Multi-temporal analysis of terrestrial laser scanning data to detect basal stem rot in oil palm trees

Husin, Nur Azuan; Khairunniza-Bejo, Siti; Abdullah, Ahmad Fikri; Kassim, Muhamad Saufi Mohd; Ahmad, Desa

doi:10.1007/s11119-021-09829-4

Cited by 10 publications

(10 citation statements)

References 54 publications

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“…To understand the disease development for long-term solutions, Husin et al (2021) used temporal laser scanning data to identify suitable time frames for monitoring the progress of the disease symptoms. Crown area and canopy strata at 850 cm from the top were identified as the most suitable parameters to be used.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Crown area and canopy strata at 850 cm from the top were identified as the most suitable parameters to be used. It was found that BSR infection can be discovered by comparing the 2 and 4-month scan images of the oil palm tree ( Husin et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The result also demonstrated the effect of G. Boninense infection can be differentiated at the later stage as early as 4-month after the first inspection Therefore, the current practice of disease censuses that are done every 6 or 12 months could be shortened to a faster cycle. Since the BSR infection can kill a palm tree in 12 months, a more regular census could help avoid crop damage by allowing early identification, prevention and treatment measures ( Husin et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…However it was worth noting, by using t-test and multiple linear regression equations to analyze the crown density profile, the five most significant strata were able to be fitted into the prediction model that produced 92.5% accuracy in disease classification. Comparing methods of machine learning, KNB was found to be the best in classifying BSR based on C200, C850, crown area, frond number and frond angle parameters ( Husin et al, 2020c ; Khairunniza-Bejo & Vong, 2014 ; Husin et al, 2020a , 2020b , 2021 ).…”

Section: Summary Of Literature Reviewmentioning

confidence: 99%

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Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods

Haw

Lai

Chuah

et al. 2023

PeerJ Computer Science

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Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Summary Of Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods

Haw

Lai

Chuah

et al. 2023

PeerJ Computer Science

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“…However, the procedures entail stem collection, which may result in plant damage and eventual destruction. Other proposed methods used include an electronic nose (e-nose) [15,16], electrical impedance [17,18], tomography [19,20], thermal imaging [21,22], multispectral imaging [23,24], spectroscopy or hyperspectral data [25][26][27][28][29][30], and a terrestrial laser scanner (TLS) [31][32][33][34][35]. A comprehensive review of sensors used to detect BSR by [36] has found that each technique has different scores in terms of accuracies and limitations.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Asymptomatic Ganoderma boninense Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine

et al. 2021

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Breeding programs to develop planting materials resistant to G. boninense involve a manual census to monitor the progress of the disease development associated with various treatments. It is prone to error due to a lack of experience and subjective judgements. This study focuses on the early detection of G. boninense infection in the oil palm seedlings using near infra-red (NIR)-hyperspectral data and a support vector machine (SVM). The study aims to use a small number of wavelengths by using 5, 4, 3, 2, and 1 band reflectance as datasets. These results were then compared with the results of detection obtained from the vegetation indices developed using spectral reflectance taken from the same hyperspectral sensor. Results indicated a kernel with a simple linear separation between two classes would be more suitable for G. boninense detection compared to the others, both for single-band reflectance and vegetation index datasets. A linear SVM which was developed using a single-band reflectance at 934 nm was identified as the best model of detection since it was not only economical, but also demonstrated a high score of accuracy (94.8%), sensitivity (97.6%), specificity (92.5%), and area under the receiver operating characteristic curve (AUC) (0.95).

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Early Detection of Plant Disease Infection Using Hyperspectral Data and Machine Learning

Azmi¹,

Khairunniza-Bejo²,

Jahari³

et al. 2023

IoT and AI in Agriculture

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Multi-temporal analysis of terrestrial laser scanning data to detect basal stem rot in oil palm trees

Cited by 10 publications

References 54 publications

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods

Non-Destructive Detection of Asymptomatic Ganoderma boninense Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine

Early Detection of Plant Disease Infection Using Hyperspectral Data and Machine Learning

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