Monitoring milling quality of rice by image analysis
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.
- SourceAvailable from: Jacopo Aguzzi[Show abstract] [Hide abstract]
ABSTRACT: The appearance of agricultural products deeply conditions their marketing. Appearance is normally evaluated by considering size, shape, form, colour, freshness condition and finally the absence of visual defects. Among these features, the shape plays a crucial role. Description of agricultural product shape is often necessary in research fields for a range of different purposes, including the investigation of shape traits heritability for cultivar descriptions, plant variety or cultivar patents and evaluation of consumer decision performance. This review reports the main applications of shape analysis on agricultural products such as relationships between shape and: (1) genetic; (2) conformity and condition ratios; (3) products characterization; (4) product sorting and finally, (5) clone selection. Shape can be a protagonist of evaluation criteria only if an appreciable level of image shape processing and automation and data are treated with solid multivariate statistic. In this context, image-processing algorithms have been increasingly developed in the last decade in order to objectively measure the external features of agricultural products. Grading and sorting of agricultural products using machine vision in conjunction with pattern recognition techniques offers many advantages over the conventional optical or mechanical sorting devices. With this aims, we propose a new automated shape processing system which could be useful for both scientific and industrial purposes, forming the bases of a common language for the scientific community. We applied such a processing scheme to morphologically discriminate nuts fruit of different species. Operative Matlab codes for shape analysis are reported. KeywordsImage analysis–Shape analysis–Agricultural products–Multivariate statisticsFood and Bioprocess Technology 01/2011; 4(5):673-692. · 4.12 Impact Factor
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ABSTRACT: Digital image analysis has an important role in geographical provenance of grains, as it can provide parameters of size, shape and color, which are important quality parameters for the design of engineering processes such as drying and milling of grains. In this study, digital image analysis was used to classify nine rice cultivars based on different morphometric parameters using the three sides of the grain (lateral, ventral and axial), Feret diameter, and 10 different form factors and color parameters (CIE L*, a* and b*). Result of principal component analyisis was an equation with seven variables (area, perimeter, length, width, thickness, sphericity and color), which was useful for distinguishing between nine different cultivars. The morphometric and color parameters for the Mor A-98 and Mor A-92 varieties showed they had 88% similarity. The variability was expressed with a confidence of 95%. Multivariate analysis indicated that the lateral side is the most sensitive for the classification of Mexican rice grains because of its color and morphometric characteristics. These results showed the application of image analysis for the future classifications of grains. Copyright © 2012 Society of Chemical Industry.Journal of the Science of Food and Agriculture 06/2012; 92(13):2709-14. · 1.76 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: An increasing number of industrial applications requires visual inspection of products. Computer vision provides consolidated tools for reliable and fully automatic characterization and classification of the product quality at relatively low costs. One of such powerful tool is multivariate image analysis (MIA). In the MIA procedure as proposed in  is considered, that is well suited for texture analysis. To extend the performance of the MIA procedure in  to the analysis of wider spatial domains and to improve the algorithm from the computational point of view, a new formulation, named iMIA, has been recently proposed in . The main contribution of the present paper is a modification of the iMIA algorithm that, by exploiting fast Fourier transform filtering, allows a considerable reduction of the computational time when spatial neighborhoods larger than few pixels are considered. Secondly, a different texture characterization with respect to  is proposed, to further extend the algorithm range of applicability. The characterization is based on histograms of textural features . The algorithm is tested on two case studies in the field of texture analysis, namely, classification of rice quality, where the different characterization of texture allows a great improvement with respect to , and the characterization of nanofiber assemblies.Journal of Process Control 01/2012; · 2.18 Impact Factor
Computers and Electronics in Agriculture
33 (2001) 19–33
Monitoring milling quality of rice by image
B.K. Yadav, V.K. Jindal *
Processing Technology Program, School of En?ironment, Resources and De?elopment,
Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand
Received 6 November 2000; received in revised form 18 May 2001; accepted 20 July 2001
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 R2more 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 R2value 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. © 2001 Elsevier Science B.V. All rights reserved.
