Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO
Fruit harvesting robots have made headlines in the agricultural industry in recent years. A fruit recognition system would assist farmers or agricultural industry practitioners in lessening workloads while increasing crop yields. Due to the similar characteristics of chili fruits, approximating the...
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| Language: | en |
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Wydawnictwo SIGMA-NOT
2023
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28537/2/0104616102023393.pdf http://eprints.utem.edu.my/id/eprint/28537/ http://pe.org.pl/articles/2023/8/15.pdf |
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| author | Lee, Li Yin Zainudin, Muhammad Noorazlan Shah Mohd Saad, Wira Hidayat Sulaiman, Noor Asyikin Idris, Muhammad Idzdihar Kamarudin, Muhammad Raihaan Mohamed, Raihani Abd Razak, Muhd Shah Jehan |
| author_facet | Lee, Li Yin Zainudin, Muhammad Noorazlan Shah Mohd Saad, Wira Hidayat Sulaiman, Noor Asyikin Idris, Muhammad Idzdihar Kamarudin, Muhammad Raihaan Mohamed, Raihani Abd Razak, Muhd Shah Jehan |
| author_sort | Lee, Li Yin |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Fruit harvesting robots have made headlines in the agricultural industry in recent years. A fruit recognition system would assist farmers or agricultural industry practitioners in lessening workloads while increasing crop yields. Due to the similar characteristics of chili fruits, approximating the chili according to their grades and identifying its maturity will be difficult. Furthermore, because of their different appearances and sizes, distinguishing between the fruits and the leaves becomes difficult. As a result, a real-time object detection algorithm called You Only Look Once (YOLO) and Mask-RCNN is investigates in order to distinguish the fruit from its plant based on its shape and colour. YOLO version 5 (YOLOv5) uses to define and distinguish the chili fruits and its leaves based on two characteristics; shape and colour. The CSPDarknet network serves as the backbone in YOLOv5, where feature extraction and mosaic augmentation has used to combine multiple images into a single image. Total 391 images has divided into two subsets: training and testing, with an 80:20 ratio. YoLov5 is notable for its ability to detect small objects with high
precision in a short amount of time while Mask-RCNN has proven its ability to recognize a chili fruits with high precision above 90%. The classification is evaluated using precision, recall, loss function, and inference time. |
| format | Article |
| id | my.utem.eprints-28537 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2023 |
| publisher | Wydawnictwo SIGMA-NOT |
| record_format | eprints |
| spelling | my.utem.eprints-285372025-03-14T16:32:03Z http://eprints.utem.edu.my/id/eprint/28537/ Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO Lee, Li Yin Zainudin, Muhammad Noorazlan Shah Mohd Saad, Wira Hidayat Sulaiman, Noor Asyikin Idris, Muhammad Idzdihar Kamarudin, Muhammad Raihaan Mohamed, Raihani Abd Razak, Muhd Shah Jehan Fruit harvesting robots have made headlines in the agricultural industry in recent years. A fruit recognition system would assist farmers or agricultural industry practitioners in lessening workloads while increasing crop yields. Due to the similar characteristics of chili fruits, approximating the chili according to their grades and identifying its maturity will be difficult. Furthermore, because of their different appearances and sizes, distinguishing between the fruits and the leaves becomes difficult. As a result, a real-time object detection algorithm called You Only Look Once (YOLO) and Mask-RCNN is investigates in order to distinguish the fruit from its plant based on its shape and colour. YOLO version 5 (YOLOv5) uses to define and distinguish the chili fruits and its leaves based on two characteristics; shape and colour. The CSPDarknet network serves as the backbone in YOLOv5, where feature extraction and mosaic augmentation has used to combine multiple images into a single image. Total 391 images has divided into two subsets: training and testing, with an 80:20 ratio. YoLov5 is notable for its ability to detect small objects with high precision in a short amount of time while Mask-RCNN has proven its ability to recognize a chili fruits with high precision above 90%. The classification is evaluated using precision, recall, loss function, and inference time. Wydawnictwo SIGMA-NOT 2023 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/28537/2/0104616102023393.pdf Lee, Li Yin and Zainudin, Muhammad Noorazlan Shah and Mohd Saad, Wira Hidayat and Sulaiman, Noor Asyikin and Idris, Muhammad Idzdihar and Kamarudin, Muhammad Raihaan and Mohamed, Raihani and Abd Razak, Muhd Shah Jehan (2023) Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO. Przeglad Elektrotechniczny, 99 (8). pp. 92-97. ISSN 0033-2097 http://pe.org.pl/articles/2023/8/15.pdf 10.15199/48.2023.08.15 |
| spellingShingle | Lee, Li Yin Zainudin, Muhammad Noorazlan Shah Mohd Saad, Wira Hidayat Sulaiman, Noor Asyikin Idris, Muhammad Idzdihar Kamarudin, Muhammad Raihaan Mohamed, Raihani Abd Razak, Muhd Shah Jehan Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO |
| title | Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO |
| title_full | Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO |
| title_fullStr | Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO |
| title_full_unstemmed | Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO |
| title_short | Analysis recognition of ghost pepper and cili-padi using Mask-RCNN and YOLO |
| title_sort | analysis recognition of ghost pepper and cili-padi using mask-rcnn and yolo |
| url | http://eprints.utem.edu.my/id/eprint/28537/2/0104616102023393.pdf http://eprints.utem.edu.my/id/eprint/28537/ http://pe.org.pl/articles/2023/8/15.pdf |
| url_provider | http://eprints.utem.edu.my/ |