Detection of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Laser Scanning Data

Khairunniza-Bejo, Siti; Vong, Chin Nee

doi:10.1016/j.aaspro.2014.11.023

Cited by 29 publications

(22 citation statements)

References 3 publications

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“…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. The scanning of oil palm trunks and canopies was done by using Faro Laser Scanner 3D data.…”

Section: ) Terrestrial Laser Scanning (Tls)mentioning

confidence: 99%

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

et al. 2021

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Ganoderma disease is a kind of infection that actuates oil palm death. Early detection of Ganoderma disease is the most recommended strategy for proper treatment and disease control plan to be taken promptly. In this paper, the detection methods for Ganoderma disease were reviewed and categorized accordingly. It was found that the combination of remote sensors and machine learning techniques could identify the disease up to four severity levels, including the early stage of infection. It also significantly reduced the labor and time costs compared to the traditional visual inspection and lab-based approaches. In terms of machine learning, support vector machine (SVM) using the idea of finding a hyperplane was suggested as the best classifier in several studies. Despite only one research was done on ANN and no research evaluating CNN and GAN in Ganoderma disease detection; ANN, CNN and GAN were recognized as the potential machine learning techniques that could enhance the detection system.INDEX TERMS Basal stem rot, Ganoderma, machine learning, oil palm, remote sensors.

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Section: ) Terrestrial Laser Scanning (Tls)mentioning

confidence: 99%

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

et al. 2021

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“…The scanner was mounted on a surveying tripod at a height of 1 m and a distance of 1.5 m from an oil palm tree [35,36]. The depth image from a single viewpoint of the tree did not provide sufficient information to construct a complete, three-dimensional (3D) point cloud model; therefore, each tree was scanned at four different positions [18,35,37,38] (Figure 2). A total of 80 oil palm trees used in this study, with 40 each for training and testing process being pre-determined by the experts from the MPOB using manual inspections based on visible symptoms appearing on the tree [39].…”

Section: Data Collectionmentioning

confidence: 99%

“…A total of 80 oil palm trees used in this study, with 40 each for training and testing process being pre-determined by the experts from the MPOB using manual inspections based on visible symptoms appearing on the tree [39]. For each process, the oil palm trees were categorized into 4 healthiness levels with 10 trees for each level: T0 (healthy palm, no foliage symptoms (0%), no fruiting body), T1 (mild infection, minimal foliage symptoms (0-25%), fruiting body), T2 (moderate infection, foliage symptoms (25-50%), fruiting body), and T3 (severe infection, foliage symptoms (50-75%), fruiting body) [26,37]. Subsequent results and discussions in this paper were based on these levels.…”

Section: Data Collectionmentioning

confidence: 99%

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

et al. 2020

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The oil palm industry is vital for the Malaysian economy. However, it is threatened by the Ganoderma boninense fungus, which causes basal stem rot (BSR) disease. Foliar symptoms of the disease include the appearance of several unopened spears, flat crowns, and small crown size. The effect of this disease depends on the severity of the infection. Currently, the disease can be detected manually by analyzing the oil palm tree’s physical structure. Terrestrial laser scanning (TLS) is an active ranging method that uses laser light, which can directly represent the tree’s external structure. This study aimed to classify the healthiness levels of the BSR disease using a machine learning (ML) approach. A total of 80 oil palm trees with four different healthiness levels were pre-determined by the experts during data collection with 40 each for training and testing. The four healthiness levels are T0 (healthy), T1 (mildly infected), T2 (moderately infected), and T3 (severely infected), with 10 trees in each level. A terrestrial scanner was mounted at a height of 1 m, and each oil palm was scanned at four positions at a distance of 1.5 m around the tree. Five tree features were extracted from the TLS data: C200 (crown slice at 200 cm from the top), C850 (crown slice at 850 cm from the top), crown area (number of pixels inside the crown), frond angle, and frond number. C200 and C850 were obtained using the crown stratification method, while the other three features were obtained from the top-down image. The obtained features were then analyzed by principal component analysis (PCA) to reduce the dimensionality of the dataset and increase its interpretability while at the same time minimizing information loss. The results showed that the kernel naïve Bayes (KNB) model developed using the input parameters of the principal components (PCs) 1 and 2 had the best performance among 90 other models with a multiple level accuracy of 85% and a Kappa coefficient of 0.80. Furthermore, the combination of the two highest PC variance with the most weighted to frond number, frond angle, crown area, and C200 significantly contributed to the classification success. The model also could classify healthy and mildly infected trees with 100% accuracy. Therefore, it can be concluded that the ML approach using TLS data can be used to predict early BSR infection with high accuracy.

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“…Generally, it can be grouped into three approaches: manual, non-remote (lab-based) and remote (non-invasive) techniques. The manual detection is considerably low in accuracy and labour intensive 18 . Conversely, non-remote or lab-based methods are costly, complex, labour intensive and are not designed for outdoor conditions 19 .…”

mentioning

confidence: 99%

“…The first study on the use of TLS for BSR detection was performed by Khairunniza-Bejo and Vong 18 . The results showed that there were correlations between the oil palm trunk's perimeter, Diameter-Based Height (DBH) and canopy area with the BSR disease.…”

mentioning

confidence: 99%

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

Husin

Khairunniza-Bejo

Abdullah

et al. 2020

Sci Rep

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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 ...

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Detection of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Laser Scanning Data

Cited by 29 publications

References 3 publications

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

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

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

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

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