Oil palm tree detection and counting in aerial images based on faster R-CNN
Malaysian oil palm industry has been a great contributor to the country’s creation of job opportunity, foreign exchange earnings and GDP. Information about the amount and the distribution of oil palm trees in a plantation are important for sustainable management. In this paper, we propose an oil pal...
| Main Authors: | , , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer, Singapore
2020
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30346/1/Oil%20palm%20tree%20detection%20and%20counting%20in%20aerial%20images%20%20.pdf |
| _version_ | 1796994411357274112 |
|---|---|
| author | Xinni, Liu Kamarul Hawari, Ghazali Fengrong, Han Izzeldin, I. Mohd Yue, Zhao Yuanfa, Ji |
| author_facet | Xinni, Liu Kamarul Hawari, Ghazali Fengrong, Han Izzeldin, I. Mohd Yue, Zhao Yuanfa, Ji |
| author_sort | Xinni, Liu |
| collection | UMP |
| description | Malaysian oil palm industry has been a great contributor to the country’s creation of job opportunity, foreign exchange earnings and GDP. Information about the amount and the distribution of oil palm trees in a plantation are important for sustainable management. In this paper, we propose an oil palm tree detection and counting method based on the Faster Regions with Convolutional Neural Network algorithm (Faster R-CNN). Experiment on the oil palm tree images collected by a drone shows that the proposed method can effectively detect the oil palm trees and counting its number when the age of the trees in a plantation is different from 2 years old to 8 years old. The proposed approach can be used to predict the scale of the plantation and meets the requirements of real-time detection. |
| first_indexed | 2024-03-06T12:47:24Z |
| format | Conference or Workshop Item |
| id | UMPir30346 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:47:24Z |
| publishDate | 2020 |
| publisher | Springer, Singapore |
| record_format | dspace |
| spelling | UMPir303462021-01-06T03:51:29Z http://umpir.ump.edu.my/id/eprint/30346/ Oil palm tree detection and counting in aerial images based on faster R-CNN Xinni, Liu Kamarul Hawari, Ghazali Fengrong, Han Izzeldin, I. Mohd Yue, Zhao Yuanfa, Ji TK Electrical engineering. Electronics Nuclear engineering Malaysian oil palm industry has been a great contributor to the country’s creation of job opportunity, foreign exchange earnings and GDP. Information about the amount and the distribution of oil palm trees in a plantation are important for sustainable management. In this paper, we propose an oil palm tree detection and counting method based on the Faster Regions with Convolutional Neural Network algorithm (Faster R-CNN). Experiment on the oil palm tree images collected by a drone shows that the proposed method can effectively detect the oil palm trees and counting its number when the age of the trees in a plantation is different from 2 years old to 8 years old. The proposed approach can be used to predict the scale of the plantation and meets the requirements of real-time detection. Springer, Singapore 2020-03-24 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30346/1/Oil%20palm%20tree%20detection%20and%20counting%20in%20aerial%20images%20%20.pdf Xinni, Liu and Kamarul Hawari, Ghazali and Fengrong, Han and Izzeldin, I. Mohd and Yue, Zhao and Yuanfa, Ji (2020) Oil palm tree detection and counting in aerial images based on faster R-CNN. In: InECCE2019: Proceedings of the 5th International Conference on Electrical, Control & Computer Engineering , 29th July 2019 , Kuantan, Pahang, Malaysia. pp. 475-482., 632. ISSN 1876-1100 ISBN 9789811523168 https://doi.org/10.1007/978-981-15-2317-5_40 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Xinni, Liu Kamarul Hawari, Ghazali Fengrong, Han Izzeldin, I. Mohd Yue, Zhao Yuanfa, Ji Oil palm tree detection and counting in aerial images based on faster R-CNN |
| title | Oil palm tree detection and counting in aerial images based on faster R-CNN |
| title_full | Oil palm tree detection and counting in aerial images based on faster R-CNN |
| title_fullStr | Oil palm tree detection and counting in aerial images based on faster R-CNN |
| title_full_unstemmed | Oil palm tree detection and counting in aerial images based on faster R-CNN |
| title_short | Oil palm tree detection and counting in aerial images based on faster R-CNN |
| title_sort | oil palm tree detection and counting in aerial images based on faster r cnn |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/30346/1/Oil%20palm%20tree%20detection%20and%20counting%20in%20aerial%20images%20%20.pdf |
| work_keys_str_mv | AT xinniliu oilpalmtreedetectionandcountinginaerialimagesbasedonfasterrcnn AT kamarulhawarighazali oilpalmtreedetectionandcountinginaerialimagesbasedonfasterrcnn AT fengronghan oilpalmtreedetectionandcountinginaerialimagesbasedonfasterrcnn AT izzeldinimohd oilpalmtreedetectionandcountinginaerialimagesbasedonfasterrcnn AT yuezhao oilpalmtreedetectionandcountinginaerialimagesbasedonfasterrcnn AT yuanfaji oilpalmtreedetectionandcountinginaerialimagesbasedonfasterrcnn |