scholarly journals Advances in the Interpretation of Tomographic Images as an Early Detection Method of Oil Palm Affected by Basal Stem Rot in Colombia

Plant Disease ◽  
2016 ◽  
Vol 100 (8) ◽  
pp. 1559-1563 ◽  
Author(s):  
M. Arango ◽  
G. Martínez ◽  
G. Torres
Keyword(s):  
Early Detection ◽  
Electrical Impedance Tomography ◽  
Oil Palm ◽  
Electrical Impedance ◽  
Disease Incidence ◽  
Stem Rot ◽  
Internal Damage ◽  
Impedance Tomography ◽  
Basal Stem Rot ◽  
Palm Trees

Basal stem rot, one of the most important diseases of oil palm in Southeast Asia, has also been identified in Colombia. The increase in disease incidence in the last decade has attracted the attention of producers and researchers. In the search for a procedure that allows for the early identification of diseased palm, Cenipalma evaluated the use of electrical impedance tomography to identify the different stages of development of basal stem rot. The tomograms were compared with transversal sections of healthy and diseased oil palm trees. Following Cenipalma’s preliminary studies on early diagnosis of basal stem rot with tomography, the present study improved upon the technique by analyzing the tomograms of 209 diseased palm trees (confirmed by symptomatology), 346 asymptomatic palm trees, and 132 healthy palm trees. The minimum and maximum electric impedance values as well as the ratio between these values was recorded. The range of 1 to 95 Ω was used to represent the internal damage. The ratios averaged 5.1 for diseased, 1.9 for asymptomatic, and 1.5 for healthy palm trees. With the range and the ratio criteria established, it was possible to identify the disease in 100% of asymptomatic sampled palm trees. This study demonstrated that electrical impedance tomography is a powerful tool for early detection of basal stem rot, which can be used to establish an early disease management program.

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

scholarly journals Electronic nose for early detection of basal stem rot caused by Ganoderma in oil palm

2020 ◽  
Vol 468 ◽  
pp. 012029
Author(s):  
I Kresnawaty ◽  
A S Mulyatni ◽  
D D Eris ◽  
H T Prakoso ◽  
Tri-Panji ◽  
...  
Keyword(s):  
Early Detection ◽  
Electronic Nose ◽  
Oil Palm ◽  
Stem Rot ◽  
Basal Stem Rot

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.

scholarly journals Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 532
Author(s):  
Izrahayu Che Hashim ◽  
Abdul Rashid Mohamed Shariff ◽  
Siti Khairunniza Bejo ◽  
Farrah Melissa Muharam ◽  
Khairulmazmi Ahmad
Keyword(s):  
Machine Learning ◽  
Success Rate ◽  
Oil Palm ◽  
Model Comparison ◽  
Stem Rot ◽  
Operating Characteristics ◽  
Basal Stem Rot ◽  
Palm Trees ◽  
Rot Disease ◽  
Mlp Model

Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables; and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers; the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy.

scholarly journals Pengamatan dan Pemetaan Penyakit Busuk Pangkal Batang di Perkebunan Kelapa Sawit Menggunakan Unmanned Aerial Vehicle (UAV) dan Kamera Multispektral

2020 ◽  
Vol 16 (2) ◽  
pp. 69-80
Author(s):  
Heri Santoso
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Satellite Imagery ◽  
Oil Palm ◽  
Cloud Cover ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Oil Palm Plantation ◽  
Palm Trees ◽  
Aerial Vehicle

Surveillance and Mapping of Basal Stem Rot Disease in Oil Palm Plantation Using Unmanned Aerial Vehicle (UAV) and Multispectral Camera Basal stem rot (BSR) disease caused by Ganoderma boninensis is still a major disease in oil palm plantations both in Indonesia and Malaysia. In some countries, remote sensing approach has been used for monitoring BSR in oil palm plantation. However, the utilization of satellite imagery in remote sensing especially in vegetation study on the tropical region was often limited by cloud cover. A drone or unmanned aerial vehicle (UAV) utilization is the best way to deal with cloud cover in the tropic region. Machine learning of random forest (RF) and satellite imagery used in the BSR study produced good accuracy. This research was aimed to identify and monitor the BSR infection on individual oil palm trees using an UAV and multispectral camera and RF classification. The results showed that the data acquired from UAV was affected by cloud shadows. The RF classification of healthy and infected oil palm trees by BSR disease and the spreading map of BSR infection was affected by cloud shadows. The highest accuracy of healthy and infected oil palm by BSR was 79.49%. Reflectance calibrator, digital to reflectance conversion, and model implications to build spreading map of BSR infection need to be conducted both on the clear area and the cloud shadow-covered area. Moreover, the UAV-based data should be considering the cloud view on the coverage area.

