Gray Level Co-occurrence Matrix Algorithm Research Articles

Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree

Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process. Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. A 1D-signal sequence is constructed on each layer, which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated. The features are trained/classified using the decision tree classifiers that classify the pest attack, disease incidence, and nutrient deficiency categories. The proposed approach provides a precision, accuracy, specificity, sensitivity, and F1-score of 97 %, 97.88 %, 96.52 %, 96.7 %, and 96.7 % respectively.

Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection

Computed tomography (CT) films are used to construct cross-sectional pictures of a particular region of the body by using many x-ray readings that were obtained at various angles. There is a general agreement in the medical community at this time that chest CT is the most accurate approach for identifying COVID-19 disease. It was demonstrated that chest CT had a higher sensitivity than reverse transcription polymerase chain reaction (RT-PCR) for the detection of COVID-19 illness. This article presents gray-level co-occurrence matrix (GLCM) texture feature extraction and convolutional neural network (CNN)-enabled optimized diagnostic model for COVID-19 detection. In this diagnostic model, CT scan images of patients are given as input. Firstly, GLCM algorithm is used to extract texture features from the CT scan images. This feature extraction helps in achieving higher classification accuracy. Classification is performed using CNN. It achieves higher accuracy than the k-nearest neighbors (KNN) algorithm and multi-layer preceptor (MLP). The accuracy of GLCM based CNN is 99%, F1 score is 99% and the recall rate is also 98%. CNN has achieved better results than MLP and KNN algorithms for COVID-19 detection.

Image Analysis and Classification Using HRSVM-CNN for Land-Cover Classification by Using Remote Sensing Images

Objective: To effectively analyze diverse satellite images and derive valuable insights, it's crucial to employ efficient methods for classification and image processing. However, due to imperfections in image formats and sensor data, satellite imagery often contains flaws and inaccuracies, requiring extra steps to enhance its quality. The proposed solution involves two key techniques: segmenting the input image using a Hybrid HRSVM-CNN and classifying the resulting high-resolution remote sensing image using a Convolutional Neural Network. This combined approach addresses the challenges posed by image inconsistencies and aims to improve the accuracy and efficiency of current methods for satellite image analysis. Methods: In this research, a high-resolution Support Vector Machine-Convolutional Neural Network (Hybrid HRSVM-CNN) and texture characteristics are used to create an automated land identification method for satellite Remote sensing (RS) images. This approach's main focus is segmentation using the Bendlet Transform and Improved Chan-Vese, and it also does classification using a Hybrid HRSVM-CNN based on feature extraction and gray-level co-occurrence matrix algorithm. Findings: The proposed classification method's accuracy was evaluated against several other classification algorithms, including Semi-Supervised Graph Based Method (SSG), Conditional Random Fields (CRF), k-Nearest Neighbor (KNN), and Bi-layer Graph-based Learning (BLGL), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). When compared with existing methods, the findings of the proposed method display excellent accuracy of 98.83%. Novelty: To start, an adaptive median filter is used to pre-process the satellite Remote sensing images, removing unwanted noises and other impacts. Following pre-processing, the image is segmented using the Bendlet Transform and Improved Chan-Vese algorithms. Gray-level co-occurrence matrix is utilized to extract texture information, and the Hybrid HRSVM-CNN is then used to categorize the various types of land. These applications frequently have a variety of issues that affect the categorization accuracy. A few significant factors, like location, irregularity, form, and diameter, reduce the process's overall accuracy. This research focuses on presenting a unique Land-Cover classification model in order to address such problems. The UC Merced Land Use dataset was considered in this research. Keywords: Bendlet Transform and Improved Chan­Vese Segmentation, Hybrid HRSVM­CNN, Land detection system, Satellite remote sensing images, Adaptive median filter, Gray­ level co­occurrence matrix

Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm

Agriculture plays a vital role in India's economy, and the health of crops is critical for maximizing yield. In particular, cucumber, a key salad ingredient known for its health benefits, is susceptible to various diseases such as water mold, bacterial wilt, angular leaf spot, anthracnose, and powdery mildew. These diseases not only affect the quality of cucumbers but also significantly reduce their yield. Early detection of these diseases is crucial for successful cultivation, but traditional manual methods of disease identification by farmers or diagnosticians are time-consuming and prone to misidentification. To address these challenges, we explore advanced artificial intelligence techniques. We implement and compare various machine learning algorithms, including ResNet, AlexNet, and VGG-16, for disease classification in cucumbers. However, these methods often struggle with issues such as noise, irrelevant features, and the generation of pertinent characteristics. To overcome these limitations, we propose a novel approach using a GLCM (Gray Level Co-occurrence Matrix) feature extraction method combined with a Random Forest classifier. This new algorithm aims to improve the accuracy and efficiency of disease detection. Our dataset comprises four distinct categories: Healthy, Anthracnose, Aphids, and CYSDV. It is sourced from diverse platforms, including online repositories like kaggle and direct collection from cucumber farms. The initial phase of our methodology involves noise reduction by converting images into the LAB color space and isolating specific regions using the k-means clustering algorithm. Subsequently, we extract texture features from the diseased leaf images using the GLCM algorithm, and classification is performed using the Random Forest model. Comparative analysis shows that our proposed Random Forest algorithm outperforms previous models like LGBM (Light Gradient Boosting Machine) and QSVM (Quantum-Support Vector Machine) in predicting disease presence in cucumber plants with higher accuracy rate of 98.62%, Precision 98.77%, Recall 98.48% and also F1 Score 98.62%.

Machine vision assisted fused deposition modelling process optimization using grey level co-occurrence matrix

Taguchi's Design of Experiments was applied to determine the effect of Fused Deposition Modeling (FDM) parameters such as layer thickness, infill density, and infill line thickness and deposition pattern on surface roughness. Nylon material was fused to produce samples. Conventional probe surface area measurements were performed to extract the Ra values of the samples. Subsequently, the non-contact measurement of 3D printed textures using the Gray Level Co-Occurrence Matrix (GLCM) algorithm was introduced through image processing. The FDM-printed surface image parameters such as correlation and homogeneity are Ra responsive and therefore a signal-to-noise ratio (S/N) analysis was performed to identify optimal parameters. A slight, unexplained deviation in surface image parameters was detected, and this inaccurate deviation may be due to image noise, image distortion, and different lighting associated with capturing and processing printed surface images. The result of the analysis shows that a good surface quality is achieved if a layer thickness of 0.2 mm, 100% bulk density and 1.0 mm bulk thickness are selected. When optimal parameters were reached, the FDM print was repeated and then the results were validated to ensure the accuracy of the method used. The results obtained in the present study could be proposed in the field of additive manufacturing, which improves the surface quality of the FDM sample.

Pavement damage identification and evaluation in UAV-captured images using gray level co-occurrence matrix and cloud model

In the road structure, the pavement is in direct contact with the wheel, and its daily maintenance is very important. Traditional pavement damage detection relies heavily on manual work, which is inefficient and unsafe. In this paper, the unmanned aerial vehicle (UAV) is used to pick up the pavement damage image, and then combine the gray level co-occurrence matrix (GLCM) algorithm and the cloud model theory to construct a damage identification and evaluation model. In the experimental stage, a road in Chongqing, China is selected to verify the model. The results show that the accuracy of the proposed theoretical model in pavement damage identification exceeds 82.5%, and the performance is good. In addition, in the evaluation case, the lightest damage is the crack, and the most serious is the rut. For the hollow and block repair, the texture feature cloud droplet membership ratio of the two is mostly between 0.5 and 0.6, indicating that the damage state of the pavement is evolving from slight to moderate.