* Corresponding author. Tel.: +66-2-524-5457; fax: +66-2-524-6200.
E-mail address: email@example.com (V.K. Jindal).
0168-1699/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–3320
Keywords: Head rice yield; Degree of rice milling; Milled rice whiteness; Rice quality determination;
Image analysis; Machine vision
Rice constitutes the world’s principal source of food, being the basic grain for the
planet’s largest population. For tropical Asians it is the staple food and is the major
source of dietary energy and protein. In Southeast Asia alone, rice is the staple food
for 80% of the population (Armienta, 1991).
Milling of rough rice (or paddy) is usually done at about 14% dry basis moisture
content to produce white, polished edible grain, due to consumer preference. From
the economic point of view, the quality of milled rice is of paramount importance
since the grain size and shape, whiteness and cleanliness are strongly correlated with
the transaction price of rice (Conway et al., 1991). All these factors are closely
related to the process of milling, in which rough rice is first subjected to dehusking
or removal of hulls and then to the removal of brownish outer bran layer, known
as whitening. Finally, polishing is carried out to remove the bran particles and
provides surface gloss to the edible white portion. A high percentage of broken
grains in the milled product or low head rice recovery represents a direct economic
loss to the millers. Head rice yield (HRY) represents the weight percent of milled
kernels with three quarters or more of their original length of brown rice relative to
rough rice weight. The degree of milling determines the extent of removal of bran
layer from the surface of milled kernels and thus relates to their whiteness. HRY
reduces with the increased duration of milling. Hence in the milling process, the
pressure in the milling chamber and the duration of milling must be adjusted to get
the maximum output.
The extent of losses during milling depends on many factors, such as variety and
condition of rough rice, degree of milling required, the kind of rice mill used, and
the operators. Modern large capacity commercial rice mills use different machines
for dehusking, whitening and polishing operations. Besides rubber roll dehuskers,
two types of milling machines, namely, abrasive and frictional types, are used for
whitening and polishing of grain, respectively. Among horizontal and vertical
abrasive type milling machines, the vertical type is more popular. The brown rice is
rubbed between the surface of an abrasive cone and sieve fitted with a set of rubber
brakes. In the frictional type machine, brown rice kernels are rubbed against each
other under pressure to get the desired whiteness. The process of bran removal in
commercial milling is through intense pressure and friction in a single or multiple
pass operation over a very short period of time. Proper setting and adjustment of
the clearance between rubber brakes and abrasive cone by the operator are critical
factors in the milling operation. In both types of machines, the degree of milling is
also controlled by adjusting the pressure in the milling chamber by means of a
spring-loaded counterweight at the discharge outlet.
In practice most control systems for rice milling equipment are essentially based
on manual operation. Informal contacts with several large-scale commercial mills in
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–33 21
Pathumthani province in central Thailand revealed that milled rice quality is
regularly monitored manually at approximate time intervals of 1–2 h due to
unavailability of continuous on-line measurement methods. Actual determinations
of HRY and milled rice whiteness are made by laboratory measurements. The
necessary adjustments made by a trained operator, based on visual inspection and
the results of laboratory measurements, take effect in a few minutes to produce
milled rice with a minimum amount of broken kernels and maximum degree of
kernel whiteness. Usually milled rice samples obtained by test milling in the
laboratory are supplied to the operator and used as reference for each grade of the
degree of milling.
Despite the extensive use of image analysis in manufacturing and medical
industries, its applications are almost non-existent in grain-based industries. The
determination of milled rice quality parameters by image processing techniques will
enable regular monitoring of milling operation in an objective manner, and thus
allow the operator to quickly react within a few minutes to changes in material
properties. Fant et al. (1994) determined the gray scale intensity in the digital
images of rice samples subjected to various degrees of milling and correlated the
mean gray level with lipids concentration on the surface of rice kernels. Liu et al.
