UPM Institutional Repository

Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm


Liaghat, Shohreh and Mansor, Shattri and Ehsani, Reza and Mohd Shafri, Helmi Zulhaidi and Meon, Sariah and Sankaran, Sindhuja (2014) Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm. Computers and Electronics in Agriculture, 101. pp. 48-54. ISSN 0168-1699


Basal stem rot (BSR), caused by Ganoderma boninense is known as the most destructive disease in oil palm plantations in Southeast Asia. Ganoderma could reduce the productivity of oil palm plantations and potentially reduce the market value of palm oil in Malaysia. Early disease management of Ganoderma could prevent production losses and reduce the use of chemicals. This study focuses on the development of a statistical model for the discrimination of Ganoderma infestation on oil palm trees at different stages using a Fourier transform infrared (FT-IR) spectroscopic technique. Leaf samples of healthy, mild, moderately, and severely-infected trees were measured using FT-IR spectrometers to obtain absorbance data from the range of 2.55–25.05 μm s (3921–399 cm−1). The samples were analyzed with and without dilution with KBr. After pre-processing (baseline correction and normalization), the Savitzky–Golay method was used to calculate first and second derivatives. Then, for the preprocessed raw, first derivatives and second derivatives datasets, principal component analysis was performed to reduce the dimensionality of the data. The selected principal component scores were used in classification using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN) and Naive-Bayes (NB) multivariate classification algorithms. The algorithms were tested to classify the leaf samples into four levels of disease severity. The results demonstrated that when samples were prepared with KBr, the LDA-based model resulted in the highest average overall classification accuracy of 92%, with individual classification accuracies greater than about 90% using the pre-processed raw dataset. This verifies the potential of mid-infrared spectroscopy for Ganoderma detection in early symptomless stages of infection in oil palm.

Download File

Full text not available from this repository.

Additional Metadata

Item Type: Article
Divisions: Faculty of Agriculture
Faculty of Engineering
DOI Number: https://doi.org/10.1016/j.compag.2013.12.012
Publisher: Elsevier
Keywords: Basal stem rot; Oil palm; Mid-infrared spectroscopy; Pattern recognition algorithms
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 15 Dec 2015 08:31
Last Modified: 15 Dec 2015 08:31
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.compag.2013.12.012
URI: http://psasir.upm.edu.my/id/eprint/34527
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item