Oil palm tree detection and counting for precision farming using deep learning CNN
Oil palm tree is a very important crop in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is crucial as it could help to estimate the potential yield of palm oil, monitoring the growing situation of palm trees after plantation such as the age and the survival rat...
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Springer Science and Business Media Deutschland GmbH
2022
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my.utm.1010932023-06-01T07:32:22Z http://eprints.utm.my/id/eprint/101093/ Oil palm tree detection and counting for precision farming using deep learning CNN Kipli, Kuryati Lee, Paul Jaw Bin Sam, Huai En Joseph, Annie Zen, Hushairi Gan, Brandon Yong Kien A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti Q Science (General) Oil palm tree is a very important crop in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is crucial as it could help to estimate the potential yield of palm oil, monitoring the growing situation of palm trees after plantation such as the age and the survival rate and also the amount of fertilizer and pesticides needed. In this paper, a deep learning-based oil palm tree detection and counting method is proposed and designed into a functioning app. Images of oil palm plantation are collected by using drones then they are pre-processed. The pre-processed images are used to train and optimize the convolutional neural network (CNN). After the CNN model is trained, it is used to predict the label for all the samples in an image dataset collected through the sliding window technique. Its performance is tested. The performance of the classifier is tested on three different tree conditions, from small number of properly separated trees to big number of crowded trees. Based on the result, accuracy ranging from 83.5% to 100% is obtained. Finally, the method is built into an application for a better user experience. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Kipli, Kuryati and Lee, Paul Jaw Bin and Sam, Huai En and Joseph, Annie and Zen, Hushairi and Gan, Brandon Yong Kien and A. Jalil, M. and Ray, Kanad and Kaiser, M. Shamim and Mahmud, Mufti (2022) Oil palm tree detection and counting for precision farming using deep learning CNN. In: Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering TCCE 2021. Lecture Notes in Networks and Systems, 348 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 549-560. ISBN 978-981167596-6 http://dx.doi.org/10.1007/978-981-16-7597-3_45 DOI:10.1007/978-981-16-7597-3_45 |
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Q Science (General) Kipli, Kuryati Lee, Paul Jaw Bin Sam, Huai En Joseph, Annie Zen, Hushairi Gan, Brandon Yong Kien A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti Oil palm tree detection and counting for precision farming using deep learning CNN |
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Oil palm tree is a very important crop in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is crucial as it could help to estimate the potential yield of palm oil, monitoring the growing situation of palm trees after plantation such as the age and the survival rate and also the amount of fertilizer and pesticides needed. In this paper, a deep learning-based oil palm tree detection and counting method is proposed and designed into a functioning app. Images of oil palm plantation are collected by using drones then they are pre-processed. The pre-processed images are used to train and optimize the convolutional neural network (CNN). After the CNN model is trained, it is used to predict the label for all the samples in an image dataset collected through the sliding window technique. Its performance is tested. The performance of the classifier is tested on three different tree conditions, from small number of properly separated trees to big number of crowded trees. Based on the result, accuracy ranging from 83.5% to 100% is obtained. Finally, the method is built into an application for a better user experience. |
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Book Section |
author |
Kipli, Kuryati Lee, Paul Jaw Bin Sam, Huai En Joseph, Annie Zen, Hushairi Gan, Brandon Yong Kien A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti |
author_facet |
Kipli, Kuryati Lee, Paul Jaw Bin Sam, Huai En Joseph, Annie Zen, Hushairi Gan, Brandon Yong Kien A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti |
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Kipli, Kuryati |
title |
Oil palm tree detection and counting for precision farming using deep learning CNN |
title_short |
Oil palm tree detection and counting for precision farming using deep learning CNN |
title_full |
Oil palm tree detection and counting for precision farming using deep learning CNN |
title_fullStr |
Oil palm tree detection and counting for precision farming using deep learning CNN |
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Oil palm tree detection and counting for precision farming using deep learning CNN |
title_sort |
oil palm tree detection and counting for precision farming using deep learning cnn |
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Springer Science and Business Media Deutschland GmbH |
publishDate |
2022 |
url |
http://eprints.utm.my/id/eprint/101093/ http://dx.doi.org/10.1007/978-981-16-7597-3_45 |
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13.160551 |