(1998) used digital image analysis to estimate the area of the bran layer on the
surface of rice kernels and correlated with the surface lipids concentration deter-
mined by chemical analysis. They reported that the degree of milling could be
measured quickly and accurately in terms of the surface lipids concentration in a
milled rice sample. Sometimes the degree of rice milling is characterized relative to
arbitrary whiteness standards depending upon the type of commercial whiteness
meter employed. However, no information is available for the estimation of HRY
based on the monitoring of dimensional parameters of kernels for rapid quality
develop techniques that could be used for estimating HRY and degree of milling
based on two-dimensional imaging of milled rice kernels being sampled at regular
ofthepresentstudy was to
2. Approach to the problem
Although HRY has been defined as the ratio of the weight of milled head rice
kernels to the total weight of the rough rice or paddy kernels for practical purposes,
it could also be expressed relative to the total weight of milled rice rather than
rough rice. In general, the HRY relative to milled rice weight is about 50% higher
than the HRY values based on the weight of rough rice. In general too, it is
possible to estimate the weight of the objects having regular shapes based on their
dimensional characteristics. Therefore, simple models could be developed for
estimating the HRY and whiteness of the milled rice samples using image analysis
from the measurements of dimensional features and gray level distribution, respec-
tively, as described in the following sections.
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–3322
2.1. Head rice yield (HRY) and characteristic dimension ratio (CDR)
Based on the results of an earlier study for a single variety of rice (Yadav and
Jindal, 1998), it was hypothesized that the weight of individual kernels, whether of
head rice and broken fraction, is proportional to their respective dimensional
features. Accordingly, the weight of a milled rice sample might be expressed as a
power function of a composite characteristic dimensional feature derived from the
whole and broken kernels in that sample. The characteristic dimensional features of
the kernels could be their length (L), perimeter (P) and projected area (A) based on
individual measurements. HRY could then be related to the characteristic dimen-
sion ratio (CDR) by
where HRY is the head rice yield defined as the ratio of the weight of head rice to
the combined weight of head and broken rice in a milled rice sample, %; Hiis the
dimensional feature of the ith head rice kernel; Bjis the dimensional feature of the
jth broken rice kernel; n is the number of head rice kernels in the milled rice
sample; m is the number of broken rice kernels in the milled rice sample; and a, b
are equation parameters.
The relationship between HRY and CDR hypothesized in the form of Eq. (1)
could be validated by the experimental data on the characteristic dimensional
features of the kernels and respective weight fractions of the head rice and broken
kernels for different rice varieties. HRY of a milled rice sample could also be
expressed on the basis of the initial weight of rough rice, if so desired.
2.2. Whiteness of milled rice
Most commercial whiteness meters operate on the principle of light reflectance
from the surface of the milled rice to measure its whiteness. Although earlier use of
digital image analysis to correlate mean gray level with lipids concentration on the
surface of rice kernels has been cited (Fant et al., 1994), it was hypothesized that
the overall whiteness of milled rice could be estimated simply from the mean value
of the gray level distribution obtained from the digitized image of the bulk sample.
3. Materials and methods
3.1. Rice samples
Rough rice samples of ten varieties, namely, Suphan Buri scented rice (HSPR),
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–3323
Suphan Buri-1 (SPR1), Klong Luang scented rice (HKLG), Suphan Buri-90
(SPR90), Royal Rice Department-7 (RD7), Royal Rice Department-23 (RD23),
Suphan Buri-60 (SPR60), Chainat-1 (CNT1), Leuang Pra Tew-123 (LPT123) and
Mali scented rice-105 (KDML105) were obtained from the Rice Experiment Center,
Klong Luang, Pathumthani, with moisture contents ranging from 10 to 13% dry
basis. Five samples weighing individually about 200 g, were taken from each rough
rice variety and kept separately in polyethylene bags. All rough rice samples were
dehusked twice with a testing husker (model THU-35A, Satake Engineering Co.
