Suitability of Feature Extraction Methods in Recognition and Classification of Grains, Fruits and Flowers

Anami, Basavaraj S.; Savakar, Dayanand G.

doi:10.2202/1556-3758.1776

Cited by 8 publications

(4 citation statements)

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“…Homogeneity measures the closeness of the distribution of elements in the GLCM to GLCM diagonal. A total of 27 texture features were extracted from three colour planes (R, G and B) and are presented in Table 2 [2,4,13,34].…”

Section: Colour and Texture Featuresmentioning

confidence: 99%

A cascade network for the classification of rice grain based on single rice kernel

Singh

Chaudhury

2020

Complex Intell. Syst.

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This paper describes the classification of four different varieties of rice grain based on four sets of features, namely morphology, colour, texture and wavelet. The classification is carried out on single rice kernel using image pre-processing steps followed by a cascade network classifier. The performance of the classifiers based on the above feature sets is also compared. It is found that morphological feature is more suitable for the classification of rice kernels, as compared to other features. The number of input features is reduced by a feature selection process using statistical analysis system (SAS) software. The classification accuracy based on selected features is compared with that of original features using different classifiers. It is found that the selected features are able to provide classification accuracy very close to the original features. The performance of the proposed cascade classifier is also tested against standard datasets from the University of California, Irvine (UCI), and the results are compared with other classifiers. The results show that the proposed classifier provides better classification accuracy as compared to other classifiers.

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Section: Colour and Texture Featuresmentioning

confidence: 99%

A cascade network for the classification of rice grain based on single rice kernel

Singh

Chaudhury

2020

Complex Intell. Syst.

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“…Works had also been done to incorporate textural features for classification purposes. Efforts have also been made to integrate all these features in terms of a single classification vector for grain kernel identification [4] [11] [12]. Most of the published research mainly focuses on identification and classification of grain kernels by placing grain kernels in a non-touching fashion.…”

Section: Introductionmentioning

confidence: 99%

Classification of Rice Using Convolutional Neural Network (Cnn)

Pradeep M,

Dr. M Siddappa

2023

IJETMS

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This paper describes the technique for automatic recognition and classification of different rice grain samples using neural network classifier. The Red Green Blue (RGB), Hue Saturation Intensity (HSI) and Hue Saturation Value (HSV) color models of the image were considered for extracting 18 color features. The classification was carried out using color and texture features separately. The color image was converted to Gray scale image and the Gray Level Co-occurrence Matrixes (GLCM) for four different directions was calculated. A total of eight texture features were calculated from the Co-occurrence matrices. Convolutional Neural Network (CNN) is used for the classification process. The classification accuracy with color features and texture features were compared. Result shows that the classification base on texture features outperform the color feature-based classification even with lesser number of features. It is found that Convolutional Neural network was able to classify two varieties of rice with 100% accuracy using texture features and the edge detection with Sobel and Canny edge detection of the fiber features in the food grain.

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“…Applications of GLCM and GLRLM towards texture feature classification of different types of cereal grains based on single kernel image are presented in [13, 18, 19, 23, 24, 29, 37]. Classification of bulk images of cereal grains using texture features based on GLCM and GLRLM is presented in [31–34, 38, 39]. Rice grain classification based on bulk images using GLCM texture is presented in [36].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of texture feature extraction techniques for rice grain classification

Singh

Chaudhury

2020

IET image process

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Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET‐A, SET‐B and SET‐C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co‐occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K‐nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET‐B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.

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Suitability of Feature Extraction Methods in Recognition and Classification of Grains, Fruits and Flowers

Cited by 8 publications

References 0 publications

A cascade network for the classification of rice grain based on single rice kernel

A cascade network for the classification of rice grain based on single rice kernel

Classification of Rice Using Convolutional Neural Network (Cnn)

Comparative analysis of texture feature extraction techniques for rice grain classification

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