The diagnose of oil palm disease using Naive Bayes Method based on Expert System Technology

Nababan, Marlince; Laia, Yonata; Sitanggang, Delima; Sihombing, Oloan; Indra, Evta; Siregar, Saut Dohot; Purba, Windania; Mancur, Roy

doi:10.1088/1742-6596/1007/1/012015

Cited by 17 publications

(14 citation statements)

References 8 publications

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“…Nababan et al [46] employed NB in an intelligence-based application to diagnose the type of oil palm plant disease. Bayes method was conducted based on the formulation of the recognized symptoms.…”

Section: Naïve Bayes (Nb)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: Naïve Bayes (Nb)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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“…Nababan et al [7] founded out a system for oil palm where features are extracted using probability function and classification using Naive Bayes is done. It provided 80% accuracy.…”

Section: It Uses Fusion Classification Technique Which Include a Combmentioning

confidence: 99%

A Comparative Analysis on AI Techniques for Grape Leaf Disease Recognition

Pai¹,

Thomas²

2021

Communications in Computer and Information Science

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Grape or Grapevine (Vitis Vinifera) belonging to the Vitaceae family is one of India's most commercially important fruit crops. It is a widely temperate crop that has become accustomed to the subtropical climate of the peninsula of India. In a Grape vineyard, there are more chances of grape fruits and its leaf to confront with diseases. Manual Observation is not feasible and is also time constrained for experts and agronomists to track with. Inorder to predict the disease in early stage, we deal with a literature survey on different methods pertinent to disease identification and classification. Through this survey, we find out the best method to be followed for disease tracking. The composition at first, presents a detailed terminology related to different kind of grape leaf diseases. Furthermore, a survey regarding automated grape leaf disease identification and categorization methods are carried out, which deals with techniques like machine learning and deep learning.

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“…Studies have also shown that the classification accuracies slightly increased when the training set size was increased [68,69]. The NB classifier was also used to classify oil palm disease on the basis of its symptoms with high accuracy: 80% [25], 84% [70], and 92.25% [71], which showed that the NB classifier is well suited with the oil palm disease classification. Moreover, the classification model using the ML approach gave better results compared to the classification model using a combination of features [21], where the accuracy for multiple levels was 80%.…”

Section: Best Model Comparisonmentioning

confidence: 99%

“…An SVM with a polynomial kernel (soft margin) executed the classification with 95% accuracy. Meanwhile, the naïve Bayes (NB) method was used to diagnose oil palm disease in Indonesia [25] on the basis of various symptoms identified in the leaves, spear, stem, and fruits. According to the results, the diagnosis of oil palm disease was achieved with 80% accuracy.…”

Section: Introductionmentioning

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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The diagnose of oil palm disease using Naive Bayes Method based on Expert System Technology

Cited by 17 publications

References 8 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

A Comparative Analysis on AI Techniques for Grape Leaf Disease Recognition

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

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