| Summary: | Breeding programs to develop planting materials resistant to G. boninense involve a manual
census to monitor the progress of the disease development associated with various treatments. It is
prone to error due to a lack of experience and subjective judgements. This study focuses on the early
detection of G. boninense infection in the oil palm seedlings using near infra-red (NIR)-hyperspectral
data and a support vector machine (SVM). The study aims to use a small number of wavelengths by
using 5, 4, 3, 2, and 1 band reflectance as datasets. These results were then compared with the results
of detection obtained from the vegetation indices developed using spectral reflectance taken from the
same hyperspectral sensor. Results indicated a kernel with a simple linear separation between two
classes would be more suitable for G. boninense detection compared to the others, both for single-band
reflectance and vegetation index datasets. A linear SVM which was developed using a single-band
reflectance at 934 nm was identified as the best model of detection since it was not only economical,
but also demonstrated a high score of accuracy (94.8%), sensitivity (97.6%), specificity (92.5%), and
area under the receiver operating characteristic curve (AUC) (0.95).
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