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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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science (IAES)
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41996/ http://umpir.ump.edu.my/id/eprint/41996/1/Oil%20palm%20unstripped%20bunch%20detector%20using%20modified%20faster%20regional%20convolutional%20neural%20network.pdf |
| _version_ | 1848826490879213568 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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). |
| first_indexed | 2025-11-15T03:45:39Z |
| format | Article |
| id | ump-41996 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:45:39Z |
| publishDate | 2022 |
| publisher | Institute of Advanced Engineering and Science (IAES) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/41996/ http://umpir.ump.edu.my/id/eprint/41996/ http://umpir.ump.edu.my/id/eprint/41996/ http://umpir.ump.edu.my/id/eprint/41996/1/Oil%20palm%20unstripped%20bunch%20detector%20using%20modified%20faster%20regional%20convolutional%20neural%20network.pdf |