Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN

Kuryati Kipli; Paul Lee Jaw Bin; Sam Huai En; Annie Joseph; Hushairi Zen; Brandon Gan Yong Kien; M. A. Jalil; Kanad Ray; M. Shamim Kaiser; Mufti Mahmud

doi:10.1007/978-981-16-7597-3_45

Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN

Kuryati Kipli^*
, Paul Lee Jaw Bin
, Sam Huai En
, Annie Joseph
, Hushairi Zen
, Brandon Gan Yong Kien
, M. A. Jalil
, Kanad Ray
, M. Shamim Kaiser
, Mufti Mahmud

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021
Editors	M. Shamim Kaiser, Kanad Ray, Anirban Bandyopadhyay, Kavikumar Jacob, Kek Sie Long
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	549-560
Number of pages	12
ISBN (Print)	9789811675966
DOIs	https://doi.org/10.1007/978-981-16-7597-3_45
State	Published - 2022
Externally published	Yes
Event	3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021 - Parit Raja, Malaysia Duration: 21 Oct 2021 → 22 Oct 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	348
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021
Country/Territory	Malaysia
City	Parit Raja
Period	21/10/21 → 22/10/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

Convolutional neural network (CNN)
Deep learning
Detection
Oil palm tree

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-16-7597-3_45

Cite this

Kipli, K., Bin, P. L. J., En, S. H., Joseph, A., Zen, H., Kien, B. G. Y., Jalil, M. A., Ray, K., Kaiser, M. S., & Mahmud, M. (2022). Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN. In M. S. Kaiser, K. Ray, A. Bandyopadhyay, K. Jacob, & K. S. Long (Eds.), Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021 (pp. 549-560). (Lecture Notes in Networks and Systems; Vol. 348). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-7597-3_45

Kipli, Kuryati ; Bin, Paul Lee Jaw ; En, Sam Huai et al. / Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN. Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021. editor / M. Shamim Kaiser ; Kanad Ray ; Anirban Bandyopadhyay ; Kavikumar Jacob ; Kek Sie Long. Springer Science and Business Media Deutschland GmbH, 2022. pp. 549-560 (Lecture Notes in Networks and Systems).

@inproceedings{98d69f6df59c4822bd8ca42ae3e6b39d,

title = "Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN",

abstract = "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.",

keywords = "Convolutional neural network (CNN), Deep learning, Detection, Oil palm tree",

author = "Kuryati Kipli and Bin, \{Paul Lee Jaw\} and En, \{Sam Huai\} and Annie Joseph and Hushairi Zen and Kien, \{Brandon Gan Yong\} and Jalil, \{M. A.\} and Kanad Ray and Kaiser, \{M. Shamim\} and Mufti Mahmud",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021 ; Conference date: 21-10-2021 Through 22-10-2021",

year = "2022",

doi = "10.1007/978-981-16-7597-3\_45",

language = "English",

isbn = "9789811675966",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "549--560",

editor = "Kaiser, \{M. Shamim\} and Kanad Ray and Anirban Bandyopadhyay and Kavikumar Jacob and Long, \{Kek Sie\}",

booktitle = "Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021",

address = "Germany",

}

Kipli, K, Bin, PLJ, En, SH, Joseph, A, Zen, H, Kien, BGY, Jalil, MA, Ray, K, Kaiser, MS & Mahmud, M 2022, Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN. in MS Kaiser, K Ray, A Bandyopadhyay, K Jacob & KS Long (eds), Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021. Lecture Notes in Networks and Systems, vol. 348, Springer Science and Business Media Deutschland GmbH, pp. 549-560, 3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021, Parit Raja, Malaysia, 21/10/21. https://doi.org/10.1007/978-981-16-7597-3_45

Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN. / Kipli, Kuryati; Bin, Paul Lee Jaw; En, Sam Huai et al.
Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021. ed. / M. Shamim Kaiser; Kanad Ray; Anirban Bandyopadhyay; Kavikumar Jacob; Kek Sie Long. Springer Science and Business Media Deutschland GmbH, 2022. p. 549-560 (Lecture Notes in Networks and Systems; Vol. 348).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN

AU - Kipli, Kuryati

AU - Bin, Paul Lee Jaw

AU - En, Sam Huai

AU - Joseph, Annie

AU - Zen, Hushairi

AU - Kien, Brandon Gan Yong

AU - Jalil, M. A.

AU - Ray, Kanad

AU - Kaiser, M. Shamim

AU - Mahmud, Mufti

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Convolutional neural network (CNN)

KW - Deep learning

KW - Detection

KW - Oil palm tree

UR - https://www.scopus.com/pages/publications/85126208150

U2 - 10.1007/978-981-16-7597-3_45

DO - 10.1007/978-981-16-7597-3_45

M3 - Conference contribution

AN - SCOPUS:85126208150

SN - 9789811675966

T3 - Lecture Notes in Networks and Systems

SP - 549

EP - 560

BT - Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021

A2 - Kaiser, M. Shamim

A2 - Ray, Kanad

A2 - Bandyopadhyay, Anirban

A2 - Jacob, Kavikumar

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PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 21 October 2021 through 22 October 2021

ER -

Kipli K, Bin PLJ, En SH, Joseph A, Zen H, Kien BGY et al. Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN. In Kaiser MS, Ray K, Bandyopadhyay A, Jacob K, Long KS, editors, Proceedings of the 3rd International Conference on Trends in Computational and Cognitive Engineering - TCCE 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 549-560. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-16-7597-3_45

Oil Palm Tree Detection and Counting for Precision Farming Using Deep Learning CNN

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this