Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease
The fast growth of oil palm has resulted in its development as a strategic global commodity. Oil palm creates export revenues and strengthens the economies of numerous nations, especially Indonesia and Malaysia. However, oil palms are susceptible to basal stem rot (BSR) caused by Ganoderma bonin...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/103998/1/IZRAHAYU%20BINTI%20CHE%20HASHIM%20-%20IR.pdf |
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| Summary: | The fast growth of oil palm has resulted in its development as a strategic
global commodity. Oil palm creates export revenues and strengthens the
economies of numerous nations, especially Indonesia and Malaysia.
However, oil palms are susceptible to basal stem rot (BSR) caused by
Ganoderma boninense (G. boninense), the most dangerous oil palm disease.
This disease has been a cause for concern as it has caused significant tree
mortality in several plantations in Malaysia. Given that there is currently no
effective cure for this disease, the only viable solution is to prolong the life of
oil palm trees. This study explored the early detection of the BSR using
thermal images and an ALOS PALSAR-2 image with dual-polarization,
Horizontal transmit and Vertical receive (HV), and Horizontal transmit and
Horizontal receive (HH). The study was conducted in Seberang Perak, part
of Felcra Seberang Perak 10, and is located in Perak, Malaysia. Initially, an
experiment was carried out to (1) identify the potential temperature variables;
(2) identify the potential backscatter variables; (3) utilize the imbalance data
approach like Random under-sampling (RUS), Random oversampling
(ROS), Synthetic Minority Oversampling (SMOTE) and AdaBoost; and (4)
evaluate the performance of machine learning (ML) classifiers Naïve Bayes
(NB), Multilayer Perceptron (MLP), as well as Random Forest (RF) in
classifying the stages and severity levels of G. boninense. The sample size
was comprised of 55 non-infected trees and 37 infected trees. During the field
experiments, oil palm tree samples of non-infected (T0), mild infected (T1),
moderate infected (T2), and severe infected (T3) were measured using the
FLIR T620 IR infrared thermal imaging camera to obtain the temperature of
the oil palm trees. The temperature variation for each thermal image was
examined using FLIR ResearchIR Max, the camera manufacturer's software,
and feature extraction for each thermal image was extracted using FLIR
Tools in the FLIR ResearcherIR environment software. The backscattering
value of each tree was then extracted from the ALOS PALSAR-2 image.
Using the Extract Multi Values tool in ArcGIS, the backscattering value for
each oil palm point was derived from the processed ALOS PALSAR-2 image.
As the ALOS PALSAR-2 image was evaluated with dual-polarization (HH and
HV), each digitized point has two distinct backscatter data with four severity
levels (T0 to T3). The machine learning algorithm consistently performs well
when presented with a well-balanced dataset. In an imbalanced dataset, one
of the two classes contains fewer total samples than the other class. The
sampling-based method, also known as the data level method, is used to
deal with this problem. In this study, the resampling method and ensemble
procedure relied entirely on the Waikato Environment for Knowledge Analysis
(WEKA) version 3.8.5 software. The classification is performed using the
derived features from the thermal images and the backscatter features. The
extracted features serve as predictors and the status of oil palm as a
response. To identify non-infected and BSR-infected trees, the WEKA tool
version 3.8.5 was used for classification. The classifiers evaluated in this
study were Nave Bayes (NB), Multilayer Perceptron (MLP), and Random
Forest (RF). Two datasets, for training and testing, were both classified. We
divided the dataset into a training dataset of 70% and a test dataset of 30%.
The classification was done with 10-fold cross-validation to avoid overfitting
and get unbiased prediction error estimates. This was the recommended
validation method for the small dataset. This study, therefore, detailed the
description of the confusion matrix as an alternative in terms of the rate of
success of the non-infected and BSR-infected tree together with the balanced
classification rate (BCR) or balanced accuracy, the precision-recall curve
(PRC), and receiver operating characteristics (ROC) curve region (AUC) to
evaluate different classifier and imbalanced approaches and measure their
performance. The study found that the Tmax, Tmin feature is the most beneficial
concerning other temperature characteristics for classifying non-infected or
infected BSR trees. In the meantime, the HV feature is most advantageous
for classifying non-infected or infected BSR trees compared to other
backscatters. Compared with a single approach and other approximate
imbalance data approaches, the ROS approach improves BCR, AUC, and
PRC data results in datasets. Next, all classifier models were employed in
classifying the BSR disease severity using the combination of the best
features of temperature (Tmax, Tmin), backscatter features (HV), and significant
ground-based data (DbH and soil moisture) with a single and ROS approach.
In conclusion, all three ML methods can classify the oil palm with severe BSR
disease with an outstanding result using the ROS approach. Meanwhile, the
MLP was found to be the ideal model with a BCR value of 0.964, AUC and
PRC having the same value of 1.000, model accuracy of 96.43%, and a
Kappa coefficient of 0.95. The MLP classifier model also had a high success
rate, whereby it correctly classified 85.71% (T0-healthy), 100% (T1-mild
infected), 100% (T2-moderate infected), and 100% (T3-severe infected). This
study concluded that for the early detection of BSR, a significant degree of
accuracy was obtained. Infected palms are asymptomatic throughout the
disease's early stages, making disease detection challenging. The survival
of affected trees must detect BSR at the mild infected (T1) stage. A meaningful conclusion of this study is that the ROS technique can
differentiate the severity of mild infection (T1) compared to a single approach
that is incapable of doing so. The main benefit of this study is the
development of an appropriate model for early identification and severity
classification of BSR disease in oil palms via remote sensing and data mining
approaches rapidly and cost-effectively. |
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