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

2021 ◽  
Author(s):  
Nur A. Husin ◽  
Siti Khairunniza-Bejo ◽  
Ahmad F. Abdullah ◽  
Muhamad S. M. Kassim ◽  
Desa Ahmad
Keyword(s):  
Oil Palm ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Temporal Analysis ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Palm Trees ◽  
Multi Temporal

scholarly journals Relationship of Oil Palm Crown Features Extracted Using Terrestrial Laser Scanning for Basal Stem Rot Disease Classification

2021 ◽  
Vol 34 ◽  
pp. 1-10
Author(s):  
Nur A. Husin ◽  
Siti Khairunniza Bejo ◽  
Ahmad F. Abdullah ◽  
Muhamad S.M. Kassim ◽  
Desa Ahmad
Keyword(s):  
Oil Palm ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Physical Symptoms ◽  
Image Processing Technique ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Crown Area ◽  
Relationship Of ◽  
The Relationship

The oil palm is the largest plantation industry in Malaysia. It has been one of the major contributors to the country’s economy and the main pillar of the commodity sectors. For over 40 years, the oil palm industry has faced a lethal and incurable disease, Basal Stem Rot (BSR), which is caused by a type of bracket fungus, Ganoderma boninense. The oil palm physical symptoms infected by BSR disease are appearance of many unopened spears, flattening of crown and smaller crown size. Terrestrial Laser Scanning (TLS, also known as ground-based LiDAR) can be used to provide accurate and precise information on tree morphology with high resolution. This study proposed an image processing technique using the ground input data taken from a TLS. Five parameters were used in the study are number of laser hits in strata 200 cm and 850 cm from the top, namely as C200 and C850, respectively, crown area, frond number and frond angle.  The objectives of this study are to analyse the relationship between the parameters and to study the relationship of the parameters with the levels of BSR disease. Results have shown that all parameters were significant in all levels of healthiness with p-values less than 5%. Frond number and frond angle showed the highest correlation value, which is equal to -0.94. Frond angle is increasing, while frond number and crown area are decreasing concurrently with the severity levels of BSR infection.

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

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1624
Author(s):  
Nur A. Husin ◽  
Siti Khairunniza-Bejo ◽  
Ahmad F. Abdullah ◽  
Muhamad S. M. Kassim ◽  
Desa Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil Palm ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Principal Component ◽  
Physical Structure ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Crown Area ◽  
Stratification Method

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.

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 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.

scholarly journals Deteksi Ganoderma secara molekuler pada kebun kelapa sawit yang diberi perlakuan biofungisida Ganor (Molecular detection of Ganoderma on oil palm plantation treated with Ganor biofungicide)

2018 ◽  
Vol 86 (1) ◽  
Author(s):  
Hayati MINARSIH ◽  
Happy WIDIASTUTI ◽  
Djoko SANTOSO
Keyword(s):  
Sex Ratio ◽  
Molecular Analysis ◽  
Oil Palm ◽  
Pathogenic Fungus ◽  
Reproductive Organs ◽  
Stem Rot ◽  
Molecular Technique ◽  
Basal Stem Rot ◽  
Palm Trees ◽  
Rot Disease

