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Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN

Identifying Unstriped Bunches (USB) is a pivotal challenge in palm oil production, contributing to reduced mill efficiency. Existing manual detection methods are proven time-consuming and prone to inaccuracies. Therefore, we propose an innovative solution harnessing computer vision technology. Speci...

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Main Authors: Wahyu, Sapto Aji, Kamarul Hawari, Ghazali, Son, Ali Akbar
Format: Article
Language:English
Published: Universitas Muhammadiyah Yogyakarta 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/40352/1/Oil%20palm%20USB_Unstripped%20Bunch_%20detector%20trained%20on%20synthetic%20images%20generated%20by%20PGGAN.pdf
http://umpir.ump.edu.my/id/eprint/40352/
https://doi.org/10.18196/jrc.v4i5.19499
https://doi.org/10.18196/jrc.v4i5.19499
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spelling my.ump.umpir.403522024-02-14T06:32:52Z http://umpir.ump.edu.my/id/eprint/40352/ Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN Wahyu, Sapto Aji Kamarul Hawari, Ghazali Son, Ali Akbar TK Electrical engineering. Electronics Nuclear engineering Identifying Unstriped Bunches (USB) is a pivotal challenge in palm oil production, contributing to reduced mill efficiency. Existing manual detection methods are proven time-consuming and prone to inaccuracies. Therefore, we propose an innovative solution harnessing computer vision technology. Specifically, we leverage the Faster R-CNN (Region-based Convolution Neural Network), a robust object detection algorithm, and complement it with Progressive Growing Generative Adversarial Networks (PGGAN) for synthetic image generation. Nevertheless, a scarcity of authentic USB images may hinder the application of Faster R-CNN. Herein, PGGAN is assumed to be pivotal in generating synthetic images of Empty Fruit Bunches (EFB) and USB. Our approach pairs synthetic images with authentic ones to train the Faster R-CNN. The VGG16 feature generator serves as the architectural backbone, fostering enhanced learning. According to our experimental results, USB detectors that were trained solely with authentic images resulted in an accuracy of 77.1%, which highlights the potential of this methodology. However, employing solely synthetic images leads to a slightly reduced accuracy of 75.3%. Strikingly, the fusion of authentic and synthetic images in a balanced ratio of 1:1 fuels a remarkable accuracy surge to 87.9%, signifying a 10.1% improvement. This innovative amalgamation underscores the potential of synthetic data augmentation in refining detection systems. By amalgamating authentic and synthetic data, we unlock a novel dimension of accuracy in USB detection, which was previously unattainable. This contribution holds significant implications for the industry, ensuring further exploration into advanced data synthesis techniques and refining detection models. Universitas Muhammadiyah Yogyakarta 2023 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/40352/1/Oil%20palm%20USB_Unstripped%20Bunch_%20detector%20trained%20on%20synthetic%20images%20generated%20by%20PGGAN.pdf Wahyu, Sapto Aji and Kamarul Hawari, Ghazali and Son, Ali Akbar (2023) Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN. Journal of Robotics and Control (JRC), 4 (5). 677 -685. ISSN 2715-5072. (Published) https://doi.org/10.18196/jrc.v4i5.19499 https://doi.org/10.18196/jrc.v4i5.19499
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Wahyu, Sapto Aji
Kamarul Hawari, Ghazali
Son, Ali Akbar
Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN
description Identifying Unstriped Bunches (USB) is a pivotal challenge in palm oil production, contributing to reduced mill efficiency. Existing manual detection methods are proven time-consuming and prone to inaccuracies. Therefore, we propose an innovative solution harnessing computer vision technology. Specifically, we leverage the Faster R-CNN (Region-based Convolution Neural Network), a robust object detection algorithm, and complement it with Progressive Growing Generative Adversarial Networks (PGGAN) for synthetic image generation. Nevertheless, a scarcity of authentic USB images may hinder the application of Faster R-CNN. Herein, PGGAN is assumed to be pivotal in generating synthetic images of Empty Fruit Bunches (EFB) and USB. Our approach pairs synthetic images with authentic ones to train the Faster R-CNN. The VGG16 feature generator serves as the architectural backbone, fostering enhanced learning. According to our experimental results, USB detectors that were trained solely with authentic images resulted in an accuracy of 77.1%, which highlights the potential of this methodology. However, employing solely synthetic images leads to a slightly reduced accuracy of 75.3%. Strikingly, the fusion of authentic and synthetic images in a balanced ratio of 1:1 fuels a remarkable accuracy surge to 87.9%, signifying a 10.1% improvement. This innovative amalgamation underscores the potential of synthetic data augmentation in refining detection systems. By amalgamating authentic and synthetic data, we unlock a novel dimension of accuracy in USB detection, which was previously unattainable. This contribution holds significant implications for the industry, ensuring further exploration into advanced data synthesis techniques and refining detection models.
format Article
author Wahyu, Sapto Aji
Kamarul Hawari, Ghazali
Son, Ali Akbar
author_facet Wahyu, Sapto Aji
Kamarul Hawari, Ghazali
Son, Ali Akbar
author_sort Wahyu, Sapto Aji
title Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN
title_short Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN
title_full Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN
title_fullStr Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN
title_full_unstemmed Oil palm USB (Unstripped Bunch) detector trained on synthetic images generated by PGGAN
title_sort oil palm usb (unstripped bunch) detector trained on synthetic images generated by pggan
publisher Universitas Muhammadiyah Yogyakarta
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40352/1/Oil%20palm%20USB_Unstripped%20Bunch_%20detector%20trained%20on%20synthetic%20images%20generated%20by%20PGGAN.pdf
http://umpir.ump.edu.my/id/eprint/40352/
https://doi.org/10.18196/jrc.v4i5.19499
https://doi.org/10.18196/jrc.v4i5.19499
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score 13.232414

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