Efficient LBP-GLCM texture analysis for asphalt pavement raveling detection using eXtreme Gradient Boost

The raveling of asphalt pavement is the primary cause of decreasing road safety, comfort, and service life. Because of the asphalt's complex texture, automatic raveling detection from image samples is a challenging operation. In this study, a computer vision technique, based on image texture features, for automatic detection of asphalt pavement raveling is proposed and verified. Two scenarios are taken into account for feature extraction. First, texture features from images are extracted using the traditional GLCM (Gray-Level Co-occurrence Matrix) algorithm. Second, the images are subjected to LBP (Local Binary Pattern) and then GLCM is employed to extract texture features. Utilizing the eXtreme Gradient Boost (XGBoost) technique, two models are built using the mentioned feature extraction scenarios and then compared. The results indicate that compared to the first scenarios prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %81), the second feature extraction scenario can offer higher prediction performance (Accuracy, Precision, Recall, and F1-Score are all approximately equal to %97). In order to demonstrate the model’s generalizability, a separate dataset is tested. Due to the acceptable performance values for this dataset (with more than %97 in terms of Accuracy, Precision, Recall, and F1-Score), the suggested model can be beneficial for transportation agencies to enhance the efficiency of road inspection activities.

POMEGRANATE DISEASE DETECTION AND PESTICIDES SUGGESTION

Abstract: Pomegranate is a valuable fruit crop that is widely cultivated in various regions. However, its production is often threatened by various diseases that can significantly affect its yield and quality. In this project, we propose a method for pomegranate disease detection and pesticides suggestion using the gray-level co-occurrence matrix (GLCM) algorithm. The proposed approach utilizes GLCM to extract texture features from the pomegranate leaf images and classify them into healthy or diseased categories. Moreover, we suggest the most effective pesticides to control the detected diseases by using the GLCM algorithm. The performance of the proposed method is evaluated using a dataset of pomegranate leaf images with different disease symptoms. The experimental results demonstrate the effectiveness of our approach in detecting and classifying pomegranate diseases with high accuracy. Furthermore, the suggested pesticides show promising results in controlling the diseases. Overall, the proposed method can assist farmers in identifying pomegranate diseases at an early stage and provide appropriate suggestions for pesticide selection, which can reduce crop losses and enhance crop productivity. Keywords: classification, disease detection, GLCM algorithm.

Identification of cashmere and wool based on LBP and GLCM texture feature selection

There are invalid and redundant features in the texture feature extraction method of cashmere and wool fibers, which leads to the low recognition accuracy. In this paper, a novel texture feature selection method based on local binary pattern, the gray level co-occurrence matrix algorithm and chi-square test was proposed to sufficiently extract the effective features of these two fibers. Firstly, the collected images of cashmere and wool fibers are processed to obtain the clear texture images with background removed by pre-processing algorithm and local binary pattern. Then, the texture features are calculated by the gray level co-occurrence matrix, and the optimal 5-dimensional features are extracted by chi-square test to represent the texture information of cashmere and wool. Finally, the two fibers are automatically classified and recognized based on the support vector machine. The experimental results show that the proposed method obtained a high recognition accuracy with the percent of 94.39. It verifies that the method based on texture feature selection is effective to identify cashmere and wool fibers.

Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM

In this study, fault diagnosis method of bearing utilizing gray level co-occurrence matrix (GLCM) and multi-beetles antennae search algorithm (MBASA)-based kernel extreme learning machine (KELM) is presented. In the proposed method, feature extraction of time–frequency image based on GLCM is proposed to extract the features of the bearing vibration signal, and multi-beetles antennae search algorithm-based KELM (MBASA-KELM) is presented to recognize the states of bearing. KELM employs the kernel-based framework, which has better generalization than traditional extreme learning machine, and it is necessary to look for an excellent optimization algorithm to select appropriate regularization parameter and kernel parameter of the KELM model because these parameters of the KELM model can affect its performance. As traditional beetle antennae search algorithm only employs one beetle, which is difficult to find the optimal parameters when the ranges of the parameters to be optimized are wide, multi-beetles antennae search algorithm (MBASA) employing multi-beetles is presented to select the regularization parameter and kernel parameter of KELM. The experimental results demonstrate that MBASA-KELM has stronger fault diagnosis ability for bearing than LSSVM, and KNN.