Ltd., Japan), and the brown rice so obtained was subsequently milled using a test
mill (model TM 05, Satake Engineering Co. Ltd., Japan). All brown rice samples of
each variety were then milled for an arbitrarily selected test duration ranging from
a minimum of 0.5 min to a maximum of 2.5 min at intervals of 0.5 min. The
variations in degree of milling of rice samples produced 5 levels of HRY and kernel
whiteness for each variety. Thus a total of 50 samples was used for determining the
milled rice characteristics.
3.2. Imaging of rice kernels
A diagram of the imaging system is shown in Fig. 1. It consisted of a lighting
unit, a color CCD camera and a frame grabber connected to a host Pentium 120
MHz computer. The lighting unit comprised a closed cylindrical image chamber
fitted with a circular 32 W fluorescent lamp working at 60 kHz to provide
flicker-free light. The camera (model 2200, Cohu Inc., USA) was equipped with a
manual zoom lens (model S6X11-II, F1.4 and 11.5–69 mm zoom, Cohu Inc., USA)
capable of producing PAL softvideo output with a resolution of 752 (H)×582 (V).
An Imascan Chroma-P PCI frame grabber board (IMAGRAPH Corporation,
USA) with 2 MB on board memory was used to receive the video signal from the
The operation of the system was carefully controlled for extracting reproducible
features from the captured images of milled rice samples through various adjust-
Fig. 1. Schematic diagram of the IA system.
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–33 24
Calibration results for measurements by the imaging setup
Actual measurementsImaging measurements Difference
ments. All the captured frames were 8-bit (0–255) gray scale images. The images of
rice kernels were obtained in batch mode for HRY determination. Rice kernels
were placed manually without touching each other in a petri dish of about 40
mm×35 mm area directly under the CCD camera. This area could be covered in
a 512×452 pixels frame with approximately 12 times magnification. The dimen-
sional features of individual kernel images were extracted using the ImageTool
program developed at the University of Texas Health Science Center at San
Antonio, Texas and available from the Internet (ftp://maxrad6.uthscsa.edu). The
imaging system was calibrated with the help of brass disks having thickness close to
that of milled rice kernels (1.22–2.88 mm) and by determining their diameter and
projected area independently by a micrometer with a least count of 0.01 mm. Table
1 shows the estimated average diameter and projected area of the selected disks.
These estimates show a maximum absolute difference of 0.94 and 1.92%, respec-
tively, relative to the measured values. The linear measurements on kernels dimen-
sions were subsequently converted into actual values based on the calibration
results. The whiteness of milled rice samples was measured with a cylindrical sample
holder, 57 mm in diameter and 13 mm deep, and taking the image of the open
surface. All measurements on the whiteness of milled rice samples were replicated
three times by filling the sample holder each time to attenuate the random error by
averaging. A white plate supplied with the commercial whiteness meter (model
C-300, Kett Electric Laboratory, Japan) was used for the adjustment of gray level
distribution recorded by the camera. The aperture opening of the CCD camera was
set at a position to obtain the mean gray level (MGL) of 240 for a 252×252 pixels
image of the white plate.
3.3. Determination of HRY
A laboratory rice grader (model TRG 05A, Satake Engineering Co. Ltd., Japan)
was used to separate the head and broken kernels in milled rice samples through
appropriate adjustment of its settings. The HRY was based on the kernel length
equal to or more than 75% of the average length of whole brown rice kernels, as
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–33 25
already defined. It was found that the grader required a minimum of 12 g
milled rice for obtaining reproducible values of HRY differing by less than 1%.
