A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice

Abstract In this research, a fuzzy inference system (FIS) coupled with image processing technique was developed as a decision-support system for qualitative grading of milled rice. Two quality indices, namely degree of milling (DOM) and percentage of broken kernels (PBK) were first graded by rice processing experts into five classes. Then, images of the same samples were captured using a machine vision system. The information obtained from the sample image processing was transferred to FIS. The FIS classifier consisted of two input linguistic variables, namely, DOM and PBK, and one output variable (Quality), all in the form of triangle membership functions. Altogether, 25 rules were considered in the FIS rule base using the AND operator and Mamdani inference system. In order to evaluate the developed system, statistical performance of the FIS classifier was compared with the experts’ judgments. Results of analysis showed a 89.8% agreement between the grading results obtained from the developed system and those determined by the experts.

[1]  Kaveh Mollazade,et al.  Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification , 2012 .

[2]  Digvir S. Jayas,et al.  Classification of cereal grains using machine vision: I. Morphology models. , 2000 .

[3]  Harpreet Kaur,et al.  Classification and Grading Rice Using Multi-Class SVM , 2013 .

[4]  Hong Xu,et al.  Classification of Rice Appearance Quality Based on LS-SVM Using Machine Vision , 2012, ICICA.

[5]  T. Nagatsuka,et al.  Classification of Philippine rice grains using machine vision and artificial neural networks. , 2008 .

[6]  B. K. Yadav,et al.  Modeling changes in milled rice (Oryza sativa L.) kernel dimensions during soaking by image analysis , 2007 .

[7]  Yul Y. Nazaruddin,et al.  Estimation of rice milling degree using image processing and Adaptive Network Based Fuzzy Inference System (ANFIS) , 2011, 2011 2nd International Conference on Instrumentation Control and Automation.

[8]  Samir Majumdar,et al.  Classification of cereal grains using machine vision , 1997 .

[9]  Peng Wan,et al.  An Inspection Method of Rice Milling Degree Based on Machine Vision and Gray-Gradient Co-occurrence Matrix , 2010, CCTA.

[10]  Mohd Azlan Hussain,et al.  Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques , 2012 .

[11]  C. S. Silva,et al.  Classification of Rice Grains Using Neural Networks , 2013 .

[12]  Hemad Zareiforoush,et al.  Milling characteristics of rice grains as affected by paddy mixture ratio and moisture content. , 2014 .

[13]  Hassan Ghassemian,et al.  Applied machine vision and artificial neural network for modeling and controlling of the grape drying process , 2013 .

[14]  Mahmoud Omid,et al.  An expert egg grading system based on machine vision and artificial intelligence techniques , 2013 .

[15]  M. H. Komarizadeh,et al.  Effects of crop-machine variables on paddy grain damage during handling with an inclined screw auger , 2010 .

[16]  Mahmoud Omid,et al.  An Intelligent Combined Method Based on Power Spectral Density, Decision Trees and Fuzzy Logic for Hydraulic Pumps Fault Diagnosis , 2008 .

[17]  P. Cano Marchal,et al.  Expert system based on computer vision to estimate the content of impurities in olive oil samples , 2013 .

[18]  Lihong Xie,et al.  Simultaneous Determination of Multi Rice Quality Parameters Using Image Analysis Method , 2014, Food Analytical Methods.

[19]  Wang Jiu-gen,et al.  Porous structures of natural materials and bionic design , 2005 .

[20]  Xin Chen,et al.  A hybrid fuzzy evaluation method for safety assessment of food-waste feed based on entropy and the analytic hierarchy process methods , 2014, Expert Syst. Appl..

[21]  Gilles Trystram,et al.  Fuzzy concepts applied to food product quality control: A review , 2006, Fuzzy Sets Syst..

[22]  Thomas Becker,et al.  Fuzzy logic control and soft sensing applications in food and beverage processes , 2013 .

[23]  J. R. Parker,et al.  Rank and response combination from confusion matrix data , 2001, Inf. Fusion.

[24]  Mahmoud Omid,et al.  Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier , 2011, Expert Syst. Appl..

[25]  Gilles Trystram,et al.  How human expertise at industrial scale and experiments can be combined to improve food process knowledge and control , 2007 .

[26]  Kwang Hyung Lee,et al.  First Course on Fuzzy Theory and Applications , 2005, Advances in Soft Computing.

[27]  F. Cheng,et al.  Identification of rice seed varieties using neural network. , 2005, Journal of Zhejiang University. Science. B.

[28]  Mahmoud Omid,et al.  A novel artificial neural networks assisted segmentation algorithm for discriminating almond nut and shell from background and shadow , 2014 .

[29]  Sanjivani Shantaiya,et al.  Identification Of Food Grains And Its Quality Using Pattern Classification , 2010 .

[30]  Kazuo Tanaka,et al.  Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach , 2008 .