Analysis of rhizobacterial community associated with the occurrence of Ganoderma basal stem rot disease in oil palm by Illumina next-generation sequencing

2021 ◽  
Vol 204 (1) ◽  
Author(s):  
Jakarat Anothai ◽  
Thanunchanok Chairin
Keyword(s):  
Next Generation Sequencing ◽  
Oil Palm ◽  
Stem Rot ◽  
Next Generation ◽  
Basal Stem Rot ◽  
Rot Disease ◽  
Generation Sequencing

scholarly journals EFFECT OF MICRONUTRIENTS-ENRICHED FERTILIZERS ON BASAL STEM ROT DISEASE INCIDENCE AND SEVERITY ON OIL PALM (ELAEIS GUINEENSIS JACQ.) SEEDLINGS

2014 ◽  
Vol 11 (10) ◽  
pp. 1841-1859 ◽  
Author(s):  
Fabien Fonguimgo Tengoua ◽  
Mohamed M. Hanafi ◽  
A. S. Idris ◽  
Kadir Jugah ◽  
Jamaludin Nurul Mayziatul Azwa ◽  
...  
Keyword(s):  
Oil Palm ◽  
Elaeis Guineensis ◽  
Disease Incidence ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Rot Disease

Identification of Ganoderma, the causal agent of basal stem rot disease in oil palm using a molecular method

2005 ◽  
Vol 159 (1) ◽  
pp. 159-170 ◽  
Author(s):  
C. Utomo ◽  
S. Werner ◽  
F. Niepold ◽  
H.B. Deising
Keyword(s):  
Oil Palm ◽  
Causal Agent ◽  
Stem Rot ◽  
Molecular Method ◽  
Basal Stem Rot ◽  
Rot Disease

scholarly journals GIS Mapping of Basal Stem Rot Disease in Relation to Soil Series Among Oil Palm Smallholders

2016 ◽  
Vol 11 (1) ◽  
pp. 2-12 ◽  
Author(s):  
K. Parthiban ◽  
R. Vanitah ◽  
K. Jusoff ◽  
A.A. Nordiana ◽  
A.R. Anuar ◽  
...  
Keyword(s):  
Oil Palm ◽  
Stem Rot ◽  
Gis Mapping ◽  
Soil Series ◽  
Basal Stem Rot ◽  
Rot Disease

scholarly journals Classification of Non-Infected and Infected with Basal Stem Rot Disease Using Thermal Images and Imbalanced Data Approach

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2373
Author(s):  
Izrahayu Che Hashim ◽  
Abdul Rashid Mohamed Shariff ◽  
Siti Khairunniza Bejo ◽  
Farrah Melissa Muharam ◽  
Khairulmazmi Ahmad
Keyword(s):  
Oil Palm ◽  
Potential Temperature ◽  
Model Performance ◽  
Imbalanced Data ◽  
Temperature Characteristic ◽  
Stem Rot ◽  
Operating Characteristics ◽  
Basal Stem Rot ◽  
Palm Trees ◽  
Rot Disease

Basal stem rot (BSR) disease occurs due to the most aggressive and threatening fungal attack of the oil palm plant known as Ganoderma boninense (G. boninense). BSR is a disease that has a significant impact on oil palm crops in Malaysia and Indonesia. Currently, the only sustainable strategy available is to extend the life of oil palm trees, as there is no effective treatment for BSR disease. This study used thermal imagery to identify the thermal features to classify non-infected and BSR-infected trees. The aims of this study were to (1) identify the potential temperature features and (2) examine the performance of machine learning (ML) classifiers (naïve Bayes (NB), multilayer perceptron (MLP), and random forest (RF) to classify oil palm trees that are non-infected and BSR-infected. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approaches such as random undersampling (RUS), random oversampling (ROS) and synthetic minority oversampling (SMOTE) in these classifications due to the different sample sizes. The study found that the Tmax feature is the most beneficial temperature characteristic for classifying non-infected or infected BSR trees. Meanwhile, the ROS approach improves the curve region (AUC) and PRC results compared to a single approach. The result showed that the temperature feature Tmax and combination feature TmaxTmin had a higher correct classification for the G. boninense non-infected and infected oil palm trees for the ROS-RF and had a robust success rate, classifying correctly 87.10% for non-infected and 100% for infected by G. boninense. In terms of model performance using the most significant variables, Tmax, the ROS-RF model had an excellent receiver operating characteristics (ROC) curve region (AUC) of 0.921, and the precision–recall curve (PRC) region gave a value of 0.902. Therefore, it can be concluded that the ROS-RF, using the Tmax, can be used to predict BSR disease with relatively high accuracy.

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