Therefore, representative samples of milled rice weighing about 12 g were
obtained with a specially fabricated sample divider and separated into head and
broken fractions by the laboratory grader for determining HRY. The use of small
test samples was necessary to limit the total number of rice kernels for
subsequent image analysis. In view of the imprecise separation of kernels by the
laboratory grader, all head and broken kernels in their respective fractions were
separated manually with the assistance of imaging system and weighed to
compute the actual HRY of the representative sample. Later all kernels in the
head and broken rice fractions were imaged to extract their dimensional
features, namely, length (L), perimeter (P) and projected area (A). The values of
CDR were then computed for different milled rice samples and related with their
actual HRY to check the applicability of Eq. (1). Finally, the HRY obtained from
the laboratory grader was compared with the actual HRY estimated by image
3.4. Whiteness of milled rice
The whiteness of milled rice samples was first measured with a commercial
whiteness meter (model C-300, Kett Electric Laboratory, Japan) to serve as the
reference values. The rice samples were then imaged in 252×252 pixels size and
analyzed by ImageTool to determine their MGL. The interrelationship between
MGL of 50 milled rice samples along with the white plate and the corresponding
whiteness valuesdetermined bythe
commercial meterwas investigated
4. Results and discussion
4.1. Estimation of head rice yield by image analysis
Dimensional features of the rice kernels were extracted from their respective
images by separating them from the background and identifying each image with a
unique number with the help of the ImageTool. Fig. 2 shows typical images of the
numbered rice kernels used for the measurement of characteristic dimensional
features and the computation of CDR for any selected rice variety. Figs. 3–5
present the relationships between HRY and CDR based on kernel length, perime-
ter, and projected area, respectively. Regression analysis showed the existence of a
power-law relationship between CDR and HRY. Table 2 presents the values of
model parameters estimated by nonlinear regression function of SPSS version 9.05
for Windows with 95% confidence interval in each case. These results validated the
hypothesis that the HRY of milled rice sample could be estimated with root mean
square error (RMSE) of less than 2% from the dimensional features extracted from
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–3326
Fig. 2. Images of rice kernels.
the images of the rice kernels in their natural rest position. The plots of actual and
estimated values of HRY based on length, perimeter and projected area are shown
in Figs. 6–8, respectively. The projected area of kernel images provided the best
estimation of HRY in terms of the RMSE determined from the actual and
estimated values for all ten Thai rice varieties. Also the influence of kernel shape
and size due to differences in rice varieties and the degree of milling could not be
discerned in the development of the composite relationships between HRY and
The separation of milled rice samples by the laboratory grader often resulted in
overlapping head and broken rice fractions. Therefore, the HRY obtained from the
laboratory grader was compared with the HRY estimation based on the measure-
ment of projected area of kernels as shown in Fig. 9. These results confirmed the
discrepancies encountered in the determination of HRY by the laboratory grader in
comparison with manual inspection of kernels by image analysis. These differences
in HRY were possibly due to the imprecise separation of the whole and broken
kernels by the laboratory grader than the more accurate vision-based measure-
ments. The HRY determination by the laboratory grader was influenced by its
operating settings such as the size of indentation in the rotating cylinder and the
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–3327
Fig. 3. Head rice yield as a function of characteristic dimension ratio based on kernel length.
inclination angle of the receiving trough. In this study, the operating conditions of
the grader were identical for all rice varieties. The manual inspection of head and
broken fractions separated by the grader confirmed the presence of rice kernels
belonging to the other group. Also, the proportion of the overlap between the head
and broken fractions depended upon the changes in the dimensional characteristics
of the rice kernels during the milling operation and the differences in rice varieties.
Therefore, the relationships between HRY determined by the grader and image
analysis in Fig. 9 tended to exhibit characteristic differences among rice varieties.
Fig. 4. Head rice yield as a function of characteristic dimension ratio based on kernel perimeter.
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–33 28
Fig. 5. Head rice yield as a function of characteristic dimension ratio based on kernel projected area.
These results further implied that the HRYs estimated by image analysis and the
laboratory grader were indeed related, and could be used for monitoring the milling
operation of different rice varieties.
