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Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking

USB Palm Oil Counting (Unstripped Bunch) is important in oil palm processing mills. Information on the number of USBs is essential because it shows the level of efficiency of the palm oil processing plant. Due to its complexity, palm oil mills rely solely on manual calculations, which is inefficient...

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Main Authors: Wahyu, Sapto Aji, Kamarul Hawari, Ghazali, Son Ali, Akbar
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/41993/1/Automatic%20Oil%20Palm%20Unstripped%20Bunch_ABST.pdf
http://umpir.ump.edu.my/id/eprint/41993/2/Automatic%20Oil%20Palm%20Unstripped%20Bunch.pdf
http://umpir.ump.edu.my/id/eprint/41993/
https://doi.org/10.1109/IC2SE52832.2021.9792068
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spelling my.ump.umpir.419932024-07-18T02:53:41Z http://umpir.ump.edu.my/id/eprint/41993/ Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking Wahyu, Sapto Aji Kamarul Hawari, Ghazali Son Ali, Akbar TK Electrical engineering. Electronics Nuclear engineering USB Palm Oil Counting (Unstripped Bunch) is important in oil palm processing mills. Information on the number of USBs is essential because it shows the level of efficiency of the palm oil processing plant. Due to its complexity, palm oil mills rely solely on manual calculations, which is inefficient workforce waste. Challenging aspects such as partial occlusion, overlap, and even different perspectives limit the use of traditional computer vision techniques. In recent years, deep learning has become increasingly popular for computer vision applications due to its superior performance over conventional methods. This paper proposes a deep learning solution to solve USB computing problems in palm oil mills. Our proposed automated USB counter system consists of an object detector built on the RCNN Faster architecture and an object tracker made in the euclidean distance. Our proposed system identifies and counts USBs with an average accuracy of 71.5% in testing. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41993/1/Automatic%20Oil%20Palm%20Unstripped%20Bunch_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/41993/2/Automatic%20Oil%20Palm%20Unstripped%20Bunch.pdf Wahyu, Sapto Aji and Kamarul Hawari, Ghazali and Son Ali, Akbar (2021) Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking. In: 2nd International Conference on Computer Science and Engineering: The Effects of the Digital World After Pandemic (EDWAP), IC2SE 2021. 2nd International Conference on Computer Science and Engineering, IC2SE 2021 , 16 - 18 November 2021 , Padang, Indonesia. pp. 1-5.. ISBN 978-166540045-9 (Published) https://doi.org/10.1109/IC2SE52832.2021.9792068
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
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Wahyu, Sapto Aji
Kamarul Hawari, Ghazali
Son Ali, Akbar
Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking
description USB Palm Oil Counting (Unstripped Bunch) is important in oil palm processing mills. Information on the number of USBs is essential because it shows the level of efficiency of the palm oil processing plant. Due to its complexity, palm oil mills rely solely on manual calculations, which is inefficient workforce waste. Challenging aspects such as partial occlusion, overlap, and even different perspectives limit the use of traditional computer vision techniques. In recent years, deep learning has become increasingly popular for computer vision applications due to its superior performance over conventional methods. This paper proposes a deep learning solution to solve USB computing problems in palm oil mills. Our proposed automated USB counter system consists of an object detector built on the RCNN Faster architecture and an object tracker made in the euclidean distance. Our proposed system identifies and counts USBs with an average accuracy of 71.5% in testing.
format Conference or Workshop Item
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 Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking
title_short Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking
title_full Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking
title_fullStr Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking
title_full_unstemmed Automatic oil palm unstripped bunch (USB) counting system based on faster RCNN and object tracking
title_sort automatic oil palm unstripped bunch (usb) counting system based on faster rcnn and object tracking
publisher IEEE
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/41993/1/Automatic%20Oil%20Palm%20Unstripped%20Bunch_ABST.pdf
http://umpir.ump.edu.my/id/eprint/41993/2/Automatic%20Oil%20Palm%20Unstripped%20Bunch.pdf
http://umpir.ump.edu.my/id/eprint/41993/
https://doi.org/10.1109/IC2SE52832.2021.9792068
_version_ 1822924519170375680
score 13.232414

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