Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques

Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques

In this study, image segmentation on Ficus Carica (fig) was developed. Fig fruit image segmentation separates fruit objects by removing the background in the image, including shadow images, and extracting the fruit shape. The developed methods for fig image segmentation were evaluated to identify ho...

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Published in:2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022
Main Author: Md Rosli N.E.; Setumin S.; Nugroho A.; Che Ani A.I.; Ikmal Fitri Maruzuki M.; Osman M.S.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142422750&doi=10.1109%2feSmarTA56775.2022.9935411&partnerID=40&md5=8b10e727631f0c5f490bc09b8463a561
id 2-s2.0-85142422750
spelling 2-s2.0-85142422750
Md Rosli N.E.; Setumin S.; Nugroho A.; Che Ani A.I.; Ikmal Fitri Maruzuki M.; Osman M.S.
Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques
2022
2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022


10.1109/eSmarTA56775.2022.9935411
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142422750&doi=10.1109%2feSmarTA56775.2022.9935411&partnerID=40&md5=8b10e727631f0c5f490bc09b8463a561
In this study, image segmentation on Ficus Carica (fig) was developed. Fig fruit image segmentation separates fruit objects by removing the background in the image, including shadow images, and extracting the fruit shape. The developed methods for fig image segmentation were evaluated to identify how well the methods work and were compared to find the best method for fig image segmentation. As a reference, ground truth was made using software called Procreate for comparative purposes. There were three methods used in this paper that include Threshold, K-means clustering, and Sharp U-Net. The platform used for this development is Google Colab. Based on the results obtained, the Sharp U-Net demonstrates the highest value of accuracy as compared to the Threshold and K-means Clustering techniques. Therefore, the most effective and efficient method to use on fig fruit image segmentation is the Sharp U-Net method. © 2022 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Md Rosli N.E.; Setumin S.; Nugroho A.; Che Ani A.I.; Ikmal Fitri Maruzuki M.; Osman M.S.
spellingShingle Md Rosli N.E.; Setumin S.; Nugroho A.; Che Ani A.I.; Ikmal Fitri Maruzuki M.; Osman M.S.
Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques
author_facet Md Rosli N.E.; Setumin S.; Nugroho A.; Che Ani A.I.; Ikmal Fitri Maruzuki M.; Osman M.S.
author_sort Md Rosli N.E.; Setumin S.; Nugroho A.; Che Ani A.I.; Ikmal Fitri Maruzuki M.; Osman M.S.
title Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques
title_short Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques
title_full Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques
title_fullStr Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques
title_full_unstemmed Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques
title_sort Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques
publishDate 2022
container_title 2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022
container_volume
container_issue
doi_str_mv 10.1109/eSmarTA56775.2022.9935411
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142422750&doi=10.1109%2feSmarTA56775.2022.9935411&partnerID=40&md5=8b10e727631f0c5f490bc09b8463a561
description In this study, image segmentation on Ficus Carica (fig) was developed. Fig fruit image segmentation separates fruit objects by removing the background in the image, including shadow images, and extracting the fruit shape. The developed methods for fig image segmentation were evaluated to identify how well the methods work and were compared to find the best method for fig image segmentation. As a reference, ground truth was made using software called Procreate for comparative purposes. There were three methods used in this paper that include Threshold, K-means clustering, and Sharp U-Net. The platform used for this development is Google Colab. Based on the results obtained, the Sharp U-Net demonstrates the highest value of accuracy as compared to the Threshold and K-means Clustering techniques. Therefore, the most effective and efficient method to use on fig fruit image segmentation is the Sharp U-Net method. © 2022 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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