Abstract
Determination of rice quality is a key function for rice research and rice industry. Traditionally, rice quality parameters were performed separately by human visual inspection. It is labor-intensive and time-consuming. In this paper, a novel analysis method was proposed to determine multi rice quality parameters simultaneously. On the glass of a flatbed scanner, 35 g rice kernels was spread and imaged. Then, the length, width, aspect ratio, head rice yield, percentage of chalky rice, chalkiness, and transparency grade were obtained after simply processing the acquired image. The developed method for the determination of rice quality was tested on 507 milled rice samples and compared to the traditional manual analysis method. The regression coefficients (R) were 0.9916, 0.9691, 0.9938, 0.9929, 0.9649, and 0.9377 for length, width, aspect ratio, head rice yield, percentage of chalky rice, and chalkiness, respectively. The accuracies of length, width, aspect ratio, and head rice yield determined by the developed method were 100, 100, 100, and 92.1 % respectively. The results indicated that the developed method was a useful and alternative method for determining rice quality.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aggarwal AK, Mohan R (2010) Aspect ratio analysis using image processing for rice grain quality. Int J Food Eng 6:8. doi:10.2202/1556-3758.1788
Bornhorst GM, Kostlan K, Singh RP (2013) Particle size distribution of brown and white rice during gastric digestion measured by image analysis. J Food Sci 78:E1383–E1391. doi:10.1111/1750-3841.12228
Brosnan T, Sun DW (2002) Inspection and grading of agricultural and food products by computer vision systems—a review. Comput Electron Agric 36:193–213. doi:10.1016/S0168-1699(02)00101-1
Camelo-Mendez GA, Camacho-Diaz BH, del Villar-Martinez AA, Arenas-Ocampo ML, Bello-Perez LA, Jimenez-Aparicio AR (2012) Digital image analysis of diverse Mexican rice cultivars. J Sci Food Agric 92:2709–2714. doi:10.1002/jsfa.5693
Cheng F, Ying YB (2004) Image recognition of diseased rice seeds based on color feature. Proc SPIE 5587:224–231. doi:10.1117/12.570095
Courtois F, Faessel M, Bonazzi C (2010) Assessing breakage and cracks of parboiled rice kernels by image analysis techniques. Food Control 21:567–572. doi:10.1016/j.foodcont.2009.08.006
Davies ER (2009) The application of machine vision to food and agriculture: a review. Imaging Sci J 57:197–217. doi:10.1179/174313109X454756
Emadzadeh B, Razavi SMA, Farahmandfar R (2010) Monitoring geometric characteristics of rice during processing by image analysis system and micrometer measurement. Int Agrophysics 24:21–27
Hernandez JA, Heyd B, Trystram G (2008a) On-line assessment of brightness and surface kinetics during coffee roasting. J Food Eng 87:314–322. doi:10.1016/j.jfoodeng.2007.12.009
Hernandez JA, Heyd B, Trystram G (2008b) Prediction of brightness and surface area kinetics during coffee roasting. J Food Eng 89:156–163. doi:10.1016/j.jfoodeng.2008.04.026
Lan Y, Fang Q, Kocher MF, Hanna MA (2002) Detection of fissures in rice grains using imaging enhancement. Int J Food Prop 5:205–215. doi:10.1081/JFP-120015602
Lin P, Chen Y, Bao Y, He Y (2012a) Image detection of rice fissures using biorthogonal B-spline wavelets in multi-resolution spaces. Food Bioprocess Technol 5:2017–2024. doi:10.1007/s11947-010-0463-9
Lin P, Chen Y, He Y (2012b) Identification of broken rice kernels using image analysis techniques combined with velocity representation method. Spaces. Food Bioprocess Technol 5:796–802. doi:10.1007/s11947-010-0454-x
Liu W, Tao Y, Siebenmorgen TJ, Chen H (1998) Digital image analysis method for rapid measurement of rice degree of milling. Cereal Chem 75:380–385. doi:10.1094/CCHEM.1998.75.3.380
