Application of Ground-Based LiDAR for Analysing Oil Palm Canopy Properties on the Occurrence of Basal Stem Rot (BSR) Disease

Abstract

Ground-based LiDAR also known as terrestrial Laser Scanning (tLS) technology is an active remote sensing imaging method said to be one of the latest advances and innovations for plant phenotyping. Basal Stem Rot (BSR) is the most destructive disease of oil palm in Malaysia that is caused by whiterot fungus Ganoderma boninense, the symptoms of which include flattening and hanging-down of the canopy, shorter leaves, wilting green fronds and smaller crown size. therefore, until now there is no critical investigation on the characterisation of canopy architecture related to this disease using tLS method was carried out. This study proposed a novel technique of BSR classification at the oil palm canopy analysis using the point clouds data taken from the TLS. A total of 40 samples of oil palm trees at the age of nine-years-old were selected and 10 trees for each health level were randomly taken from the same plot. The trees were categorised into four health levels -T0, T1, T2 and T3, which represents the healthy, mildly infected, moderately infected and severely infected, respectively. the tLS scanner was mounted at a height of 1 m and each palm was scanned at four scan positions around the tree to get a full 3D image. Five parameters were analysed: S200 (canopy strata at 200 cm from the top), S850 (canopy strata at 850 cm from the top), crown pixel (number of pixels inside the crown), frond angle (degree of angle between fronds) and frond number. the results taken from statistical analysis revealed that frond number was the best single parameter to detect BSR disease as early as T1. In classification models, a linear model with a combination of parameters, ABD -A (frond number), B (frond angle) and D (S200), delivered the highest average accuracy for classification of healthy-unhealthy trees with an accuracy of 86.67 per cent. It also can classify the four severity levels of infection with an accuracy of 80 per cent. This model performed better when compared to the severity classification using frond number. the novelty of this research is therefore on the development of new approach to detect and classify BSR using point clouds data of tLS.LiDAR (Light detection and ranging) is an active remote sensing technology similar to Radar (Radio detection and ranging) but which uses laser light. LiDAR measures the distance or range to a target by illuminating the target with a pulsed laser light and measuring the reflected pulses with a sensor. It can directly represent external structures and carry out profiling for objects or trees. Laser Scanning (LS) profiling systems consist of a measuring instrument that can measure vertical angles, horizontal angles, and distances with a high standard of accuracy and speed by means of a mobile mirror or prism system allowing the mapping of morphological features of targets 1 .Research and field site works used extensive biometric data in estimating tree properties while offering the possibility of reducing inventory costs. Previous studies have demonstrated that LiDAR could be used to ...

Classification of Basal Stem Rot Disease in Oil Palm Plantations Using Terrestrial Laser Scanning Data and Machine Learning

“…The NB classifier was also used to classify oil palm disease on the basis of its symptoms with high accuracy: 80% [25], 84% [70], and 92.25% [71], which showed that the NB classifier is well suited with the oil palm disease classification. Moreover, the classification model using the ML approach gave better results compared to the classification model using a combination of features [21], where the accuracy for multiple levels was 80%.…”

“…This study aimed to fill the research gap on the capability of ML for BSR classification using TLS data, which has not been explored. Therefore, the same 5 features used in [21] were also used in this study: C200 (crown slice located 200 cm from the top), C850 (crown slice located 850 cm from the top), crown area, frond number, and frond angle. The top is defined as the highest vegetation visible on the tree.…”

“…However, very few TLS studies have focused on the detailed oil palm tree architecture. Recently, the authors of [20] studied the changes in the oil palm architecture, owing to the BSR disease, whereas [21] used single and combined features extracted from oil palm trees using the TLS method. The latter study used the raw data and a regression approach to measure how close it fits the expected model's line and curve.…”

Discovering the Ganoderma Boninense Detection Methods Using Machine Learning: A Review of Manual, Laboratory, and Remote Approaches

“…It operates by releasing a pulsed laser light to illuminate the target. The reflected pulses are then measured with a sensor to directly represent the external structures, meanwhile, carry out profiling for the targeted objects [31]. Khairunniza-Bejo and Vong [32] was the first that proposed the use of TLS application for the study of BSR detection.…”

Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests

“…A ground-based LiDAR also known as terrestrial laser scanning (TLS) system for detecting basal stem rot (caused by white-rot fungus Ganoderma boninense) in oil palm canopies was reported in [174]. A total of 40 samples of oil palm trees at the age of nineyears-old (with heights between 10 to 11 meters) were selected and categorized into four disease levels: healthy/disease-free, mildly infected, moderately infected, and severely infected.…”