Analysis of Changes in Oil Palm Canopy Architecture From Basal Stem Rot Using Terrestrial Laser Scanner

Area of Science:

• Agricultural remote sensing
• Plant pathology
• Forestry and botanical sciences

Background:

• Basal stem rot (BSR), caused by Ganoderma fungus, severely impacts oil palm (Elaeis guineensis) yields, causing billions in losses.
• Early, accurate, and non-destructive detection of BSR is crucial for effective disease management and mitigating economic losses.
• Existing methods struggle with early-stage symptom identification, necessitating advanced diagnostic tools.

Purpose of the Study:

• To propose and evaluate an efficient image processing technique using Terrestrial Laser Scanning (TLS) point clouds for early BSR detection in oil palms.
• To correlate TLS-derived canopy architectural parameters with BSR severity levels.
• To establish TLS as a viable tool for non-destructive, early-stage diagnosis of Ganoderma infection in oil palm plantations.

Main Methods:

• Collected TLS data from 40 oil palm trees (9 years old) with varying BSR severity levels.
• Processed TLS point clouds to generate overhead 2D canopy images.
• Analyzed canopy architecture using crown pixel count, frond angle, and frond number.

Main Results:

• Crown pixel, frond angle, and frond number showed significant correlations with BSR severity (R² up to 0.97).
• Frond number analysis distinguished all BSR severity levels, detecting infection as early as mild (T1).
• Crown pixel and frond angle parameters detected Ganoderma infection at moderate severity (T2), with some overlap at T1.

Conclusions:

• TLS is effective in detecting low-level Basal Stem Rot infection in oil palms as early as the mild severity stage (T1).
• Canopy architectural analysis via TLS provides a reliable, non-destructive method for early BSR detection and disease management.
• This technology offers significant benefits for managing oil palm plantation health and preventing yield losses.