Snake Fruit Classification by Using Histogram of Oriented Gradient Feature and Extreme Learning Machine

Rismiyati,; Wibawa, Helmie Arif

doi:10.1109/icicos48119.2019.8982528

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Each pixel in a block has its gradient magnitude separated into multiple orientation bins based on the gradient angle. The orientation binning process is illustrated in Figure 4 [16]. In block normalization, adjacent blocks are grouped.…”

Section: Histogram Of Oriented Gradients (Hog)mentioning

confidence: 99%

Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization

Wikky Fawwaz Al Maki,

Khaidir Mauladan,

Indra Bayu Muktyas

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Various cultivars of date fruits distributed throughout exhibit diverse complexity and unique attributes, including color, flavor, shape, and texture. These distinctive characteristics and appearance occasionally lack variability in date fruits, since various kinds of date fruit may have subtle differences in color, shape, and texture. To overcome the difficulty of sorting and classifying multiple types of date fruit, a classification model was developed to categorize date fruit according to their visual appearances and digital characteristics. This study proposes a classification system that categorizes date fruit into five distinct types. The system achieves this by extracting features related to date fruit images' color, shape, and texture. Specifically, color moments, HOG descriptors, and circularity are used for feature extraction. The resulting high-quality training data is then used to train a K-Nearest-Neighbor (KNN) classifier. Considering the parameters applied to develop the proposed classification model is essential. Therefore, the proposed KNN model will be optimized by Principal Component Analysis (PCA) and Binary Particle Swarm Optimization (BPSO). PCA is employed for dimensionality reduction, whereas BPSO is implemented to discover the optimal neighbors. The experimental results demonstrated that the classification model achieved an accuracy of 93.85%, a considerable improvement of 12% over barebone KNN.

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Section: Histogram Of Oriented Gradients (Hog)mentioning

confidence: 99%

Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization

Wikky Fawwaz Al Maki,

Khaidir Mauladan,

Indra Bayu Muktyas

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Classifier yang digunakan adalah ELM dan SVM. Akurasi yang didapatkan adalah 95% dan 97,3% untuk ELM dan SVM [6]. Sementara itu, metode deep learning untuk membedakan kualitas salak dilakukan dengan menggunakan CNN.…”

Section: Pendahuluanunclassified

“…Data set yang digunakan pada penelitian diambil dari data set penelitian sebelumnya tentang klasifikasi salak [6], [7]. Data set ini sejumlah 370 citra yang terdiri dari 190 citra dari kelas bagus, dan 180 citra dari kelas jelek.…”

Section: Data Setunclassified

VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Rismiyati

Luthfiarta

2021

Telematika

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Purpose: This study aims to differentiate the quality of salak fruit with machine learning. Salak is classified into two classes, good and bad class.Design/methodology/approach: The algorithm used in this research is transfer learning with the VGG16 architecture. Data set used in this research consist of 370 images of salak, 190 from good class and 180 from bad class. The image is preprocessed by resizing and normalizing pixel value in the image. Preprocessed images is split into 80% training data and 20% testing data. Training data is trained by using pretrained VGG16 model. The parameters that are changed during the training are epoch, momentum, and learning rate. The resulting model is then used for testing. The accuracy, precision and recall is monitored to determine the best model to classify the images.Findings/result: The highest accuracy obtained from this study is 95.83%. This accuracy is obtained by using a learning rate = 0.0001 and momentum 0.9. The precision and recall for this model is 97.2 and 94.6.Originality/value/state of the art: The use of transfer learning to classify salak which never been used before.

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“…Snakefruit itself is local fruit from Indonesia. Other than by using transfer learning, several other researches perform on snakefruit quality classification were performed by using ELM, SVM and CNN [12], [13]. The highest accuracy so far is achieved by VGG16 architecture with 95% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning with Xception Architecture for Snakefruit Quality Classification

Rismiyati

Luthfiarta

2022

JAIS

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Machine learning has been greatly used in the field of image classification. Several machine learning techniques perform very well in this task. The development of machine learning technique in recent years are in the direction of deep learning. One of the main challenge of deep learning is that it requires the number of the samples to be extremely large for the model to perform well. This is because the number of feature that trainable parameter are huge. One of the solution to overcome this is by introducing transfer learning. One of the architecture that is currently introduced is Xception architecture. This architecture is claimed to outperform VGG16, ResNet50, and inception in terms of model accuracy and model size. This research aims to classify snakefruit quality by using transfer learning with Xception architecture. This is to explore possibility to achieve better result as Xception architecture generally perform better than other available architecture in transfer learning. The snakefruit quality is classified into two classes. Hyperparameter value is optimized by several scenario to determine the best model. The best performance is achieved by using learning rate of 0.0005, momentum 0.9 and dropout value of 0 or 0.25. The accuracy achieved is 94.44%.

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Snake Fruit Classification by Using Histogram of Oriented Gradient Feature and Extreme Learning Machine

Cited by 4 publications

References 10 publications

Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization

Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization

VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification

Transfer Learning with Xception Architecture for Snakefruit Quality Classification

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