Investigation on the use of acoustic tomography for the detection of ganoderma boninense infection in oil palm

• Main Author: Marcella Lennie Michael
• Format: Thesis
• Language: English; English
• Published: 2020
• Subjects: SD411-428 Conservation and protection Including forest influences, damage by elements, fires, forest reserves
• Online Access: https://eprints.ums.edu.my/id/eprint/42036/1/24%20PAGES.pdf; https://eprints.ums.edu.my/id/eprint/42036/2/FULLTEXT.pdf; https://eprints.ums.edu.my/id/eprint/42036/

Oil palm (Elaeis guineensis) is the leading vegetable oil crop particularly in South – East Asia countries especially in Indonesia and Malaysia since it brings great economic importance to the country. However, due to incurable disease known as Basal Stem Rot caused by Ganoderma boninense fungi, considerable yield losses has increased. Most of the management strategies for this disease failed due to inability to detect the disease at early stages. In this research, new model of stress wave based acoustic system is introduced to evaluate the internal condition of three different characteristics of oil palm standing trees namely; healthy, moderately infected and severely infected. Twelve samples were chosen for each characteristics with the average of 35 years and 45 cm for age and diameter size respectively. The acoustic system is consisted of a regular steel and rubber hammer, eight piezoelectric sensors, four amplifiers, cables, battery box and system software. Acoustic sensor probes were inserted perpendicularly around the oil palm trunk to obtain the readout data by tapping each sensor with a steel hammer. Data was collected by a computer and two dimensional (2D) tomography images were generated automatically by the system software. Results obtained were then compared with visual inspection of the real cross section after oil palm trees were cut down and the tress wave velocity distribution in each sample were analyzed. Tissue samples of each oil palm were collected and subjected to laboratory for ergosterol analysis. Findings of this study revealed that, there was a clear difference between tomography image of healthy, asymptomatic and severely infected sample. The 2D tomograms give a close correlation with the real cross section of oil palm that was cut down. Meanwhile, stress wave velocity distribution shows that, higher difference in velocity range leads to more color depth in tomography image compared to lower velocity range. Besides that, this system was proven to provide better information on stages of infection compared to the ergosterol analysis. Therefore, this study suggested that stress wave based acoustic tomography can be utilized as a new method of future detection Ganoderma boninense infection in oil palm. This method could give great benefits in oil palm industry especially in order to detect the infected area on an ‘In-situ’ basis so that the right remedial measure and treatment could be applied and most importantly reduce the economic losses in the industry.