Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network

Golpour, Iman; Parian, Jafar Amiri; Chayjan, Reza Amiri

doi:10.17221/238/2013-cjfs

Cited by 52 publications

(32 citation statements)

References 15 publications

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“…Classifications of different varieties of seed were also carried out during the past few years using different classification models. Various classification models based on bulk grain samples were reported in [1,2,12,18,25,37]. It is observed that the classification tasks involved in the above literature were not challenging.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Iman Golpour et.al and S.Majumdar et.al. suggested STEPDISC analysis method for feature selection [18], [19]. Anami et.al projected feature extraction methods for identification and classification of food grains, fruits and flowers [20].…”

Section: Introductionmentioning

confidence: 99%

Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features

Pushpalatha¹,

Karegowda²,

Ramesh³

2017

IJCA

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Content based image retrieval (CBIR) is an automated way to retrieve images based on the visual content or image features itself. Visual inspection of food type is tiresome and time consuming task. This paper presents the retrieval of similar looking bulk split gram images using Grey Level Cooccurrence Matrix (GLCM) and Color Grey Level Cooccurrence Matrix (CGLCM) texture features. Texture feature matching procedure is based on three distance measures namely, Euclidean distance, Canberra distance and City block distance. The performance of a retrieved image is measured in terms of Precision. Experimental results show that the CGLCM provides better retrieving result than GLCM.

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“…Then, by means of principal component analysis, the structure of the wavelet textural features was determined and finally classification ratio of 96.82% for different types of Chinese famous tea using multi-class least square support vector machine could be found. Golpour et al [14] tried to identify five rice cultivars by means of developing an image processing algorithm. Thirteen color features were applied and a 96.66% classification accuracy using an optimized neural network with two hidden layers was achieved.…”

Section: Introductionmentioning

confidence: 99%

Identifying Potato Varieties Using Machine Vision and Artificial Neural Networks

Azizi

Abbaspour‐Gilandeh

Nooshyar

et al. 2015

International Journal of Food Properties

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The objective of this study is to develop a method for identifying and discriminating ten potato varieties by combining machine vision and artificial neural network methods. The potato varieties include Agria, Emrad. A total number of 72 characteristic parameters specifying color, textural and morphological features are found among these varieties. By using principal component analysis (PCA), 16 principal features are selected for identifying and discriminating potato varieties. The data obtained from image processing were classified using linear discriminant analysis and nonlinear artificial neural network method. The accuracy of discriminant analysis were 73.3%, 93.3%, 73.3%, 40%, 73.3%, 73.3%, 66.7%, 80%, 40% and 53.3%, respectively for the varieties used in this study. The classification accuracy was improved by 100% for all the varieties using neural network analysis and the Correct Classification Ratio (CCR) was 100% using this method.A c c e p t e d M a n u s c r i p t 2 It is revealed from the results that machine vision technique and neural network analysis could identify potato varieties with acceptable accuracy.

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Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network

Cited by 52 publications

References 15 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

Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features

Identifying Potato Varieties Using Machine Vision and Artificial Neural Networks

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