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Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles

The conventional method to quantify leaf biochemical properties (nutrients and chlorophylls) is tedious, labour-intensive, and impractical for vast oil palm plantation areas. Spectral analysis retrieved from a spectroradiometer and an unmanned aerial vehicle (UAV) and imbalanced approaches such as t...

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Main Authors: Amirruddin, Amiratul Diyana, Muharam, Farrah Melissa, Ismail, Mohd Hasmadi, Tan, Ngai Paing, Ismail, Mohd Firdaus
Format: Article
Published: Elsevier 2022
Online Access:http://psasir.upm.edu.my/id/eprint/103414/
https://www.sciencedirect.com/science/article/pii/S0168169921006633
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spelling my.upm.eprints.1034142023-06-13T07:11:34Z http://psasir.upm.edu.my/id/eprint/103414/ Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles Amirruddin, Amiratul Diyana Muharam, Farrah Melissa Ismail, Mohd Hasmadi Tan, Ngai Paing Ismail, Mohd Firdaus The conventional method to quantify leaf biochemical properties (nutrients and chlorophylls) is tedious, labour-intensive, and impractical for vast oil palm plantation areas. Spectral analysis retrieved from a spectroradiometer and an unmanned aerial vehicle (UAV) and imbalanced approaches such as the Synthetic Minority Over-sampling TEchnique (SMOTE) and machine learning have given promising results for monitoring plant biochemical properties. However, the integration of these methods is not widely explored for oil palm. There are three primary aims of the current study. We evaluate the effectiveness of the integration of SMOTE, Logistic Model Tree (LMT), and Adaptive Boosting (AdaBoost) to address data imbalance problems for the assessment of the oil palm nutrients and chlorophylls status. The performance of the raw band and vegetation index (VI) extracted from the UAV in assessing leaf biochemical properties of mature oil palms is also addressed. Finally, we compare the competency of the spectral model retrieved from the spectroradiometer and UAV. In the study, nitrogen (N) treatments varying between 0 and 6 kg palm−1 were applied to mature Tenera palms. The integration of SMOTE with LMT and AdaBoost (LMT-SMOTEBoost) outperformed other approaches in classifying the leaf biochemical sufficiency status of mature oil palm. The VIs outperformed the raw band in discriminating the leaf biochemical properties at the canopy level. Both leaf and canopy spectral models obtained from spectroradiometer and UAV were comparable and produced good performance with balanced accuracy (BAcc) above 0.77. Using these techniques may provide palm oil plantation owners with a cost-effective way to monitor nutrient levels in palms more efficiently and comprehensively to ensure greater harvests and tree health. Elsevier 2022 Article PeerReviewed Amirruddin, Amiratul Diyana and Muharam, Farrah Melissa and Ismail, Mohd Hasmadi and Tan, Ngai Paing and Ismail, Mohd Firdaus (2022) Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles. Computers and Electronics in Agriculture, 193. art. no. 106646. pp. 1-16. ISSN 0168-1699; ESSN: 1872-7107 https://www.sciencedirect.com/science/article/pii/S0168169921006633 10.1016/j.compag.2021.106646
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/
description The conventional method to quantify leaf biochemical properties (nutrients and chlorophylls) is tedious, labour-intensive, and impractical for vast oil palm plantation areas. Spectral analysis retrieved from a spectroradiometer and an unmanned aerial vehicle (UAV) and imbalanced approaches such as the Synthetic Minority Over-sampling TEchnique (SMOTE) and machine learning have given promising results for monitoring plant biochemical properties. However, the integration of these methods is not widely explored for oil palm. There are three primary aims of the current study. We evaluate the effectiveness of the integration of SMOTE, Logistic Model Tree (LMT), and Adaptive Boosting (AdaBoost) to address data imbalance problems for the assessment of the oil palm nutrients and chlorophylls status. The performance of the raw band and vegetation index (VI) extracted from the UAV in assessing leaf biochemical properties of mature oil palms is also addressed. Finally, we compare the competency of the spectral model retrieved from the spectroradiometer and UAV. In the study, nitrogen (N) treatments varying between 0 and 6 kg palm−1 were applied to mature Tenera palms. The integration of SMOTE with LMT and AdaBoost (LMT-SMOTEBoost) outperformed other approaches in classifying the leaf biochemical sufficiency status of mature oil palm. The VIs outperformed the raw band in discriminating the leaf biochemical properties at the canopy level. Both leaf and canopy spectral models obtained from spectroradiometer and UAV were comparable and produced good performance with balanced accuracy (BAcc) above 0.77. Using these techniques may provide palm oil plantation owners with a cost-effective way to monitor nutrient levels in palms more efficiently and comprehensively to ensure greater harvests and tree health.
format Article
author Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Ismail, Mohd Hasmadi
Tan, Ngai Paing
Ismail, Mohd Firdaus
spellingShingle Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Ismail, Mohd Hasmadi
Tan, Ngai Paing
Ismail, Mohd Firdaus
Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
author_facet Amirruddin, Amiratul Diyana
Muharam, Farrah Melissa
Ismail, Mohd Hasmadi
Tan, Ngai Paing
Ismail, Mohd Firdaus
author_sort Amirruddin, Amiratul Diyana
title Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
title_short Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
title_full Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
title_fullStr Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
title_full_unstemmed Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
title_sort synthetic minority over-sampling technique (smote) and logistic model tree (lmt)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (elaeis guineensis) using spectroradiometers and unmanned aerial vehicles
publisher Elsevier
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/103414/
https://www.sciencedirect.com/science/article/pii/S0168169921006633
_version_ 1769844460822200320
score 13.211869

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