Development of an expert vision-based system for inspecting rice quality indices
Main Article Content
Keywords
rice inspection, image analysis, colour processing, wavelet transform, defect detection
Abstract
In this study, a computer vision system comprising of a special rice tray, scanner, and computer-aided processing software was developed to assess rice appearance quality. The applicability of the system was evaluated for assessment of four rice varieties. Rice grains were accurately (>98%) classified into whole and broken kernels regarding their dimensional features estimated precisely with coefficient of determination (R2) of more than 98% and root mean squared error of 0.08. Optimal thresholding on the vertical coefficient of wavelet transform resulted in fissure detection with an accuracy of 96.51%. Red and black spots of the rice kernels were also precisely detected by thresholding on the red colour difference and gray-scale components respectively. Results indicated that very high accuracies (R2 about 99%) were obtained for whiteness and chalkiness measurements. It was concluded that the image processing technique has a significant potential to be applied for appearance quality assessment of rice kernels.
References
Ajay, G., Suneel, M., Kumar, K.K. and Prasad, P.S., 2013. Quality evaluation of rice grains using morphological methods. International Journal of Soft Computing and Engineering 2: 35-37.
Anami, B.S., Naveen, N. and Hanamaratti, N., 2015. Behavior of HSI color co-occurrence features in variety recognition from bulk paddy grain image samples. International Journal of Signal Processing, Image Processing and Pattern Recognition 8: 19-30.
Azman, N., Bejo, S.K., Ismail, W., Ishak, W. and Wayayok, A., 2014. Estimating maturity of paddy using RGB colour space. Journal of Advanced Agricultural Technologies 1: 119-124.
Brescia, M.A., Sacco, D., Sgaramella, A., Pasqualone, A., Simeone, R., Peri, G. and Sacco, A., 2007. Characterisation of different typical Italian breads by means of traditional, spectroscopic and image analyses. Food Chemistry 104: 429-438.
Bulanon, D., Kataoka, T., Ota, Y. and Hiroma, T., 2002. AE–automation and emerging technologies: a segmentation algorithm for the automatic recognition of Fuji apples at harvest. Biosystems Engineering 83: 405-412.
Chaugule, A. and Mali, S.N., 2014. Evaluation of texture and shape features for classification of four paddy varieties. Journal of Engineering 2014, Article ID: 617263.
Chen, X., Ke, S., Wang, L., Xu, H. and Chen, W., 2012. Classification of rice appearance quality based on LS-SVM using machine vision. In: Liu, C., Wang, L. and Yang, A. (eds) Information Computing and Applications, ICICA 2012. Communications in Computer and Information Science, volume 307. Springer Verlag, Berlin, Germany, pp. 104-109.
Courtois, F., Faessel, M. and Bonazzi, C., 2010. Assessing breakage and cracks of parboiled rice kernels by image analysis techniques. Food control 21: 567-572.
Dehrouyeh, M., Omid, M., Ahmadi, H., Mohtasebi, S. and Jamzad, M., 2010. Grading and quality inspection of defected eggs using machine vision. International Journal of Advanced Science and Technology 17: 23-31.
Dubosclard, P., Larnier, S., Konik, H., Herbulot, A. and Devy, M., 2015. Automated visual grading of grain kernels by machine vision. In: Meriaudeau, F. and Aubreton, O. (eds.). Proceedings Volume 9534 of the 12th International Conference on Quality Control by Artificial Vision 2015, Le Creusot, France. International Society for Optics and Photonics, Bellingham, WA, USA, 8 pp.
Dutta, M.K., Singh, A. and Ghosal, S., 2015. A computer vision based technique for identification of acrylamide in potato chips. Computers and Electronics in Agriculture 119: 40-50.
Golpour, I., Parian, J.A. and Chayjan, R.A., 2014. Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network. Czech Journal of Food Science 32: 280-287.
