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Oil palm unstripped bunch detector using modified faster regional convolutional neural network

The palm oil processing industry in Malaysia and Indonesia is significant and plays a vital role in the community's welfare. The efficiency of palm oil mills is characterized by the low number of unstripped bunch (USBs), so USB detection is essential in the palm oil production process. So far,...

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Main Authors: Wahyu, Sapto Aji, Kamarul Hawari, Ghazali, Son Ali, Akbar
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science (IAES) 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/41996/1/Oil%20palm%20unstripped%20bunch%20detector%20using%20modified%20faster%20regional%20convolutional%20neural%20network.pdf
http://umpir.ump.edu.my/id/eprint/41996/
http://doi.org/10.11591/ijai.v11.i1.pp189-200
http://doi.org/10.11591/ijai.v11.i1.pp189-200
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spelling my.ump.umpir.419962024-07-18T03:03:50Z http://umpir.ump.edu.my/id/eprint/41996/ Oil palm unstripped bunch detector using modified faster regional convolutional neural network Wahyu, Sapto Aji Kamarul Hawari, Ghazali Son Ali, Akbar TK Electrical engineering. Electronics Nuclear engineering The palm oil processing industry in Malaysia and Indonesia is significant and plays a vital role in the community's welfare. The efficiency of palm oil mills is characterized by the low number of unstripped bunch (USBs), so USB detection is essential in the palm oil production process. So far, USB detection is done manually and is often ignored because it is labor-intensive. We developed a USB detector based on faster regional convolutional neural network with a modified visual geometry group 16 (VGG16) backbone to solve this problem. To see the performance of our proposed USB detector, we compared it to the faster region based convolutional neural networks (R-CNN) USB detector with the VGG16 standard backbone. Based on the validation test, the USB faster R-CNN detector with modified VGG16 can improve the performance of the USB faster R-CNN detection system based on the original VGG 16 backbone. The proposed system can work faster (100% faster) with an mAP value of 0.782 (7.42% more precise) than the USB Detector with the original VGG16. In the training process, the proposed system on the speed parameter has better training parameters, which is 58.9% faster, the total loss is smaller (43.4% smaller), and the proposed system has better best accuracy (98%) than the previous system (93%). Still, it has a smaller overlap bounding box (23.91% less). Institute of Advanced Engineering and Science (IAES) 2022-03 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/41996/1/Oil%20palm%20unstripped%20bunch%20detector%20using%20modified%20faster%20regional%20convolutional%20neural%20network.pdf Wahyu, Sapto Aji and Kamarul Hawari, Ghazali and Son Ali, Akbar (2022) Oil palm unstripped bunch detector using modified faster regional convolutional neural network. IAES International Journal of Artificial Intelligence (IJ-AI), 11 (1). pp. 189-200. ISSN 2252-8938. (Published) http://doi.org/10.11591/ijai.v11.i1.pp189-200 http://doi.org/10.11591/ijai.v11.i1.pp189-200
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 unstripped bunch detector using modified faster regional convolutional neural network
description The palm oil processing industry in Malaysia and Indonesia is significant and plays a vital role in the community's welfare. The efficiency of palm oil mills is characterized by the low number of unstripped bunch (USBs), so USB detection is essential in the palm oil production process. So far, USB detection is done manually and is often ignored because it is labor-intensive. We developed a USB detector based on faster regional convolutional neural network with a modified visual geometry group 16 (VGG16) backbone to solve this problem. To see the performance of our proposed USB detector, we compared it to the faster region based convolutional neural networks (R-CNN) USB detector with the VGG16 standard backbone. Based on the validation test, the USB faster R-CNN detector with modified VGG16 can improve the performance of the USB faster R-CNN detection system based on the original VGG 16 backbone. The proposed system can work faster (100% faster) with an mAP value of 0.782 (7.42% more precise) than the USB Detector with the original VGG16. In the training process, the proposed system on the speed parameter has better training parameters, which is 58.9% faster, the total loss is smaller (43.4% smaller), and the proposed system has better best accuracy (98%) than the previous system (93%). Still, it has a smaller overlap bounding box (23.91% less).
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 unstripped bunch detector using modified faster regional convolutional neural network
title_short Oil palm unstripped bunch detector using modified faster regional convolutional neural network
title_full Oil palm unstripped bunch detector using modified faster regional convolutional neural network
title_fullStr Oil palm unstripped bunch detector using modified faster regional convolutional neural network
title_full_unstemmed Oil palm unstripped bunch detector using modified faster regional convolutional neural network
title_sort oil palm unstripped bunch detector using modified faster regional convolutional neural network
publisher Institute of Advanced Engineering and Science (IAES)
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/41996/1/Oil%20palm%20unstripped%20bunch%20detector%20using%20modified%20faster%20regional%20convolutional%20neural%20network.pdf
http://umpir.ump.edu.my/id/eprint/41996/
http://doi.org/10.11591/ijai.v11.i1.pp189-200
http://doi.org/10.11591/ijai.v11.i1.pp189-200
_version_ 1822924519510114304
score 13.232414

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