Machine vision system for classification of bulk raisins using texture features

Khojastehnazhand, Mostafa; Ramezani, H.

doi:10.1016/j.jfoodeng.2019.109864

Cited by 30 publications

(20 citation statements)

References 17 publications

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“…In this study, 11 GLRLM based texture features including Short Run Emphasis (SRE), Long Run Emphasis (LRE), Gray-Level Non-Uniformity (GLN), Run Length Non-Uniformity (RLN), Run Percentage (RP), Low Gray-Level Run Emphasis (LGRE), High Gray-Level Run Emphasis (HGRE), Short Run Low Gray-Level Emphasis (SRLGE), Short Run High Gray-Level Emphasis (SRHGE), Long Run Low Gray-Level Emphasis (LRLGE), and Long Run High Gray-Level Emphasis (LRHGE) were extracted from images. These features and their formulas have been previously described in several research articles [64][65][66] and the related MATLAB codes are revealed by Wei [67].…”

Section: F Gray Level Run Length Matrix (Glrlm) Texture Featuresmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

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Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M1 and LogitBoost algorithms was evaluated to enhance the plant classification performance of four classifiers, namely Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Random Forest (RF), and Support Vector Machine (SVM). Four feature filtering techniques including Correlationbased Feature Selection (CFS), Information Gain (IG), Gain Ratio (GR), and OneR were applied to the imageextracted features and 10 of the most significant features were selected and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M1 and LogitBoost algorithms improved the classification accuracy. Regarding the performance values, the LogitBoost-IG-RF structure, which provided a classification accuracy of 99.58%, a kappa (k) of 0.9948, and a Root Mean Squared Error (RMSE) of 0.0688 on training dataset, was selected as the most appropriate classifier for plant discrimination in peanut fields. The accuracy, k, and RMSE criteria of this combination on test dataset were 95.00%, 0.9375, and 0.1591, respectively. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

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Section: F Gray Level Run Length Matrix (Glrlm) Texture Featuresmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

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“…In previous studies, when using GLRLM texture features for classification tasks, there is good performance [ 41 , 42 ]. Khojastehnazhand et al [ 43 ] used GLRLM and GLCM features to classify raisins and found that the classification effect of GLRLM was better than GLCM features. In this article, the reason for the poor performance of GLRLM maybe because there are too many redundant features between the features, it will adversely affect the classification and result in low classification accuracy.…”

Section: Resultsmentioning

confidence: 99%

Cotton stubble detection based on wavelet decomposition and texture features

et al. 2021

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Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

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“…Different texture feature algorithms and different modeling methods were used to evaluate the performance of the system for the classification of good and bad raisins in different mixtures (Khojastehnazhand & Ramezani, 2019). The research results…”

Section: Discussionmentioning

confidence: 99%

Extraction and classification of apple defects under uneven illumination based on machine vision

Bao

Yin

Liu

et al. 2022

J Food Process Engineering

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The extraction and classification of different kinds of defects can achieve a more sophisticated classification of apples. In this study, a region of interest (ROI) extraction algorithm based on background separation, brightness correction, and the global threshold was designed for the extraction of apple decay and bruise under uneven light conditions. After brightness correction, we calculate the average gray value of the apple region and multiply the value by 0.8 as the threshold. In this way, the threshold of different apple images can be determined automatically without a manual setting. After extracting ROI, we realized the classification of the two kinds of defects by extracting texture features. Based on the selected texture features, the support vector machine discriminant models were established. The accuracy of the model based on angular second moment was 94.8% and the model based on entropy was 94.7%. The total time from ROI extraction to classification was approximately 1.2 s. The results showed that this extraction and classification process had application prospects in practical detection classification. Practical ApplicationsUngraded fruits were not easy to store, transport, or sell at a price based on quality.The classification was directly related to fruit packaging, transportation, storage and sales of the results, and benefits, so it was very important to carry out fruit classification. Machine vision technology can achieve objective and nondestructive rapid detection. Many studies had achieved extraction of defective parts. However, they directly classified apples with defects into one category without further classification of types of the defects. In many classification standards, such as GBT10651-2008, they need to classify apples more finely according to the defect types. This study achieved extraction and classification of different kinds of apple defects by machine vision technology and achieved a satisfactory accuracy and detection speed, which provided theoretical support for the realization of strict and fine classification of apple in industrial applications in the future. | INTRODUCTIONApples played an important role in healthy diet and were one of the most grown and consumed fruits in the world (Song, Jiang, Wang, & Guo, 2020). Apples were susceptible to a variety of surface defects, which reduced the quality of the fruit, affected consumers' purchasing decisions, and harmed the profit margins of the fruit industry (Lu & Lu, 2017). It was very necessary to apply nondestructive testing

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Machine vision system for classification of bulk raisins using texture features

Cited by 30 publications

References 17 publications

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Cotton stubble detection based on wavelet decomposition and texture features

Extraction and classification of apple defects under uneven illumination based on machine vision

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