This research addresses the most alarming disease threat within the oil palm industry: Basal Stem Rot (BSR), which is caused by the fungus Ganoderma boninense. This devastating disease not only infects mature trees but also renders young oil palm seedlings susceptible, thus making the early detection of symptoms a critical necessity for prompt treatment. The central objective of this study was to differentiate the growth characteristics of healthy seedlings (NONF) from those infected (INF) with BSR through the application of Ground-based LiDAR technology.

The methodology encompassed the study of one hundred INF seedlings alongside 20 NONF seedlings, which served as the designated control group. Three key parameters were systematically measured using the LiDAR system across four sessions conducted at two-week intervals: height, stem diameter, and point density.

The analysis of the collected data revealed significant differences in the mean height and mean stem diameter when comparing the INF and NONF seedlings. Crucially, the results derived from the LiDAR measurements demonstrated a high correlation, exceeding 86%, when cross-referenced with traditional manual measurements, thereby confirming the reliability and accuracy of the LiDAR technique.

The temporal aspect of the study was particularly insightful: the mean stem diameter for the NONF seedlings exhibited a consistent and steady increase throughout the six-week measurement period. In stark contrast, the INF seedlings displayed an erratic and inconsistent growth pattern in their stem diameter over the same duration. This finding conclusively establishes the efficacy of Ground-based LiDAR as a powerful growth monitoring tool capable of accurately discerning the health status of oil palm seedlings.

In conclusion, this research reinforces the pivotal role of remote sensing technology in agriculture. It provides a rapid, precise, and repeatable method for the early detection of BSR symptoms, ultimately enabling more effective treatment interventions and mitigating significant yield losses in oil palm cultivation.

Figure: Measurements of oil palm seedlings: (a) manual; and (b) LiDAR point cloud measurements

Reference:

Husin, N. A., Ridu, R. C., Md Noh, N., & Bejo, S. K. (2025). Growth monitoring of healthy and BSR-infected oil palm seedlings using ground-based LiDAR. Pertanika Journal of Science & Technology, 33(3), 1137–1158.

Link:

http://www.pertanika.upm.edu.my/

Date of Input: 30/10/2025 | Updated: 30/10/2025 | ainzubaidah