Detection and visualization of soybean protein powder in ground beef using visible and near-infrared hyperspectral imaging

The undeclared addition of soybean proteins in meats is a serious problem due to health and economic reasons. This paper proposes a methodology for quantitative determination and rapid visualization of soybean protein powder (SPP) addition in ground beef using hyperspectral imaging (HSI). Hyperspectral images of a total of 256 samples with different SPP contents were acquired by HSI system in spectral range of 400–1000 nm. Regions of interest (ROIs) were identified to withstand interference of background and containers to extract spectra. Principal component analysis (PCA) of extracted spectra found that PC1 and PC2 were effective for grouping samples into different levels. Gray level co-occurrence matrix (GLCM) algorithm was thus applied on the corresponding first two PC score images to extract eight texture features. Partial least squares regression (PLSR) was used to establish quantitative models, yielding the optimal correlation coefficient in prediction (Rp) of 0.9979 and residual predictive deviation (RPD) of 14.48 using standard normal variate (SNV) followed by detrending preprocessed spectra. Deming and Passing-Bablok regression were also conducted, and HSI results revealed high model performance which can be interchanged with laboratory standard. Key wavelengths were selected using PC loadings, two-dimensional correlation spectroscopy (2D-COS) and regression coefficients (RC), respectively. After that, PLSR models were evaluated with various inputs, and results showed spectra played predominate role rather than texture. Consequently, simplified PLSR model based on six selected wavelengths from PC loadings was retained with Rp = 0.9933, RPD = 8.45 and limit of detection (LOD) of 0.74%. Colorful spatial distribution maps were successfully created to clearly observe SPP adulteration levels. Current study indicates that HSI has high potential for determining and visualizing SPP adulteration in ground beef.

Surface Wettability Prediction Using Image Analysis and an Artificial Neural Network.

In this study, a wettability-predicting method that uses an artificial neural network (ANN) by learning from digital images of the actual surface structures was developed. Polyester film surfaces were treated with oxygen plasma to realize various nanostructured surfaces. Surface structural characteristics from SEM images were quantified in a multifaceted way using a box-counting algorithm, a gray-level co-occurrence matrix algorithm, and binary image analysis. An ANN model that can predict wettability from surface structures was developed using the quantified surface structure and the resulting wettability as learning data. Furthermore, a surface with an optimal nanostructure to achieve superhydrophobicity was suggested by considering extracted surface structural parameters that significantly affect the surface wettability.

Classification of Coffee Bean Defects Using Gray-Level Co-Occurrence Matrix and K-Nearest Neighbor

Defects in coffee beans can significantly affect the quality of coffee production so that defects in coffee beans can cause a decreasing the level of coffee production. The purpose of this study is to implement the GLCM (gray-level co-occurrence matrix) and the K-NN (k-nearest neighbor) method on a web-based program and provided a website to detect coffee bean defects. This study uses the GLCM algorithm to extract the features of the coffee images and uses the K-NN algorithm to classify the defect level of coffee beans. The system development was built using Unified Modeling Language. The development of this website was utilized the programming structure of PHP, HTML, CSS, Javascript, Mozilla Firefox as a browser for the website and MySql for the database management systems. The results show that the system can provide the output in the form of a classification level of the defect level of the coffee bean images. Then, the accuracy of the coffee bean defect assessment was achieved by 90%. Finally, this study concluded that the proposed system could help the coffee farmers determine the defect level of the coffee beans using images input.