Gudipalli, A., Prabha, N.A. and Reddy Ch, P., 2016. A review on analysis and grading of rice using image processing. ARPN Journal of Engineering and Applied Sciences 11: 13550-13555.
Haff, R.P. and Toyofuku, N., 2008. X-ray detection of defects and contaminants in the food industry. Sensing and Instrumentation for Food Quality and Safety 2: 262-273.
Hosseinpour, S., Rafiee, S., Aghbashlo, M. and Mohtasebi, S.S., 2014. A novel image processing approach for in-line monitoring of visual texture during shrimp drying. Journal of Food Engineering 143: 154-166.
Huang, C., Yang, W., Duan, L., Jiang, N., Chen, G., Xiong, L. and Liu, Q., 2013. Rice panicle length measuring system based on dual-camera imaging. Computers and Electronics in Agriculture 98: 158-165.
Institute of Standards and Industrial Research of Iran (ISIRI), 2012. Rice – specifications and test methods. ISIRI, Tehran, Iran. Available at: http://standard.isiri.gov.ir/.
Jia, W., Yue, Y., Fernstrom, J.D., Yao, N., Sclabassi, R.J., Fernstrom, M.H. and Sun, M., 2012. Imaged based estimation of food volume using circular referents in dietary assessment. Journal of Food Engineering 109: 76-86.
Kawamura, S., Natsuga, M., Takekura, K. and Itoh, K., 2003. Development of an automatic rice-quality inspection system. Computers and Electronics in Agriculture 40: 115-126.
Kiani, S. and Minaei, S., 2016. Potential application of machine vision technology to saffron (Crocus sativus L.) quality characterization. Food Chemistry 212: 392-394.
Kuo, T.-Y., Chung, C.-L., Chen, S.-Y., Lin, H.-A. and Kuo, Y.-F., 2016. Identifying rice grains using image analysis and sparse-representation-based classification. Computers and Electronics in Agriculture 127: 716-725.
Lakshmi, G., Rao, A.M. and Sachin, K., 2016. Crack detection in paddy using image processing techniques. International Journal of Scientific and Engineering Research 7: 336-339.
Mandavi, R. and Nagwanshi, K.K., 2015. Content based image retrieval and classification using principal component analysis. International Journal of Science and Research 4: 2579-2582.
Mebatsion, H., Paliwal, J. and Jayas, D., 2013. Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Computers and Electronics in Agriculture 90: 99-105.
Otsu, N., 1979. An automatic threshold selection method based on discriminate and least squares criteria. Denshi Tsushin Gakkai Ronbunshi 63: 349-356.
Pearson, T., 2009. Hardware-based image processing for high-speed inspection of grains. Computers and Electronics in Agriculture 69: 12-18.
Sansomboonsuk, S. and Afzulpurkar, N., 2006. The appropriate algorithms of image analysis for rice kernel quality evalution. In: Proceedings of the 20th Conference of Mechanical Engineering Network of Thailand, Bangkok, October 18-20, 2006, Nakhon Ratchasima, Thailand, pp. 18-20. Available at: http://me.psu.ac.th/tsme/ME_NETT20/article/pdf/amm/AMM096.pdf.
Shei, H.-J. and Lin, C.-S., 2012. An optical automatic measurement method for the moisture content of rough rice using image processing techniques. Computers and Electronics in Agriculture 85: 134-139.
Shouche, S., Rastogi, R., Bhagwat, S. and Sainis, J.K., 2001. Shape analysis of grains of Indian wheat varieties. Computers and Electronics in Agriculture 33: 55-76.
Sun, C., Liu, T., Ji, C., Jiang, M., Tian, T., Guo, D., Wang, L., Chen, Y. and Liang, X., 2014. Evaluation and analysis the chalkiness of connected rice kernels based on image processing technology and support vector machine. Journal of Cereal Science 60: 426-432.
Verma, B., 2010. Image processing techniques for grading and classification of rice. In: Computer and Communication Technology (ICCCT) 2010, International Conference on IEEE, September 17-19, 2010, Allahabad, Uttar Pradesh, India, pp. 220-223.
