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

Bejo, Siti Khairani; Shahibullah, Muhamad Syahir; Azmi, Aiman Nabilah Noor; Jahari, Mahirah

doi:10.3390/app112210878

Cited by 16 publications

(8 citation statements)

References 53 publications

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“…Furthermore, combining spectral features with ML algorithms achieves high levels of accuracy in differentiating the different biotic attacks that plants can suffer [37]. This algorithm showed sensitivity and accuracy in detecting diseases in the early stages of attack, even without a physical signal, and its performance was increased by using hyperspectral data [38]. Additionally, reflectance values across different wavelengths guarantees more information for the algorithms, improving their performance.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus

Gregori,

de Souza Loureiro,

Amorim Pessoa

et al. 2023

Remote Sensing

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Assessing different levels of red gum lerp psyllid (Glycaspis brimblecombei) can influence the hyperspectral reflectance of leaves in different ways due to changes in chlorophyll. In order to classify these levels, the use of machine learning (ML) algorithms can help process the data faster and more accurately. The objectives were: (I) to evaluate the spectral behavior of the G. brimblecombei attack levels; (II) find the most accurate ML algorithm for classifying pest attack levels; (III) find the input configuration that improves performance of the algorithms. Data were collected from a clonal eucalyptus plantation (clone AEC 0144—Eucalyptus urophilla) aged 10.3 months old. Eighty sample evaluations were carried out considering the following severity levels: control (no shells), low infestation (N1), intermediate infestation (N2), and high infestation (N3), for which leaf spectral reflectances were obtained using a spectroradiometer. The spectral range acquired by the equipment was 350 to 2500 nm. After obtaining the wavelengths, they were grouped into representative interval means in 28 bands. Data were submitted to the following ML algorithms: artificial neural networks (ANN), REPTree (DT) and J48 decision trees, random forest (RF), support vector machine (SVM), and conventional logistic regression (LR) analysis. Two input configurations were tested: using only the wavelengths (ALL) and using the spectral bands (SB) to classify the attack levels. The output variable was the severity of G. brimblecombei attack. There were differences in the hyperspectral behavior of the leaves for the different attack levels. The highest attack level shows the greatest distinction and the highest reflectance values. LR and SVM show better accuracy in classifying the severity levels of G. brimblecombei attack. For the correct classification percentage, the RL and SVM algorithms performed better, both with accuracy above 90%. Both algorithms achieved F-score values close to 0.90 and above 0.8 for Kappa. The entire spectral range guaranteed the best accuracy for both algorithms.

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Section: Discussionmentioning

confidence: 99%

Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus

Gregori,

de Souza Loureiro,

Amorim Pessoa

et al. 2023

Remote Sensing

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“…It is defined as the degree of correct diagnosis of the category in a total of two categories. This parameter indicates the number of correctly identified patterns and is formulated and defined as follows [50]:…”

Section: Criteria For Model Evaluationmentioning

confidence: 99%

Nondestructive Technique for Identifying Adulteration and Additives in Lemon Juice Based on Analyzing Volatile Organic Compounds (VOCs)

et al. 2023

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In light of the frequent occurrence of counterfeit food sold in global commercial markets, it is necessary to verify the authenticity of tasty natural-plant-based products by checking their labels, as well as their pricing and quality control. Lemon juice has repeatedly been the victim of fraud attempts by manufacturers to lower the price of products. Electronic noses are used in many fields, including the beverage industry, for classification and quality control. This involves the detection and differentiation of volatile organic compounds (VOCs) released from food. This study evaluated pure lemon juice and 11 counterfeit samples (water, lemon pulp, and wheat straw) using an electronic nose equipped with 8 metal oxide sensors to detect fraud. Chemometric methods such as principal component analysis (PCA), linear and quadratic analysis (LDA), support vector machines (SVMs), and artificial neural networks (ANNs) were used to analyze the response patterns of the sensors. The outputs of eight sensors were considered as the input of the model and the number of lemon juice groups, and its adulterations were also considered as the output of the model. Of the total data, 60% (for training), 20% (for validation), and 20% (for testing) were used. According to the results, all models had an accuracy of more than 95%, and the Nu-SVM linear function method had the highest accuracy among all models. Hence, it can be concluded that the electronic nose based on metal oxide semiconductor sensors combined with chemometric methods can be an effective tool with high efficiency for rapid and nondestructive classification of pure lemon juice and its counterfeits.

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“… Khairunniza-Bejo et al (2021) investigated the possibility of using fewer wavelengths between one and five in order to counter the economic aspect of hardware design. The results were then compared to detection results obtained from vegetation indices developed using spectral reflectance extracted from two wavelengths from the same hyperspectral sensor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In both single-band reflectance and vegetation index datasets, the results showed that a kernel with a simple linear separation between two classes would be more suitable for G. boninense detection than the others. A linear SVM with a single-band reflectance at 934 nm was identified as the best detection model because it was not only cost effective but also demonstrated high accuracy (94.8%), specificity (92.5%) and sensitivity (97.6%) ( Khairunniza-Bejo et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Cited by 16 publications

References 53 publications

Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus

Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus

Nondestructive Technique for Identifying Adulteration and Additives in Lemon Juice Based on Analyzing Volatile Organic Compounds (VOCs)

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

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