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Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers

Nitrogen (N) is a crucial element in sustaining oil palm production. However, assessing N status of tall perennial crops such as oil palm is complex and not as straightforward as assessing annual crops, due to complex N partitioning, age, and larger amounts of respiratory loads. Hence, the objective...

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Main Authors: Amirruddin, Amiratul Diyana, Muharam, Farrah Melissa, Mazlan, Norida
Format: Article
Language:English
Published: Taylor & Francis 2017
Online Access:http://psasir.upm.edu.my/id/eprint/59408/1/Assessing%20leaf%20scale%20measurement%20for%20nitrogen%20content%20of%20oil%20palm%20performance%20of%20discriminant%20analysis%20and%20support%20vector%20machine%20classifiers.pdf
http://psasir.upm.edu.my/id/eprint/59408/
https://www.tandfonline.com/doi/abs/10.1080/01431161.2017.1372862
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spelling my.upm.eprints.594082018-03-01T07:31:45Z http://psasir.upm.edu.my/id/eprint/59408/ Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers Amirruddin, Amiratul Diyana Muharam, Farrah Melissa Mazlan, Norida Nitrogen (N) is a crucial element in sustaining oil palm production. However, assessing N status of tall perennial crops such as oil palm is complex and not as straightforward as assessing annual crops, due to complex N partitioning, age, and larger amounts of respiratory loads. Hence, the objectives of this study were to evaluate the potential of spectral measurements obtained from leaf scale and machine learning approaches as a rapid tool for quantifying oil palm N status. This study involved assessing the performance of discriminant analysis (DA) and Support Vector Machine (SVM) classifiers for discriminating spectral bands sensitive to N sufficiency levels and comparing the predictive accuracy of DA and SVM for classifying N status of immature and mature oil palms. The experiment was conducted on immature Tenera seedlings (13 months old) and mature Tenera palm stands (9 and 12 years old) that were arranged in Randomized Complete Block Design with treatments varied from 0 to 2 kg N. Generally, the discriminant function of both classifiers was age-dependent. A clear trade-off between the classifiers’ number of spectral bands and their accuracies was observed; the DA with a larger number of optimal spectral bands could discriminate N sufficiency levels of all maturity classes with higher accuracies compared to the SVM, yet the latter could produce reasonable accuracies with a lesser number of spectral bands. N status of all maturity classes could be classified satisfactorily with SVM (71–88%) via the satellite-simulated blue and green bands, signifying the possibility to develop spectral index or an N-sensitive sensor for oil palm. Taylor & Francis 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59408/1/Assessing%20leaf%20scale%20measurement%20for%20nitrogen%20content%20of%20oil%20palm%20performance%20of%20discriminant%20analysis%20and%20support%20vector%20machine%20classifiers.pdf Amirruddin, Amiratul Diyana and Muharam, Farrah Melissa and Mazlan, Norida (2017) Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers. International Journal of Remote Sensing, 38 (23). pp. 7260-7280. ISSN 0143-1161; ESSN: 1366-5901 https://www.tandfonline.com/doi/abs/10.1080/01431161.2017.1372862 10.1080/01431161.2017.1372862
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 Nitrogen (N) is a crucial element in sustaining oil palm production. However, assessing N status of tall perennial crops such as oil palm is complex and not as straightforward as assessing annual crops, due to complex N partitioning, age, and larger amounts of respiratory loads. Hence, the objectives of this study were to evaluate the potential of spectral measurements obtained from leaf scale and machine learning approaches as a rapid tool for quantifying oil palm N status. This study involved assessing the performance of discriminant analysis (DA) and Support Vector Machine (SVM) classifiers for discriminating spectral bands sensitive to N sufficiency levels and comparing the predictive accuracy of DA and SVM for classifying N status of immature and mature oil palms. The experiment was conducted on immature Tenera seedlings (13 months old) and mature Tenera palm stands (9 and 12 years old) that were arranged in Randomized Complete Block Design with treatments varied from 0 to 2 kg N. Generally, the discriminant function of both classifiers was age-dependent. A clear trade-off between the classifiers’ number of spectral bands and their accuracies was observed; the DA with a larger number of optimal spectral bands could discriminate N sufficiency levels of all maturity classes with higher accuracies compared to the SVM, yet the latter could produce reasonable accuracies with a lesser number of spectral bands. N status of all maturity classes could be classified satisfactorily with SVM (71–88%) via the satellite-simulated blue and green bands, signifying the possibility to develop spectral index or an N-sensitive sensor for oil palm.
format Article
author Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Mazlan, Norida
spellingShingle Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Mazlan, Norida
Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers
author_facet Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Mazlan, Norida
author_sort Amirruddin, Amiratul Diyana
title Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers
title_short Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers
title_full Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers
title_fullStr Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers
title_full_unstemmed Assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers
title_sort assessing leaf scale measurement for nitrogen content of oil palm: performance of discriminant analysis and support vector machine classifiers
publisher Taylor & Francis
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/59408/1/Assessing%20leaf%20scale%20measurement%20for%20nitrogen%20content%20of%20oil%20palm%20performance%20of%20discriminant%20analysis%20and%20support%20vector%20machine%20classifiers.pdf
http://psasir.upm.edu.my/id/eprint/59408/
https://www.tandfonline.com/doi/abs/10.1080/01431161.2017.1372862
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score 13.211869

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