Vithu, P. and Moses, J., 2016. Machine vision system for food grain quality evaluation: a review. Trends in Food Science and Technology 56: 13-20.
Wan, P., Long, C. and Huang, X., 2010. A detection method of rice process quality based on the color and BP neural network. International Conference on Computer and Computing Technologies in Agriculture. In: Li, D., Liu, Y. and Chen, Y. (eds.) Computer and computing technologies in agriculture IV: 4th IFIP TC 12 Conference, CCTA 2010, Nanchang, China, October 22-25, 2010, Selected Papers, Part I. Springer, Berlin, Germany, pp. 25-34.
Wang, Y., Wang, D., Zhang, G. and Wang, J., 2013. Estimating nitrogen status of rice using the image segmentation of GR thresholding method. Field Crops Research 149: 33-39.
Yadav, B. and Jindal, V., 2001. Monitoring milling quality of rice by image analysis. Computers and Electronics in Agriculture 33: 19-33.
Yao, Q., Chen, J., Guan, Z., Sun, C. and Zhu, Z., 2009. Inspection of rice appearance quality using machine vision. In: Intelligent Systems GCIS ‘09, WRI Global Congress on Intelligent Systems, May 19-21, 2009. Xiamen, China P.R., pp. 274-279.
Yun, Y.-T., Chung, C.-T., Lee, Y.-J., Na, H.-J., Lee, J.-C., Lee, D.-H., Lee, K.-W., Kang, J.-W. and Ahn, S.-N., 2015. Variation of yield and quality in early maturing rice cultivars by transplanting date in Chungnam plain area in Korea. Plant Breeding and Biotechnology 3: 47-57.
Zapotoczny, P., Zielinska, M. and Nita, Z., 2008. Application of image analysis for the varietal classification of barley: morphological features. Journal of Cereal Science 48: 104-110.
Zareiforoush, H., Minaei, S., Alizadeh, M.R. and Banakar, A., 2015. A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice. Measurement 66: 26-34.
Zareiforoush, H., Minaei, S., Alizadeh, M.R. and Banakar, A., 2016a. Qualitative classification of milled rice grains using computer vision and metaheuristic techniques. Journal of Food Science and Technology 53: 118-131.
Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. and Samani, B.H., 2016b. Design, development and performance evaluation of an automatic control system for rice whitening machine based on computer vision and fuzzy logic. Computers and Electronics in Agriculture 124: 14-22.
Anami, B.S., Naveen, N. and Hanamaratti, N., 2015. Behavior of HSI color co-occurrence features in variety recognition from bulk paddy grain image samples. International Journal of Signal Processing, Image Processing and Pattern Recognition 8: 19-30.
Azman, N., Bejo, S.K., Ismail, W., Ishak, W. and Wayayok, A., 2014. Estimating maturity of paddy using RGB colour space. Journal of Advanced Agricultural Technologies 1: 119-124.
Brescia, M.A., Sacco, D., Sgaramella, A., Pasqualone, A., Simeone, R., Peri, G. and Sacco, A., 2007. Characterisation of different typical Italian breads by means of traditional, spectroscopic and image analyses. Food Chemistry 104: 429-438.
Bulanon, D., Kataoka, T., Ota, Y. and Hiroma, T., 2002. AE–automation and emerging technologies: a segmentation algorithm for the automatic recognition of Fuji apples at harvest. Biosystems Engineering 83: 405-412.
Chaugule, A. and Mali, S.N., 2014. Evaluation of texture and shape features for classification of four paddy varieties. Journal of Engineering 2014, Article ID: 617263.
Chen, X., Ke, S., Wang, L., Xu, H. and Chen, W., 2012. Classification of rice appearance quality based on LS-SVM using machine vision. In: Liu, C., Wang, L. and Yang, A. (eds) Information Computing and Applications, ICICA 2012. Communications in Computer and Information Science, volume 307. Springer Verlag, Berlin, Germany, pp. 104-109.
