Development of a Classification System for Quality Evaluation of Oryza Sativa L. (Rice) Using Computer Vision

Carrying out effective and sustainable agriculture product has become an important issue in recent years. Agricultural production has to keep up with an ever-increasing population. A key to this is the usage of modern techniques (for precision agriculture) to take advantage of the quality in the market. The paper reviews various quality evaluation and grading techniques of Oryza Sativa L. (rice) in food industry using computer vision and image processing. In this paper basic problem of rice industry for quality assessment is defined which is traditionally done manually by human inspector. Computer Vision provides one alternative for an automated, non-destructive and cost-effective technique. In this paper we quantify the qualities of various rice varieties in Asian subcontinent and figure out features which directly or indirectly affect the quality of the rice. Based on these features a generalized approach of quality is proposed to be used for quality evaluation of any type of rice variety.

[1]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[2]  Dai Xiaopeng,et al.  Research on the rice chalkiness measurement based on the image processing technique , 2011, 2011 3rd International Conference on Computer Research and Development.

[3]  Kavindra R. Jain,et al.  Non-destructive quality evaluation in spice industry with specific reference To Cuminum cyminum L (cumin) seeds , 2009, 2009 Innovative Technologies in Intelligent Systems and Industrial Applications.

[4]  Murat O. Balaban,et al.  Machine Vision Applications to Aquatic Foods: A Review , 2011 .

[5]  Bhupinder Verma,et al.  Image processing techniques for grading & classification of rice , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).

[6]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[7]  Da-Wen Sun,et al.  Recent applications of image texture for evaluation of food qualities—a review , 2006 .

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

[9]  Kavindra R. Jain,et al.  Non-Destructive Quality Evaluation in Spice Industry with Specific Reference to Cuminum Cyminum L (Cumin) Seeds , 2009, 2009 Second International Conference on Emerging Trends in Engineering & Technology.

[10]  N. Shobha Rani,et al.  Historical significance, grain quality features and precision breeding for improvement of export quality basmati varieties in India , 2006 .

[11]  M. Khodaparast,et al.  Effect of Stewing in Cooking Step on Textural and Morphological Properties of Cooked Rice , 2009 .

[12]  Kavindra R. Jain,et al.  Unified Approach in Food Quality Evaluation Using Machine Vision , 2011, ACC.

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

[14]  Yande Liu,et al.  An automatic method for identifying different variety of rice seeds using machine vision technology , 2010, 2010 Sixth International Conference on Natural Computation.

[15]  R.M. Carter,et al.  Characterisation and Identification of Rice Grains through Digital Image Analysis , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[16]  Seyed Mohammad Ali Razavi,et al.  Monitoring geometric characteristics of rice during processing by image analysis system and micrometer measurement , 2010 .

[17]  Ahmed S. Abutaleb,et al.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989, Comput. Vis. Graph. Image Process..

[18]  M. Abdullah,et al.  QUALITY INSPECTION OF BAKERY PRODUCTS USING A COLOR-BASED MACHINE VISION SYSTEM , 2000 .

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

[20]  Li Yaoming,et al.  Multi-scale edge detection of rice internal damage based on computer vision , 2008, 2008 IEEE International Conference on Automation and Logistics.

[21]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[22]  Ahmed S. Abutableb.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989 .

[23]  Nitin Afzulpurkar,et al.  The Appropriate Algorithms of Image analysis for Rice Kernel Quality Evalution , 2006 .

[24]  Sundaram Gunasekaran,et al.  Computer vision technology for food quality assurance , 1996 .

[25]  Y.–N. Wan,et al.  KERNEL HANDLING PERFORMANCE OF AN AUTOMATIC GRAIN QUALITY INSPECTION SYSTEM , 2002 .

[26]  C. K. Modi,et al.  Image morphological operation based quality analysis of coriander seed (Coriandrum satavum L) , 2011, 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC).

[27]  Da-Wen Sun,et al.  Learning techniques used in computer vision for food quality evaluation: a review , 2006 .

[28]  Chang-Chun Liu,et al.  Classifying Paddy Rice by Morphological and Color Features Using Machine Vision , 2005 .

[29]  M. Z. Abdullah,et al.  Automated inspection system for colour and shape grading of starfruit (Averrhoa carambola L.) using machine vision sensor , 2005 .

[30]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[31]  C. Park,et al.  Intelligent classification methods of grain kernels using computer vision analysis , 2011 .

[32]  José Blasco,et al.  Machine Vision System for Automatic Quality Grading of Fruit , 2003 .