Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy

Abstract

Basal stem rot (BSR) is a prominent plant disease caused by Ganoderma boninense fungus, which infects oil palm plantations leading to large economic losses in palm oil production. There is need for novel disease detection techniques that can be used to reduce the oil palm losses due to BSR. Thus, this paper investigated the feasibility of utilizing electrical properties such as impedance, capacitance, dielectric constant, and dissipation factor in early detection of BSR disease in oil palm tree. Leaf samples from different oil palm trees (healthy, mild, moderate, and severely-infected) were collected and measured using a solid test fixture (16451B, Keysight Technologies, Japan) connected to an impedance analyzer (4294A, Agilent Technologies, Japan) at a frequency range of 100 kHz-30 MHz with 300 spectral interval. Genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS) were used to analyze the electrical properties of the dataset and the most significant frequencies were selected. Following the selection of significant frequencies, the features were evaluated using two classifiers, support vector machine (SVM) and artificial neural networks (ANN) to determine the overall and individual class classification accuracies. The selection model comparative feature analysis demonstrated that the best statistical indicators with overall accuracy (88.64%), kappa (0.8480) and low mean absolute error (0.1652) were obtained using significant frequencies produced by SVM-FS model. The results indicated that the SVM classifier shows better performance as compared to ANN classifier. The results also showed that the classes, features selection models, and the electrical properties were found to be significantly different (p < .1). The impedance values were highly classified by Ganoderma disease at different levels of severity with overall accuracies of more than 80%. Impedance can be considered as the best electrical properties that can be used to estimate the severity of BSR disease in oil palm using spectroscopy technique. As such, this study demonstrates the potentials of utilizing electrical properties for detection of Ganoderma diseases in oil palm.

Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method

“…These models calculate impurity-based importance. The importance value in the trained tree could be regarded as the importance score of each individual feature ( Khaled et al, 2018 ). Linear estimators with sparse penalty term can be used as feature selectors.…”

“…The coefficients of the trained models are considered as the importance scores of each feature. A typical example is support vector machine (SVM) feature selector (SVM-FS) (Khaled et al, 2018;Pes, 2019). The model-based feature selectors consider the target labels.…”

“…The model-based feature selection methods have great potential to screen out informative and output-related features by improving the used model. There are also several tree-based feature selection approaches, such as extra-trees classifier (ETC) (Geurts et al, 2006;Krol and Polanska, 2017) and random forest (RF) (Khaled et al, 2018).…”

Classification of Basal Stem Rot Disease in Oil Palm Plantations Using Terrestrial Laser Scanning Data and Machine Learning

“…The researchers used electrical properties, such as impedance, capacitance, dielectric constant, and dissipation factor to detect BSR disease in oil palm trees at an early stage [31]. Only 56 samples from mature oil palm trees were selected, with 14 trees in each of the four infection levels.…”

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