Courtois, F., Faessel, M. and Bonazzi, C., 2010. Assessing breakage and cracks of parboiled rice kernels by image analysis techniques. Food control 21: 567-572.
Dehrouyeh, M., Omid, M., Ahmadi, H., Mohtasebi, S. and Jamzad, M., 2010. Grading and quality inspection of defected eggs using machine vision. International Journal of Advanced Science and Technology 17: 23-31.
Dubosclard, P., Larnier, S., Konik, H., Herbulot, A. and Devy, M., 2015. Automated visual grading of grain kernels by machine vision. In: Meriaudeau, F. and Aubreton, O. (eds.). Proceedings Volume 9534 of the 12th International Conference on Quality Control by Artificial Vision 2015, Le Creusot, France. International Society for Optics and Photonics, Bellingham, WA, USA, 8 pp.
Dutta, M.K., Singh, A. and Ghosal, S., 2015. A computer vision based technique for identification of acrylamide in potato chips. Computers and Electronics in Agriculture 119: 40-50.
Golpour, I., Parian, J.A. and Chayjan, R.A., 2014. Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network. Czech Journal of Food Science 32: 280-287.
Gudipalli, A., Prabha, N.A. and Reddy Ch, P., 2016. A review on analysis and grading of rice using image processing. ARPN Journal of Engineering and Applied Sciences 11: 13550-13555.
Haff, R.P. and Toyofuku, N., 2008. X-ray detection of defects and contaminants in the food industry. Sensing and Instrumentation for Food Quality and Safety 2: 262-273.
Hosseinpour, S., Rafiee, S., Aghbashlo, M. and Mohtasebi, S.S., 2014. A novel image processing approach for in-line monitoring of visual texture during shrimp drying. Journal of Food Engineering 143: 154-166.
Huang, C., Yang, W., Duan, L., Jiang, N., Chen, G., Xiong, L. and Liu, Q., 2013. Rice panicle length measuring system based on dual-camera imaging. Computers and Electronics in Agriculture 98: 158-165.
Institute of Standards and Industrial Research of Iran (ISIRI), 2012. Rice – specifications and test methods. ISIRI, Tehran, Iran. Available at: http://standard.isiri.gov.ir/.
Jia, W., Yue, Y., Fernstrom, J.D., Yao, N., Sclabassi, R.J., Fernstrom, M.H. and Sun, M., 2012. Imaged based estimation of food volume using circular referents in dietary assessment. Journal of Food Engineering 109: 76-86.
Kawamura, S., Natsuga, M., Takekura, K. and Itoh, K., 2003. Development of an automatic rice-quality inspection system. Computers and Electronics in Agriculture 40: 115-126.
Kiani, S. and Minaei, S., 2016. Potential application of machine vision technology to saffron (Crocus sativus L.) quality characterization. Food Chemistry 212: 392-394.
Kuo, T.-Y., Chung, C.-L., Chen, S.-Y., Lin, H.-A. and Kuo, Y.-F., 2016. Identifying rice grains using image analysis and sparse-representation-based classification. Computers and Electronics in Agriculture 127: 716-725.
Lakshmi, G., Rao, A.M. and Sachin, K., 2016. Crack detection in paddy using image processing techniques. International Journal of Scientific and Engineering Research 7: 336-339.
Mandavi, R. and Nagwanshi, K.K., 2015. Content based image retrieval and classification using principal component analysis. International Journal of Science and Research 4: 2579-2582.
Mebatsion, H., Paliwal, J. and Jayas, D., 2013. Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Computers and Electronics in Agriculture 90: 99-105.
Otsu, N., 1979. An automatic threshold selection method based on discriminate and least squares criteria. Denshi Tsushin Gakkai Ronbunshi 63: 349-356.