Adaptive Extraction of Oil Painting Texture Features Based on Reaction Diffusion Equation

The oil painting retrieval technology based on the reaction diffusion equation has attracted widespread attention in the fields of oil painting processing and pattern recognition. The description and extraction of oil painting information and the classification method of oil paintings are two important processes in content-based oil painting retrieval. Inspired by the restoration and decomposition functional model of equal oil painting, we propose a reaction diffusion equation model. The new model contains two reaction diffusion equations with different principal parts. One principal part is total variation diffusion, which is used to remove noise. The other main part is thermal diffusion, which is used to modify the source term of the denoising reaction-diffusion equation to achieve the effect of protecting the texture of the oil painting. The interaction of the two reaction-diffusion equations finally achieves denoising while maintaining the boundaries and textures. Under the framework of the above reaction diffusion equation model, we introduce Laplace flow to replace the original total variation flow, so that the new denoising reaction diffusion equation combines the isotropic diffusion and total variation flow of the thermal reaction diffusion equation to achieve the effect of adaptive theoretical research. Using regularization methods and methods, we, respectively, get the well-posedness of the two model solutions, which provides the necessary preparation for numerical calculations. Based on the statistical theory and classification principles of support vector machines, combined with the characteristics of oil painting classification, the research and analysis are carried out from the three important aspects of kernel function, training algorithm, and multiclass classifier algorithm that affect the classification effect and speed. Numerical experiments show that the given filter model has a better processing effect on images with different types and different degrees of noise pollution. On this basis, an oil painting classification system based on texture features is designed, combined with an improved gray-level cooccurrence matrix algorithm and a multiclass support vector machine classification model, to extract, train, and classify oil paintings. Experiments with three types of oil paintings prove that the system can achieve a good oil painting classification effect. Different from the original model, the new model is based on the framework of reaction-diffusion equations. In addition, the new model has good effects in removing step effects, maintaining boundaries and denoising, especially in maintaining texture.

Improved window adaptive gray level co-occurrence matrix for extraction and analysis of texture characteristics of pulmonary nodules

Background and ObjectiveIdentifying benign and malignant pulmonary nodules is essential for the early diagnosis of lung cancer and targeted surgical resection. This study aimed to differentiate benign from malignant pulmonary nodules based on computed tomography (CT) plain scan texture analysis technique. MethodsA total of 47 pulmonary nodules use the improved window adaptive gray level co-occurrence matrix (GLCM) algorithm to extract the texture characteristics of the area of interest. The Fisher coefficient (Fisher), classification error probability joint average correlation coefficient (POE+ACC), mutual information (MI), and the combination of above three methods joint (FPM) were used to select the best texture parameters set. After that, the analysis of the screened texture parameters was adopted. The B11 module provides four analytical methods, including raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis (LDA), and nonlinear discriminant analysis (NDA). The results were expressed in the form of misclassification rate (MCR). Region of curve (ROC) analysis was also performed on the selected optimal texture parameters. ResultsThe MCR of all the three texture feature extraction methods, Fisher, POE+ACC, and MI, were lower in differentiating benign from malignant pulmonary nodules. FPM method could further reduce the MCR. The NDA analysis had the lowest MCR for both of these three feature extraction methods. The MCR can be further reduced to 2.13% by the combination of NDA and FPM. The ROC curve showed that Perc.01% parameter had the highest AUC value and the most discriminative efficacy. ConclusionThe lowest MCR values were calculated by the FPM dimensionality reduction method and the NDA analysis method. The improved GLCM algorithm has a discriminative role in CT texture analysis of benign and malignant pulmonary nodules.

