Monitoring milling quality of rice by image analysis

School of Environment, Resources and Development (SERD), Asian Institute of Technology, Krung Thep, Bangkok, Thailand
Computers and Electronics in Agriculture (Impact Factor: 1.76). 12/2001; 33(1):19-33. DOI: 10.1016/S0168-1699(01)00169-7

ABSTRACT Rough rice is milled to produce polished edible grain by first subjecting to dehusking or removal of hulls and then to the removal of brownish outer bran layer known as whitening. The control of whiteness (degree of milling) and percentage of broken kernels in milled rice is required to minimize the economic loss to the millers. Digital image analysis was used to determine the head rice yield (HRY), representing the proportion by weight of milled kernels with three quarters or more of their original length, and the whiteness of milled rice. Ten varieties of Thai rice were subjected to varying degrees of milling by adjusting the test duration from 0.5 to 2.5 min. Three-dimensional features (namely, length, perimeter and projected area) were extracted from the images of individual kernels in a milled sample and used to compute a characteristic dimension ratio (CDR) defined as the ratio of the sum of a particular dimensional feature of all head rice kernels to that of all kernels comprising head and broken rice in the sample. HRY and CDR were found to be related by power functions based on the above-mentioned dimensional features, with R2 more than 0.99 in all cases. The CDR based on the projected area of kernels in their natural rest position provided the best estimate of the HRY with the lowest root mean square error of 1.1% among all dimensional features studied. In case of the whiteness of milled samples, the values provided by a commercial whiteness meter and the mean of gray level distribution determined by image analysis correlated with an R2 value of 0.99. The results of this study showed that two-dimensional imaging of milled rice kernels could be used for making quantitative assessment of HRY and degree of milling for on-line monitoring and better control of the rice milling operation.

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Available from: B.K. Yadav, Sep 28, 2015
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    • "Student's t test indicated that the mean reflectance values of the bran pixels significantly differed from those of the endosperm pixels in the selected spectral range (P < 0.05). This result verified the concept of measuring rice DOM based on surface light reflectance (Yadav and Jindal 2001) and justified the use of HSI for assessing residual bran distribution on rice surface. The mean reflectance values of the bran and endosperm pixels throughout the selected spectral range (485–930 nm) were 0.50 and 0.32, respectively. "
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    • "Several researchers (Abud-Archila et al., 2000; Bello et al., 2006; Chen et al., 1997; Clement and Seguy, 1994; Reid et al., 1998; Sarker and Farouk, 1989; Yadav and Jindal, 2001; Yang et al.,) determined some milling quality of rice grains . "
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    • "Recent research has shown that machine vision has the potential to become a viable tool for rice quality inspection, most of these studies have utilized well-defined images of rice kernels acquired under controlled conditions. Under controlled situations, rice kernels are usually placed apart from each other manually or by other means during image acquisition (Yadav and Jindal, 2001; Igathinathane et al., 2008). However, it is quite time consuming and/or not always practical to separate the rice prior to imaging. "
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