Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features

Septiarini, Anindita; Sunyoto, Andi; Hamdani, Hamdani; Kasim, Anita Ahmad; Utaminingrum, Fitri; Hatta, Heliza Rahmania

doi:10.1016/j.scienta.2021.110245

Cited by 70 publications

(34 citation statements)

References 36 publications

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“…On the other hand, previous studies have used various color spaces, such as HSV, YIQ, and YCbCr. While utilizing these color spaces, a conversion process based on the values in the RGB color space was required, which is defined in Equations ( 1) -( 7) [15]- [17]. The sample image was resized and converted to RGB to HSV, YIQ, and YCbCr color space is shown in Fig.…”

Section: Pre-processingmentioning

confidence: 99%

“…Therefore, the color feature is applied using a color moment which consists of analyzing the most suitable color space as a descriptor of the value of mean (µ), the standard deviation (σ), median (m), the minimum (min), and maximum (max). Color moments are used because they have been successfully applied in previous studies regarding classification cases [17], [18]. These features are extracted on each channel in four color spaces, including RGB, HSV, YIQ, and YCbCr, to select the optimal color space to use as a description.…”

Section: Feature Extractionmentioning

confidence: 99%

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Analysis of color features performance using support vector machine with multi-kernel for batik classification

Winarno

Hadikurniawati

Septiarini

et al. 2022

Int. J. Adv. Intell. Informatics

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Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.

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Section: Pre-processingmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Winarno

Hadikurniawati

Septiarini

et al. 2022

Int. J. Adv. Intell. Informatics

Self Cite

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show abstract

“…In the final stage, classification was used to determine the type of object that was being considered. There were several commonly classification methods employed: decision tree [1], SVM [2], [6], artificial neural networks [23], [24], and KNN [25], [26].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 4 depicts the image created as a result of each step in this process. The resulting image of each step on pre-processing: (a) ROI image, (b) HSV image, (c) grayscale image of channel H, (d) grayscale images of channel S, and (e) grayscale images of channel VThe conversion of RGB to HSV color space is defined in equation (1) -(3)[24]:…”

mentioning

confidence: 99%

Chili Classification Using Shape and Color Features Based on Image Processing

Sihombing

Septiarini

Kridalaksana

et al. 2022

SJI

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Abstract. Purpose: Chili is an agricultural product that has several varieties and is in great demand. It can be consumed directly or processed first. This study aims to classify the types of chili using color and shape features. The types of chili are divided into five classes: cayenne pepper, green chili, big green chili, big red chili, and curly chili. The chili classification method was evaluated using three parameters: precision, recall, and accuracy.Methods: This study applied the K-Nearest Neighbors (KNN) method with the Euclidean and Manhattan distance calculation algorithm and used two feature types: color and shape. The color features were extracted based on RGB color space by obtaining the mean and standard deviation values. Meanwhile, the shape features used aspect ratio, area, and boundary.Result: The evaluation results of the classification method were able to achieve the precision, recall, and accuracy values of 1.0, which means that all test data were classified correctly. The evaluation was applied with 210 training images and 90 test images based on the test results.Novelty: This study extracted two types of features: color and shape. Those features fed the KNN method by applying the Euclidean and Manhattan distance calculation algorithm; hence, the optimal results were achieved.

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“…Some researchers use the GLCM classification method for describing texture details into spatial domain and edge images [9], [11]. Moreover, GLCM is also used to detect rock images in the road type classification based on visual data, the GLCM method is also used to characterize way surfaces by several aspects such as texture [12], color [13], and border features of riders' sight image to coach a neural network of objects. In addition to GLCM, there is also the use of several classification methods such as convolutional neural networks [14] and support vector machine [15] to detect, but, the accuracy was low [16] and the sample size is too small with an accuracy below 60% [8].…”

Section: Introductionmentioning

confidence: 99%

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

Marianingsih¹,

Widodo²,

Pieter³

et al. 2022

JMEST

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The mechanized ability to specify the way surface type is a piece of key enlightenment for autonomous transportation machine navigation like wheelchairs and smart cars. In the present work, the extracted features from the object are getting based on structure and surface evidence using Gray Level Co-occurrence Matrix (GLCM). Furthermore, K-Nearest Neighbor (K-NN) Classifier was built to classify the road surface image into three classes, asphalt, gravel, and pavement. A comparison of KNN and Naïve Bayes (NB) was used in present study. We have constructed a road image dataset of 450 samples from real-world road images in the asphalt, gravel, and pavement. Experiment result that the classification accuracy using the K-NN classifier is 78%, which is better as compared to Naïve Bayes classifier which has a classification accuracy of 72%. The paving class has the smallest accuracy in both classifier methods. The two classifiers have nearly the same computing time, 3.459 seconds for the KNN Classifier and 3.464 seconds for the Naive Bayes Classifier.

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Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features

Cited by 70 publications

References 36 publications

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Chili Classification Using Shape and Color Features Based on Image Processing

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

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