Sakai N, Yonekawa S, Matsuzaki A, Morishima H (1996) Two-dimensional image analysis of the shape of rice and its application to separating varieties. J Food Eng 27:397–407. doi:10.1016/0260-8774(95)00022-4
Shimizu N, Haque MA, Andersson M, Kimura T (2008) Measurement and fissuring of rice kernels during quasi-moisture sorption by image analysis. J Cereal Sci 48:98–103. doi:10.1016/j.jcs.2007.08.009
Valous NA, Sun DW (2012) Image processing techniques for computer vision in the food and beverage industries. In: Sun DW (ed) Computer vision technology in the food and beverage industries. Woodhead Publ. LTD., Cambridge, pp 97–129
van Dalen G (2004) Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis. Food Res Int 37:51–58. doi:10.1016/j.foodres.2003.09.001
van Dalen G (2005) Characterisation of rice using flatbed scanning and image analysis. In: Riley AP (ed) Food policy, control and research pp 149-186
Yadav BK, **dal VK (2001) Monitoring milling quality of rice by image analysis. Comput Electron Agric 33:19–33. doi:10.1016/S0168-1699(01)00169-7
Yadav BK, **dal VK (2007a) Dimensional changes in milled rice (Oryza sativa L.) kernel during cooking in relation to its physicochemical properties by image analysis. J Food Eng 81:710–720. doi:10.1016/j.jfoodeng.2007.01.005
Yadav BK, **dal VK (2007b) Modeling changes in milled rice (Oryza sativa L.) kernel dimensions during soaking by image analysis. J Food Eng 80:359–369. doi:10.1016/j.jfoodeng.2006.06.005
Yan L, Park CW, Lee SR, Lee CY (2011) New separation algorithm for touching grain kernels based on contour segments and ellipse fitting. J Zhejiang Univ-SCI C 12:54–61. doi:10.1631/jzus.C0910797
Yonemaru J, Morita S (2012) Image analysis of grain shape to evaluate the effects of high temperatures on grain filling of rice, Oryza sativa L. Field Crop Res 137:268–271. doi:10.1016/j.fcr.2012.08.003
Yao Q, Guan ZX, Zhou YF, Tang J, Hu Y, Yang BJ (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: Yang PL (ed) 2009 International conference on engineering computation, Hong Kong, 2–3 May 2009, pp 79-83. doi: 10.1109/ICEC.2009.73
Yoshioka Y, Iwata H, Tabata M, Ninomiya S, Ohsawa R (2007) Chalkiness in rice: Potential for evaluation with image analysis. Crop Sci 47:2113–2120. doi:10.2135/cropsci2006.10.0631sc
Zhong Q, Zhou P, Yao Q, Mao K (2009) A novel segmentation algorithm for clustered slender-particles. Comput Electron Agric 69:118–127. doi:10.1016/j.compag.2009.06.015
Acknowledgments
This work is funded by the Agriculture Industry Standards Project of the Ministry of Agriculture, China (agricultural finance industry standard no. [2012]565) and the Analysis and Measurement Foundation of Zhejiang Province (project no. 2012C37025).
Conflict of Interest
Changyun Fang declares that he has no conflict of interest. **anqiao Hu declares that she has no conflict of interest. Chengxiao Sun declares that he has no conflict of interest. Binwu Duan declares that she has no conflict of interest. Lihong **e declares that she has no conflict of interest. ** Zhou declares that he has no conflict of interest. This article does not contain any studies with human or animal subjects.
Author information
Authors and Affiliations
Corresponding author
Additional information
Changyun Fang and **anqiao Hu contributed equally to this work.
Rights and permissions
About this article
Cite this article
Fang, C., Hu, X., Sun, C. et al. Simultaneous Determination of Multi Rice Quality Parameters Using Image Analysis Method. Food Anal. Methods 8, 70–78 (2015). https://doi.org/10.1007/s12161-014-9870-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12161-014-9870-2