Pattern Recognition of Sarong Fabric Using Machine Learning Approach Based on Computer Vision for Cultural Preservation

doi:10.22266/ijies2022.1031.26

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…On the other hand, recall is the accurately predicted class out of all actual classes, and precision is shown as a separate class that is correctly anticipated in all class predictions. These parameters were calculated using ( 1), (2), and (3) [16], [25].…”

Section: Model Evaluationmentioning

confidence: 99%

Classification of tea leaf disease using convolutional neural network approach

Hairah,

Septiarini,

Puspitasari

et al. 2024

IJECE

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Leaf diseases on tea plants affect the quality of tea. This issue must be overcome since preparing tea drinks requires high-quality tea leaves. Various automatic models for identifying disease in tea leaves have been developed; however, their performance is typically low since the extracted features are not selective enough. This work presents a classification model for tea leaf disease that distinguishes six leaf classes: algal spot, brown, blight, grey blight, helopeltis, red spot, and healthy. Deep learning using a convolutional neural network (CNN) builds an effective model for detecting tea leaf illness. The Kaggle public dataset contains 5,980 tea leaf images on a white background. Pre-processing was performed to reduce computing time, which involved shrinking and normalizing the image prior to augmentation. Augmentation techniques included rotation, shear, flip horizontal, and flip vertical. The CNN model was used to classify tea leaf disease using the MobileNetV2 backbone, Adam optimizer, and rectified linear unit (ReLU) activation function with 224×224 input data. The proposed model attained the highest performance, as evidenced by the accuracy value 0.9455.

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Section: Model Evaluationmentioning

confidence: 99%

Classification of tea leaf disease using convolutional neural network approach

Hairah,

Septiarini,

Puspitasari

et al. 2024

IJECE

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“…GLCM operates using a kernel window, as mentioned by Haralick [28]. The kernel window in GLCM analysis compares the pixel values within a specified neighborhood to calculate the occurrence and distribution of different pixel value pairs [29]. With this kernel, GLCM captures the patterns of correlation between pixels in the inspected image and is commonly employed to identify texture features in images [30].…”

Section: Related Workmentioning

confidence: 99%

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

2024

IJIES

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Quality inspection of solder connections in electronic circuit manufacturing is commonly performed using automatic optical inspection (AOI) technology. The utilization of deep learning in AOI has demonstrated high accuracy and fast computation, yet it requires expensive graphics processing unit (GPU) equipped computers. To enable broader and more cost-effective utilization, there is a need for an AOI model that can be embedded in simple central processing unit (CPU) based computers. To pursue this objective, several research efforts have been undertaken to develop AOI models based on classical machine learning techniques. However, the accuracy and speed achieved by these models have not yet matched deep learning-based AOI models. This study aims to enhance the computational efficiency of classic machine learning by processing only pixels containing textural information. The effectiveness enhancement is achieved through the application of log-polar transformation in the extraction of texture features using the gray level co-occurrence matrix (GLCM) to detect defects in trough hole technology (THT) solder joint connections. By transforming cartesian coordinates into polar coordinates, the textural areas to be detected assume a square shape, facilitating efficient texture feature extraction. To ensure a significant improvement in performance, a comparative performance evaluation is conducted on classic machine learning-based AOI models with and without the log-polar transformation. The texture features extracted from both models are classified using the support vector machine (SVM) method. Model testing and evaluation reveals that the proposed enhancement effort is capable of increasing accuracy levels to 95% and reach computation time by 120 milliseconds.

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“…The color features were successfully extracted using color moments [22]. Most color distribution information was included in the low-order moments.…”

Section: Color Momentsmentioning

confidence: 99%

Betel leaf classification using color-texture features and machine learning approach

et al. 2023

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The existence of machine learning has been exploited to solve difficulties in various fields, including the classification of leaf species in agriculture. Betel leaf is one of the plants that provide health advantages. The objective of using a machine learning approach is to classify the betel leaf species. This study involved several processes: image acquisition, region of interest (ROI) detection, pre-processing, feature extraction, and classification. The feature extraction used the combination features of color and texture. Furthermore, the classification applied four classifiers, including artificial neural network (ANN), K-nearest neighbors (KNN), Naive Bayes, and support vector machine (SVM). The evaluation in this study implemented cross-validation with a K-fold value of 5. The method performance produced the highest accuracy value of 100% using the color and texture features with the SVM classifier.

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Pattern Recognition of Sarong Fabric Using Machine Learning Approach Based on Computer Vision for Cultural Preservation

Cited by 11 publications

References 25 publications

Classification of tea leaf disease using convolutional neural network approach

Classification of tea leaf disease using convolutional neural network approach

Enhancing GLCM-Based Feature Extraction for THT Solder Joint Inspection through Log Polar Transformation

Betel leaf classification using color-texture features and machine learning approach

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