Classification of COVID-19 using feature extraction GLCM and SVM algorithm

Coronavirus Disease (COVID-19) is a new variant of the corona virus that mutates and spreads rapidly between humans. The high rate of transmission and spread is not matched by the fast process of diagnosis because it has to go through a polymerase chain reaction (PCR) test in the laboratory.To identify quickly, efficiently and effectively, then a classification system is used using x-ray images of the chest using the Gray Level Co-occurrence Matrix (GLCM) method and the Support Vector Machine (SVM) algorithm. The data used are digital images of normal chest X-Ray (CXR) diagnosis and positive diagnosis of Covid-19 with 408 training data and 128 test data. The test is carried out on the GLCM parameters, namely with a distance of d = 1,2,3 and angles , , , and feature extraction with contrast, correlation, energy, homogeneity, and dissimilarity.The test results show that the highest accuracy is at the distance d = 1 and the angle ?= is 90.47% and the lowest accuracy is at the distance d = 3 and the angle ? = is 80.35%.

Toward digital agricultural mapping in Africa: evidence of Northern Nigeria

Optical image provides a unique spectral and textural opportunity to acquire data free of charge in cloud-free weather conditions. However, textural information from recent Sentinel-2A data has not been sufficiently recognized. Several researches have shown that texture features are essential for extracting crop information. This study presents a new scheme that integrates spectral bands and textural features for mapping maize fields and other land cover types (i.e., build-up, trees, others, and water) and evaluates the effectiveness of random forest (RF) and support vector machine (SVM) classifiers with different kernel functions based on Sentinel 2A (S-2A) data. The textural features were derived from multiple bands of S-2A image using a grey-level co-occurrence matrix (GLCM) algorithm. To normalize the analysis of these features, Random Forest feature selection was employed. The outcomes show that S-2A images are appropriate for mapping maize fields and other land covers when the image is accessible during the growing season. The SVM classifier with a radial basis function (RBF) kernel is superior to a polynomial (P), linear (L), and sigmoid (S) kernel functions, and the RF classifiers. For the SVM_RBF, the highest overall accuracy was obtained from the integration of spectral bands and textural features at scenario 2 SVM_RBF (87.40%), followed by the combination of spectral and textural features at scenario 2 SVM_P kernel (86.56%). However, scenario 5 SVM_P kernel (85.54%), scenario 4 SVM_P kernel function (85.30%), the spectral features alone, and the SVM_P kernel function, have the highest O/A (83.90%) than that of the SVM_RBF (83.66%). The inclusion of textural features contributed significantly in mapping the smallholder’s maize field in the study area. This demonstrated the value of S-2A in agricultural research.

Vision based prediction of surface roughness for end milling

Abstract Measurement of surface roughness helps to assess the machined component's functionality. In the past three decades, several scientists have contributed to the computation of surface roughness. This research article deals with two distinct methods for prediction of surface roughness employing the surface profilometer and machine vision for AISI 1040 steel specimens prepared by varying cutting parameters of end milling viz. feed rates, speed and cutting depth. Using a surface profilometer, the surface roughness parameters are evaluated. At the other hand, the texture features were extracted using a Gray Level Co-occurrence Matrix Algorithm (GLCM) and a computer vision system. Correlations are formed among characteristics of machined surface and the texture feature such as contrast, entropy, energy, and homogeneity. The comparable findings revealed a maximum relative error of −8% using contrast and energy, − 11% using entropy and −10% using homogeneity.

Image Classification of Chicken Embryo Based on Matched Filter and Skeleton Curvature Feature

To solve the problem of nondestructive detection of egg formation activity in vaccine preparation, a new method based on matching filter for chicken embryo image blood vessels segmentation algorithm combined with the curvature feature of the blood vessels skeleton was proposed. Gaussian matching filter was used to enhance the contrast between the blood vessels and the background of the green channel image and the blood vessels were segmented from the background. Morphological methods was used to process the binary images to extract the vascular skeleton feature. The curvature of the vascular skeleton was calculated by using the least square method. Finally, the activity of chicken embryo was judged by combining the shape feature of blood vessel with the texture feature of chicken embryo extracted by gray level co-occurrence matrix algorithm. The experimental results show that this method has high accuracy in the classification experiment of chicken embryo images affected by spot noise, and meets the quality requirements of producing virus species in vaccine preparation.

Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography.

Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.