Development of classification models for basal stem rot (BSR) disease in oil palm using dielectric spectroscopy
| Autor: | Nazmi Mat Nawi, Alfadhl Yahya Khaled, Siti Khairunniza Bejo, Samsuzana Abd Aziz, Idris Abu Seman, Mohamad Anuar Izzuddin |
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| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
010401 analytical chemistry Dielectric Quadratic classifier Linear discriminant analysis 01 natural sciences 0104 chemical sciences Dielectric spectroscopy Horticulture Disease severity Principal component analysis Palm oil Stem rot Agronomy and Crop Science 010606 plant biology & botany Mathematics |
| Zdroj: | Industrial Crops and Products. 124:99-107 |
| ISSN: | 0926-6690 |
| Popis: | Basal stem rot (BSR) is the most destructive disease in oil palm plantations caused by Ganoderma boninense fungus, leading to a major economic setback in palm oil production. In order to reduce the losses caused by this disease, an effective early detection method is needed. Early detection not only prevents production losses, but it also reduces the use of chemicals. Therefore, this paper aims at investigating an early detection method utilizing dielectric properties (impedance, capacitance, dielectric constant, and dissipation factor) of oil palm trees. Leaf samples of healthy, mild, moderate, and severely-infected trees were collected and leaves’ dielectric properties were measured at a frequency range of 100 kHz–30 MHz with 100 kHz intervals. These spectral data were then reduced by principal component analysis (PCA) method. Following that, the reduced spectral data were tested to classify the leaf samples into four levels of disease severity. The classifiers used are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), and Naive Bayes (NB). The results showed that the dielectric spectra of oil palm leaves of diffident BSR severity levels were statistically different (p |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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