Pearson, T., 2009. Hardware-based image processing for high-speed inspection of grains. Computers and Electronics in Agriculture 69: 12-18.
Sansomboonsuk, S. and Afzulpurkar, N., 2006. The appropriate algorithms of image analysis for rice kernel quality evalution. In: Proceedings of the 20th Conference of Mechanical Engineering Network of Thailand, Bangkok, October 18-20, 2006, Nakhon Ratchasima, Thailand, pp. 18-20. Available at: http://me.psu.ac.th/tsme/ME_NETT20/article/pdf/amm/AMM096.pdf.
Shei, H.-J. and Lin, C.-S., 2012. An optical automatic measurement method for the moisture content of rough rice using image processing techniques. Computers and Electronics in Agriculture 85: 134-139.
Shouche, S., Rastogi, R., Bhagwat, S. and Sainis, J.K., 2001. Shape analysis of grains of Indian wheat varieties. Computers and Electronics in Agriculture 33: 55-76.
Sun, C., Liu, T., Ji, C., Jiang, M., Tian, T., Guo, D., Wang, L., Chen, Y. and Liang, X., 2014. Evaluation and analysis the chalkiness of connected rice kernels based on image processing technology and support vector machine. Journal of Cereal Science 60: 426-432.
Verma, B., 2010. Image processing techniques for grading and classification of rice. In: Computer and Communication Technology (ICCCT) 2010, International Conference on IEEE, September 17-19, 2010, Allahabad, Uttar Pradesh, India, pp. 220-223.
Vithu, P. and Moses, J., 2016. Machine vision system for food grain quality evaluation: a review. Trends in Food Science and Technology 56: 13-20.
Wan, P., Long, C. and Huang, X., 2010. A detection method of rice process quality based on the color and BP neural network. International Conference on Computer and Computing Technologies in Agriculture. In: Li, D., Liu, Y. and Chen, Y. (eds.) Computer and computing technologies in agriculture IV: 4th IFIP TC 12 Conference, CCTA 2010, Nanchang, China, October 22-25, 2010, Selected Papers, Part I. Springer, Berlin, Germany, pp. 25-34.
Wang, Y., Wang, D., Zhang, G. and Wang, J., 2013. Estimating nitrogen status of rice using the image segmentation of GR thresholding method. Field Crops Research 149: 33-39.
Yadav, B. and Jindal, V., 2001. Monitoring milling quality of rice by image analysis. Computers and Electronics in Agriculture 33: 19-33.
Yao, Q., Chen, J., Guan, Z., Sun, C. and Zhu, Z., 2009. Inspection of rice appearance quality using machine vision. In: Intelligent Systems GCIS ‘09, WRI Global Congress on Intelligent Systems, May 19-21, 2009. Xiamen, China P.R., pp. 274-279.
Yun, Y.-T., Chung, C.-T., Lee, Y.-J., Na, H.-J., Lee, J.-C., Lee, D.-H., Lee, K.-W., Kang, J.-W. and Ahn, S.-N., 2015. Variation of yield and quality in early maturing rice cultivars by transplanting date in Chungnam plain area in Korea. Plant Breeding and Biotechnology 3: 47-57.
Zapotoczny, P., Zielinska, M. and Nita, Z., 2008. Application of image analysis for the varietal classification of barley: morphological features. Journal of Cereal Science 48: 104-110.
Zareiforoush, H., Minaei, S., Alizadeh, M.R. and Banakar, A., 2015. A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice. Measurement 66: 26-34.
Zareiforoush, H., Minaei, S., Alizadeh, M.R. and Banakar, A., 2016a. Qualitative classification of milled rice grains using computer vision and metaheuristic techniques. Journal of Food Science and Technology 53: 118-131.
Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. and Samani, B.H., 2016b. Design, development and performance evaluation of an automatic control system for rice whitening machine based on computer vision and fuzzy logic. Computers and Electronics in Agriculture 124: 14-22.
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Feb 6, 2018
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