Detection%20of%20Sweetness%20Level%20for%20Fruits%20_new.pdf
http://irep.iium.edu.my/86522/13/86522_Detection%20of%20Sweetness%20Level%20for%20Fruits_scopus.pdf
http://irep.iium.edu.my/86522/
https://ieeexplore.ieee.org/document/9289712
_version_ 1695530645378301952
score 13.211869

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