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Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest

A common practice of chlorophyll (chl) determination has been using the chemical analysis that is destructive and time-consuming. A current prospective alternative method such hyperspectral remote sensing offers a non-destructive measurement of chl which provides a result in the rapid and real-time...

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Main Authors: Amirruddin, Amiratul Diyana, Muharam, Farrah Melissa, Ismail, Mohd Hasmadi, Ismail, Mohd Firdaus, Tan, Ngai Paing, Karam Singh, Daljit Singh
Format: Article
Language:English
Published: Elsevier 2020
Online Access:http://psasir.upm.edu.my/id/eprint/89237/1/AGE.pdf
http://psasir.upm.edu.my/id/eprint/89237/
https://www.sciencedirect.com/science/article/pii/S0168169919309974
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spelling my.upm.eprints.892372021-09-20T23:25:49Z http://psasir.upm.edu.my/id/eprint/89237/ Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest Amirruddin, Amiratul Diyana Muharam, Farrah Melissa Ismail, Mohd Hasmadi Ismail, Mohd Firdaus Tan, Ngai Paing Karam Singh, Daljit Singh A common practice of chlorophyll (chl) determination has been using the chemical analysis that is destructive and time-consuming. A current prospective alternative method such hyperspectral remote sensing offers a non-destructive measurement of chl which provides a result in the rapid and real-time manner. Therefore, the ultimate aims of this study were to propose the chls (chl a, chl b, total chl content (TCC) and relative chl content (RCC)) sufficiency levels via Jenks Natural Breaks (JNB) classification and recommend the best subset of chl-sensitive wavelengths, frond number and classifier for classifying the chls according to the designated sufficiency levels via hyperspectral remote sensing platform. In order to achieve the objectives, an experiment was conducted on mature Tenera palm stands (12 and 15 years old) with N treatments varied from 0 to 2 kg. The Jenks Natural Breaks (JNB) classification was proposed to determine the chl sufficiency levels. Feature selection was carried out to select the chl-sensitive wavelengths while Decision Tree (DT) and Random Forest (RF) classifiers were used to classify the chl sufficiency levels using the selected wavelengths. Results from this study showed that the chl-sensitive wavelengths mostly belong to the red-edge region and importantly, they were frond age-dependent. Generally, the classification accuracy tended to decrease as frond gets older. The RF performed the best in discriminating the chl sufficiency levels with accuracy ranging from 92.79 to 98.77% compared to 56.30–90.48% achieved by DT. In the nutshell, the execution of frond 9 was more ideal than frond 17 for monitoring chl content of mature oil palm via remote sensing and RF portrayed promising insight in assessing chls content of mature oil palm. Elsevier 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/89237/1/AGE.pdf Amirruddin, Amiratul Diyana and Muharam, Farrah Melissa and Ismail, Mohd Hasmadi and Ismail, Mohd Firdaus and Tan, Ngai Paing and Karam Singh, Daljit Singh (2020) Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest. Computers and Electronics in Agriculture, 169. art. no. 105221. pp. 1-11. ISSN 0168-1699 https://www.sciencedirect.com/science/article/pii/S0168169919309974 10.1016/j.compag.2020.105221
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description A common practice of chlorophyll (chl) determination has been using the chemical analysis that is destructive and time-consuming. A current prospective alternative method such hyperspectral remote sensing offers a non-destructive measurement of chl which provides a result in the rapid and real-time manner. Therefore, the ultimate aims of this study were to propose the chls (chl a, chl b, total chl content (TCC) and relative chl content (RCC)) sufficiency levels via Jenks Natural Breaks (JNB) classification and recommend the best subset of chl-sensitive wavelengths, frond number and classifier for classifying the chls according to the designated sufficiency levels via hyperspectral remote sensing platform. In order to achieve the objectives, an experiment was conducted on mature Tenera palm stands (12 and 15 years old) with N treatments varied from 0 to 2 kg. The Jenks Natural Breaks (JNB) classification was proposed to determine the chl sufficiency levels. Feature selection was carried out to select the chl-sensitive wavelengths while Decision Tree (DT) and Random Forest (RF) classifiers were used to classify the chl sufficiency levels using the selected wavelengths. Results from this study showed that the chl-sensitive wavelengths mostly belong to the red-edge region and importantly, they were frond age-dependent. Generally, the classification accuracy tended to decrease as frond gets older. The RF performed the best in discriminating the chl sufficiency levels with accuracy ranging from 92.79 to 98.77% compared to 56.30–90.48% achieved by DT. In the nutshell, the execution of frond 9 was more ideal than frond 17 for monitoring chl content of mature oil palm via remote sensing and RF portrayed promising insight in assessing chls content of mature oil palm.
format Article
author Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Ismail, Mohd Hasmadi
Ismail, Mohd Firdaus
Tan, Ngai Paing
Karam Singh, Daljit Singh
spellingShingle Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Ismail, Mohd Hasmadi
Ismail, Mohd Firdaus
Tan, Ngai Paing
Karam Singh, Daljit Singh
Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest
author_facet Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Ismail, Mohd Hasmadi
Ismail, Mohd Firdaus
Tan, Ngai Paing
Karam Singh, Daljit Singh
author_sort Amirruddin, Amiratul Diyana
title Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest
title_short Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest
title_full Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest
title_fullStr Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest
title_full_unstemmed Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: analysis of decision tree and random forest
title_sort hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (elaeis guineensis) based on frond numbers: analysis of decision tree and random forest
publisher Elsevier
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/89237/1/AGE.pdf
http://psasir.upm.edu.my/id/eprint/89237/
https://www.sciencedirect.com/science/article/pii/S0168169919309974
_version_ 1712286756261330944
score 13.211869

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