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Automatic detection of oil palm tree from UAV images based on the deep learning method

Palm oil is a major contributor to Malaysia’s GDP in the agriculture sector. The sheer vastness of oil palm plantations requires a huge effort to administer. An oil palm plantation in regards to the irrigation process, fertilization, and planning for planting new trees require an audit process to co...

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Main Authors: Xinni, Liu, Kamarul Hawari, Ghazali, Fengrong, Han, Izzeldin, I. Mohd
Format: Article
Language:English
English
Published: Bellwether Publishing, Ltd. 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/30344/1/Automatic%20detection%20of%20oil%20palm%20tree%20from%20UAV.pdf
http://umpir.ump.edu.my/id/eprint/30344/2/Automatic%20detection%20of%20oil%20palm%20tree%20from%20UAV_FULL.pdf
http://umpir.ump.edu.my/id/eprint/30344/
https://doi.org/10.1080/08839514.2020.1831226
https://doi.org/10.1080/08839514.2020.1831226
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spelling my.ump.umpir.303442021-01-06T02:56:04Z http://umpir.ump.edu.my/id/eprint/30344/ Automatic detection of oil palm tree from UAV images based on the deep learning method Xinni, Liu Kamarul Hawari, Ghazali Fengrong, Han Izzeldin, I. Mohd TK Electrical engineering. Electronics Nuclear engineering Palm oil is a major contributor to Malaysia’s GDP in the agriculture sector. The sheer vastness of oil palm plantations requires a huge effort to administer. An oil palm plantation in regards to the irrigation process, fertilization, and planning for planting new trees require an audit process to correctly count the oil palm trees. Currently, the audit is done manually using aerial view images. Therefore, an effective and efficient method is imperative. This paper proposes a new automatic end-to-end method based on deep learning (DL) for detection and counting oil palm trees from images obtained from unmanned aerial vehicle (UAV) drone. The acquired images were first cropped and sampled into small size of sub-images, which were divided into a training set, a validation set, and a testing set. A DL algorithm based on Faster-RCNN was employed to build the model, extracts features from the images and identifies the oil palm trees, and gives information on the respective locations. The model was then trained and used to detect individual oil palm tree based on data from the testing set. The overall accuracy of oil palm tree detection was measured from three different sites with 97.06%, 96.58%, and 97.79% correct oil palm detection. The results show that the proposed method is more effective, accurate detection, and correctly counts the number of oil palm trees from the UAV images. Bellwether Publishing, Ltd. 2021 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30344/1/Automatic%20detection%20of%20oil%20palm%20tree%20from%20UAV.pdf pdf en http://umpir.ump.edu.my/id/eprint/30344/2/Automatic%20detection%20of%20oil%20palm%20tree%20from%20UAV_FULL.pdf Xinni, Liu and Kamarul Hawari, Ghazali and Fengrong, Han and Izzeldin, I. Mohd (2021) Automatic detection of oil palm tree from UAV images based on the deep learning method. Applied Artificial Intelligence, 35 (1). pp. 13-24. ISSN 0883-9514 https://doi.org/10.1080/08839514.2020.1831226 https://doi.org/10.1080/08839514.2020.1831226
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP 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
Xinni, Liu
Kamarul Hawari, Ghazali
Fengrong, Han
Izzeldin, I. Mohd
Automatic detection of oil palm tree from UAV images based on the deep learning method
description Palm oil is a major contributor to Malaysia’s GDP in the agriculture sector. The sheer vastness of oil palm plantations requires a huge effort to administer. An oil palm plantation in regards to the irrigation process, fertilization, and planning for planting new trees require an audit process to correctly count the oil palm trees. Currently, the audit is done manually using aerial view images. Therefore, an effective and efficient method is imperative. This paper proposes a new automatic end-to-end method based on deep learning (DL) for detection and counting oil palm trees from images obtained from unmanned aerial vehicle (UAV) drone. The acquired images were first cropped and sampled into small size of sub-images, which were divided into a training set, a validation set, and a testing set. A DL algorithm based on Faster-RCNN was employed to build the model, extracts features from the images and identifies the oil palm trees, and gives information on the respective locations. The model was then trained and used to detect individual oil palm tree based on data from the testing set. The overall accuracy of oil palm tree detection was measured from three different sites with 97.06%, 96.58%, and 97.79% correct oil palm detection. The results show that the proposed method is more effective, accurate detection, and correctly counts the number of oil palm trees from the UAV images.
format Article
author Xinni, Liu
Kamarul Hawari, Ghazali
Fengrong, Han
Izzeldin, I. Mohd
author_facet Xinni, Liu
Kamarul Hawari, Ghazali
Fengrong, Han
Izzeldin, I. Mohd
author_sort Xinni, Liu
title Automatic detection of oil palm tree from UAV images based on the deep learning method
title_short Automatic detection of oil palm tree from UAV images based on the deep learning method
title_full Automatic detection of oil palm tree from UAV images based on the deep learning method
title_fullStr Automatic detection of oil palm tree from UAV images based on the deep learning method
title_full_unstemmed Automatic detection of oil palm tree from UAV images based on the deep learning method
title_sort automatic detection of oil palm tree from uav images based on the deep learning method
publisher Bellwether Publishing, Ltd.
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/30344/1/Automatic%20detection%20of%20oil%20palm%20tree%20from%20UAV.pdf
http://umpir.ump.edu.my/id/eprint/30344/2/Automatic%20detection%20of%20oil%20palm%20tree%20from%20UAV_FULL.pdf
http://umpir.ump.edu.my/id/eprint/30344/
https://doi.org/10.1080/08839514.2020.1831226
https://doi.org/10.1080/08839514.2020.1831226
_version_ 1688548100985061376
score 13.211869

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