Classification of basal stem rot disease in oil palm using dielectric spectroscopy
Dielectric spectroscopy method has been identified to be able to classify plant diseases. However, studies on its application on basal stem rot (BSR) disease in oil palm are yet to be explored. This study investigated the feasibility of utilizing dielectric spectral properties such as impedance,...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/77644/1/FK%202019%2027%20ir.pdf |
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| Summary: | Dielectric spectroscopy method has been identified to be able to classify plant
diseases. However, studies on its application on basal stem rot (BSR) disease
in oil palm are yet to be explored. This study investigated the feasibility of
utilizing dielectric spectral properties such as impedance, capacitance,
dielectric constant, and dissipation factor in classifying BSR disease in oil palm
trees. Leaflet samples from different oil palm trees (healthy, mild, moderate,
and severely-infected) were collected and dielectric properties were measured
using a solid test fixture connected to an impedance analyzer at a frequency
range of 100 kHz–30 MHz with 500 spectral intervals. Two data reduction
methods were used 1) feature selections methods and 2) principal component
analysis (PCA). First, features selection algorithms (genetic algorithm (GA),
random forest (RF), and support vector machine-feature selection (SVM-FS))
were used to select the most significant frequencies. Then, data at the most
significant frequencies were served as the input of six classifiers, namely:
support vector machine (SVM), artificial neural networks (ANN), linear
discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest
neighbours (kNN) and Naïve Bayes (NB) to classify different levels of BSR
disease. In a separate analysis, PCA was used as the data reduction method
and the highest principal components (PCs) were served as the inputs to the
classifiers. An analysis was done to see the effect of implementing different
data reduction algorithms in classifying BSR disease. The results showed that the impedance parameter was the best in classifying
BSR severity levels compared to the other dielectric properties. The severelyinfected
oil palm leaflets had the highest mean impedance (11.95 Ω) and the
lowest was found at the healthy oil palm leaflets (4.56 Ω). For feature selection
algorithms, SVM-FS model gave the best classification accuracies compared to GA and RF; ranged from 81.82% to 88.64% with SVM and kNN as the best
classifiers. Better classification accuracy was achieved using QDA classifier
when implementing PCA dimensionality reduction with the accuracy of 96.36%.
Without implementing any data reduction algorithm, the highest classification
accuracy was found in SVM classifier with 79.55%. As such, this study
demonstrates the potentials of utilizing dielectric spectral properties of oil palm
leaflets in classifying the BSR diseases of the trees. |
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