Qualitative classification of milled rice grains using computer vision and metaheuristic techniques

Zareiforoush, Hemad; Minaei, Saeid; Alizadeh, Mohammad; Banakar, Ahmad

doi:10.1007/s13197-015-1947-4

Cited by 62 publications

(43 citation statements)

References 24 publications

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“…In addition, the heat transfer mechanism and function of the Universal Pearson VII kernel (98.48%), the end of the REP algorithm (97.50%) and the Bayesian correlation with the Hill Climber search algorithm (96.89%) ) is much higher, respectively. The results presented in this article can be used to develop an efficient system for the complete automation and classification of rice grains [8].…”

Section: Litrature Reviewmentioning

confidence: 99%

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

2020

CEIS

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Rice is the source of Pakistan agriculture industry and food. For agriculture, industry and the oldest sector in the world use rice for different purpose. There are many challenges in the particular sector such as their analysis. This analysis mostly often related to its texture, color, shape, grain etc. In this study, Vision system used to check the quality of rice using some texture features such as color, shape and characteristics. In this study Computer Vision Image Processing tool applied on three different types of rice. Using this tool we apply pattern classification using nearest neighbor and K-nearest neighbor algorithm. Using these algorithms we get results of three varieties of rice Bastmati, Jasmine and White rice. In both algorithms white rice result show best from Basmati rice and Jasmine rice. White rice result is 93.75 % which is best as quality wise. Other tool also available like as MATLAB, Mazda etc for future more best prediction.

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Section: Litrature Reviewmentioning

confidence: 99%

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

2020

CEIS

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“…While the accuracy shows that how close to the observed data are the predictions given by a classi er. These measures were calculated using equations 22 and 23 [62,63].…”

Section: Where;mentioning

confidence: 99%

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Bakhshipour

Zareiforoush

2020

Preprint

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A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies were 95.56% for J48-CFS, 92.78% for REP-CFS, and 93.33% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

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“…In the context of grading of rice, the percentage of the head or whole grain and broken grain is a most important factor which determines the milling efficiency. Till date, several studies reported improvement in classification accuracy of rice grain using machine vision and image processing techniques [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [14] proposed a methodology to identify five corn varieties with the accuracy of more than 90% using pattern recognition techniques and neural networks. In a comparative analysis in [11] of artificial neural networks, support vector machines, decision trees and Bayesian Networks to classify milled rice samples, it has produced highest classification accuracy with ANN. Despite promising results, there are several problems might arise with ANN's training and designing [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based Grading of Basmati Rice Grains Using Image Processing Technique

Mandal

2018

ASI

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Grading of rice intents to discriminate broken and whole grain from a sample. Standard techniques for image-based rice grading using advanced statistical methods seldom take into account the domain knowledge associated with the data. In the context of a high product value basmati rice with an image based grading process, one ought to consider the physical properties of grain and the associated knowledge. In this present work, a model of quality grade testing and identification is proposed using a novel digital image processing and knowledge-based adaptive neuro-fuzzy inference system (ANFIS). The rationale behind adopting a grading system based on fuzzy rules relies on capabilities of ANFIS to simulate the behaviour of an expert in the characterization of rice grain using the physical properties of rice grains. The rice kernels are characterized with the help of morphological descriptors and geometric features which are derived from sample images of milled basmati rice. The predictive capability of the proposed technique has been tested on a sufficient number of training and test images of basmati rice grain. The proposed method outperforms with a promising result in an evaluation of rice quality with >98.5% classification accuracy for broken and whole grain as compared to standard machine learning technique viz. support vector machine (SVM) and K-nearest neighbour (KNN). The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency.

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Qualitative classification of milled rice grains using computer vision and metaheuristic techniques

Cited by 62 publications

References 24 publications

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Adaptive Neuro-Fuzzy Inference System Based Grading of Basmati Rice Grains Using Image Processing Technique

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