4.2. Estimation of milled rice whiteness
A plot of gray level distribution in the image (252×252 pixels) of a milled rice
sample is presented in Fig. 10. The values of gray level varied over a range from 70
to 190, depending upon the degree of milling and rice variety. The MGL increased
with the degree of milling of rice samples. However, its range for different rice
varieties when milled for same duration was different. The relationship between the
whiteness of the milled rice samples determined by the commercial meter and MGL
Results of regression analysis for estimating HRY from the dimensional characteristics of kernel
images (Eq. (1))
Characteristic dimension RMSEb(% HRY)
Standard error Regression
Projected area (mm2)
aCorresponding to confidence interval of 95%.
bRMSE (% HRY)=?(HRYact−HRYest)2/N.
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–33 29
Fig. 6. Comparison of actual and estimated HRY based on kernel length.
is shown in Fig. 11. A linear relationship existed between the whiteness meter
reading and MGL for 51 samples of ten Thai rice varieties as follows:
where WR is the whiteness reading from the commercial meter in arbitrary units,
and MGL is the mean gray level for gray value ranging from 0 to 255.
A comparison of the two methods used for estimating the whiteness of milled rice
samples is presented in Fig. 12. These results showed that the estimated values of
milled rice whiteness based on Eq. (2) in terms of MGL were close to the
Fig. 7. Comparison of actual and estimated HRY based on kernel perimeter.
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–3330
Fig. 8. Comparison of actual and estimated HRY based on kernel projected area.
measurements by whiteness meter as indicated by the slope of regression line equal
to 0.998 and R2value of 0.973 for 50 samples.
The proposed technique for estimating milled rice whiteness can be implemented
in a straightforward manner. Recently an electronic milling degree control system
has been introduced in the Satake vertical abrasive whitener (model VTA10AB) to
monitor the whiteness of milled rice continuously and automatically adjust the
position of a counterweight to control the discharge and in turn the pressure inside
the milling chamber (http://www.Satake-usa.com/abrasive.htm). However, the esti-
mation of HRY in a milled rice sample from kernel images will require the
Fig. 9. Relationship between HRY determined by the laboratory grader and image analysis.
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–33 31
Fig. 10. The gray level distribution in an image of a milled rice sample.
positioning of the individual kernels without touching each other prior to extracting
their dimensional features. At present, no information is commercially available for
monitoring the amount of head rice kernels in milled rice. However, a SPY grain
grader, recently introduced by Maztech MicroVision Ltd., Canada makes use of the
physical features of grains and surface discoloration (http://www.maztech.com/
spy.htm). Individual grains approximately ranging from 500 to 900 are picked by
SPY picker and placed on a vacuum-assisted sample holder for extracting their
physical features such as the distribution of kernel sizes by imaging. It seems likely
that similar procedures could be adopted for the inspection of milled rice quality,
based on the techniques described in this paper. The developments of such low-cost
machine vision-based techniques that either enhance or replace currently used
manual methods may pave the way for rapid assessment, and thus better control of
rice milling operations in a conventional setting.
Fig. 11. Relationship between whiteness meter reading and mean gray level of milled rice samples.
B.K. Yada?, V.K. Jindal / Computers and Electronics in Agriculture 33 (2001) 19–33 32
Fig. 12. Comparison of actual and estimated whiteness meter readings for milled rice samples.
Results of this study show that HRY and whiteness of milled rice could be
estimated from two-dimensional images of the milled rice kernels in their natural
rest position. The HRY showed a distinct power-law relationship with the charac-
teristic dimension ratio defined in terms of length, perimeter and projected area of
head and broken rice kernels. However, the estimation of HRY from CDR based
on kernel projected area yielded slightly better results with the lowest RMSE of
1.1% as compared to CDR based on kernel length or perimeter. The degree of
milling was directly related to the MGL estimated from the gray level distribution
in milled rice kernels images expressed in arbitrary whiteness units as measured by
a commercial whiteness meter. The developed empirical relationships for estimating
HRY and milled rice whiteness could be used for regular monitoring and better
control of rice milling operations.
The financial support and laboratory facilities provided by the School of Envi-
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