AbstractGanor organic fungicide potentially reduces Ganoderma, a pathogenic fungus causing basal stem rot disease. Application of Ganor on oil palm trees in the plantation attacked Ganoderma, inhibits the growth of Ganoderma fruiting bodies, improves rooting and stimulates the opening of the spear leaf. This study aims to identify molecularly the presence of Ganoderma in oil palm trees that have been attacked by Ganoderma routinely treated with Ganor for three months. Molecular analysis was performed by PCR using Ganoderma specific primers. The analysis results of sample from trunks and roots of  oil palm, indicating that the Ganoderma infected oil palm which has been treated with Ganor, were relatively free (96.4%) of Ganoderma. Of the 28 samples examined of treated plants, 27 samples did not indicate the presence of Ganoderma specific DNA band. On the other hand, the untreated oil palm trees infected by Ganoderma were still detected by the appearence of  DNA bands specific to Ganoderma. The results of molecular analysis indicated that Ganor treatments can effectively reduce the attack rate of Ganoderma in oil palm trees in the plantation infected by Ganoderma. However, the use of the molecular technique for early detection needs to be further tested to evaluate its consistency prior to introduction to the commercial growers. The reproducibility can be confirmed by repeating the experiment using more samples. Ganor effectiveness in curing oil palm trees infected by Ganoderma, maybe indicated by the ability of the reproductive organs to develop, particularly female flowers. The sex ratio of Ganor treated oil palms was clearly higher than that of control palms in 10 to 12 weeks after the treatment.[Keywords: organic fungicides, stem rot, molecular analysis, Elais guinensis Jack.] AbstrakFungisida organik Ganor berpotensi mengurangi serangan Ganoderma, cendawan patogenik penyebab penyakit busuk pangkal batang. Aplikasi Ganor pada tanaman kelapa sawit di kebun yang terserang Ganoderma, menghambat pertumbuhan tubuh buah Ganoderma, memper-baiki perakaran dan merangsang pembukaan daun tombak. Penelitian ini bertujuan untuk mengidentifikasi secara molekuler adanya Ganoderma pada tanaman kelapa sawit terserang Ganoderma yang telah mendapat perlakuan Ganor secara rutin selama tiga bulan. Analisis molekuler dilakukan dengan teknik PCR menggunakan primer DNA spesifik Ganoderma. Hasil analisis sampel batang dan akar tanaman kelapa sawit, menunjukkan bahwa tanaman Perlakuan, yaitu kelapa sawit terserang Ganoderma yang telah mendapat perlakuan Ganor, 96,4% bebas Ganoderma. Dari 28 sampel tanaman Perlakuan yang diperiksa, 27 sampel tidak menunjukkan adanya pita DNA spesfik Ganoderma. Sementara itu pada tanaman Kontrol, yaitu tanaman kelapa sawit terserang Ganoderma dan tidak mendapat perlakuan Ganor, 100% masih terdeteksi adanya Ganoderma. Dari 7 sampel tanaman kontrol yang diperiksa semuanya menunjukkan adanya pita DNA spesifik Ganoderma. Hasil analisis molekuler ini mengindikasikan bahwa perlakuan Ganor efektif mengurangi tingkat serangan Ganoderma pada tanaman kelapa sawit di kebun yang terinfeksi Ganoderma. Namun demikian, untuk lebih meyakinkan praktisi perkebunan, penggunakan teknik molekuler ini masih perlu diuji lebih lanjut terkait konsistensinya. Reprodusibilitas dapat dikonfirmasi dengan mengulangi percobaan menggunakan lebih banyak sampel. Efektivitas Ganor dalam menyehatkan tanaman kelapa sawit terserang Ganoderma ini, terindikasi juga dari perkembangan organ reproduktifnya. Sex ratio meningkat dalam waktu 10 hingga 12 minggu setelah perlakuan.[Kata Kunci:  fungisida organik, busuk pangkal  batang, analisis molekuler, Elais guinensis Jack. ]

scholarly journals Identification of a partial oil palm polygalacturonase-inhibiting protein (EgPGIP) gene and its expression during basal stem rot infection caused by Ganoderma boninense

2010 ◽  
Vol 9 (46) ◽  
pp. 7788-7797 ◽  
Author(s):  
R Al Obaidi Jameel ◽  
Mohd Yusuf Yusmin ◽  
Chin Chong Tey ◽  
Mhd Noh Normahnani ◽  
Yasmin Othman Rofina
Keyword(s):  
Oil Palm ◽  
Stem Rot ◽  
Ganoderma Boninense ◽  
Basal Stem Rot ◽  
Polygalacturonase Inhibiting Protein ◽  
Inhibiting Protein
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