GammaScorpion: mobile gamma-ray tomography system for early detection of basal stem rot in oil palm plantations

2013 ◽  
Vol 52 (3) ◽  
pp. 036502 ◽  
Author(s):  
Jaafar Abdullah ◽  
Hearie Hassan ◽  
Mohamad Rabaie Shari ◽  
Salzali Mohd ◽  
Mahadi Mustapha ◽  
...  
Keyword(s):  
Early Detection ◽  
Oil Palm ◽  
Gamma Ray ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Tomography System

scholarly journals Karakteristik antibodi anti Ganoderma sp. yang dihasilkan dengan menggunakan jenis dan sumber antigen yang berbeda [Characteristic of antibodies againt Ganoderma sp produced from different types and sources of antigens]

2017 ◽  
Vol 85 (1) ◽  
Author(s):  
Irma KRESNAWATY ◽  
Kholis A AUDAH ◽  
Hasim MUNAWAR ◽  
Happy WIDIASTUTI
Keyword(s):  
Amino Acid ◽  
Early Detection ◽  
Oil Palm ◽  
Stem Rot ◽  
Protein Profiles ◽  
Basal Stem Rot ◽  
Amino Acid Compositions ◽  
Different Types ◽  
Chicken Eggs ◽  
Important Disease

Basal stem rot (BSR) disease caused by  Ganoderma sp. is the most important disease in oil palm plantations.The effectivity of BSR control depends on early detection of this disease. The earlier the disease is known, the severity of damage could be prevented. Therefore, technology for early detection of Ganoderma infection is very important. Immunochromatographic techniques based on the reaction of antigens and antibodies can be developed for detection of Ganoderma sp infection. The objective of the study was to produce antibodies using different Ganoderma sp. In this study, immunoglobulin Y ( IgY ) against Ganoderma sp produced in chicken eggs was used as the source of antibodies. Laying hens were immunized with several types of Ganoderma sp. because it is known to have genetic variations. The source of Ganoderma sp. isolates were mycelium and exudates. The polyclonal IgY antibodies produced economically and abundantly.  The antibodies derived from the mycelium showed more consistent results compared with those derived from the exudates. In addition, the antibodies derived from Ganoderma sp of Cimulang and Bekri showed higher reactivity  with some of the antigens compared to those from Cisalak Baru (CSB). The characteristics and the protein profiles of antibodies produced using Cimulang, Bekri  and Cisalak Baru isolates were vary in term of,  sensitivity and amino acid compositions

Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm

2014 ◽  
Vol 101 ◽  
pp. 48-54 ◽  
Author(s):  
Shohreh Liaghat ◽  
Shattri Mansor ◽  
Reza Ehsani ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Sariah Meon ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Early Detection ◽  
Oil Palm ◽  
Stem Rot ◽  
Mid Infrared ◽  
Basal Stem Rot ◽  
Mid Infrared Spectroscopy ◽  
Rot Disease

Ganoderma boninense (basal stem rot of oil palm).

2021 ◽  
Author(s):  
R. R. M. Paterson
Keyword(s):  
Papua New Guinea ◽  
Oil Palm ◽  
Disease Incidence ◽  
New Guinea ◽  
The Philippines ◽  
Peninsular Malaysia ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Oil Palms ◽  
Serious Disease

Abstract Darmono (1998) described disease incidence of 51% in some areas of Sumatra, Indonesia. More recent estimation of disease is provided for Malaysia and Sumatra in Paterson (2019, a, b). Basal stem rot (BSR) infection of oil palms in Thailand remains low (Likhitekaraj and Tummakate, 2000): Pornsuriya et al. (2013) indicated that levels were at 1.53%, although they state that the disease was experienced widely in southern plantations. The BSR levels may be influenced by being contiguous with peninsular Malaysia where the disease levels are high (Paterson, 2019b). A scenario of 10% infection currently is a reasonable scenario for Thailand. Papua New Guinea has an important palm oil industry (Corley and Tinker, 2015). The level of BSR in Papua New Guinea is not as high as in some other areas of South-East Asia although 50% has been recorded (Pilotti, 2005; Pilotti et al., 2018). An average of 25% infection is a plausible scenario for this country as the initial level is lower than that used for Malaysia and Sumatra, Indonesia. The Philippines has an oil palm industry at a lower level than that of Thailand (Corley and Tinker, 2015). BSR will be low as the plantations have not been established recently (Woods, 2015) and distances between plantations will be high. Equally, there are no reports of infection by BSR in the literature. Hence a low level of BSR can be expected. BSR of oil palms has been recorded widely throughout the tropics and is considered as a serious disease in Africa and South America.

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