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Fast Weighing of Pistachio Nuts by Vibration
Sensor Array
Musa Ataş1, Yahya Doğan
2, and İsa Ataş
3
1El-Cezeri Cybernetics & Robotics Laboratory, Siirt University, Siirt, Turkey
2Department of Computer Engineering, Siirt University, Siirt, Turkey
3Department of Electrical and Electronics Engineering, Dicle University, Diyarbakır, Turkey
Email: hakmesyo, yahyadogan72, [email protected]
Abstract—Impact acoustic sound signal is previously used to
discriminate open-shell pistachios from closed ones and for
crack detection purposes. Weight of the pistachio samples
can be utilized as a feature vector for sorting and grading
processes. Nevertheless, traditional weighing procedure is
time consuming. Moreover, efficient fast weighing system
based on impact acoustic signals for pistachio nuts has not
been studied yet. This study aims to discuss the design and
evaluation of a real time fast weighing system for pistachio
nuts. Proposed system can be extended to other agricultural
or industrial products where weight information is critical
as well. In order to eliminate the sensor noise and improve
the signal quality, piezoelectric sensor arrays containing 15
piezoelectric vibration sensors are employed. Final impact
acoustic signal energy is determined by averaging the sensor
array signals.10 pistachio samples with incremented weights
ranging from 0.56 to 1.64 gr are utilized for calibration
process of the sensor array. Extra two heavy objects (4.05
and 5.65 gr) are participated to the calibration set also. In
order to improve accuracy and achieve consistent
measurements repetitive trials approach is adopted.
Excessive repetition of experiments theoretically yields more
accurate and consistent measurements with minimum
standard deviation. Consequently it is observed that 10
times repetition scheme produces satisfactory results with
3% coefficient of variation and 5ms of computational cost
indicates that proposed system can be applicable for fast
weighing of pistachio nuts.
Index Terms—fast weighing, impact acoustic, vibration
sensor, pistachio sorting, piezoelectric sensor, sensor array
I. INTRODUCTION
Measurement is a vital element for all sorts of
scientific researches and disciplines including
engineering, manufacturing and production. Weighing is
a type of measurement which we assess the objects that
we deal with. Moreover, weighing is important for
classification, sorting and grading issues because even
weight parameter itself may be considered as a salient
and discriminative feature. United States standards for
grades of pistachio nuts 51.2544 subsection, identifies the
average weight of the nuts per ounce. Table I lists the
weight limits of grading standards. Note that, gram
conversion was made for the sake of clearness from the
original document [1].
Manuscript received May 25, 2015; revised October 29, 2015.
TABLE I. U.S. STANDARD FOR GRADES OF PISTACHIO NUTS
Size Designations Average number of
nut per ounce
Weight Range
in gram
Colossal <18 >1.58 gr
Extra Large 18-20 1.41-1.58 gr
Large 21-25 1.13-1.35 gr
Medium 26-30 0.95-1.09 gr
Small >30 <0.95 gr
In order to classify pistachio nuts according to the
weights specified in Table I, mechanical sieves have been
established first. However, mechanical sieves/screening
actually only able to classify the nuts according to their
sizes. Although size of the pistachio nuts relates to the
weight information, varied densities and inner structure
of the kernels may also increase the misclassification
rates. Fig. 1 depicts certain pistachio nuts having different
weights with similar sizes. Note that, theoretically
mechanical sieve put them into single group, although in
reality they belong to small, medium and large grade
standards. Due to its prescribed deficiency, mechanical
sieves are not applicable for this problem. As standards
are directly related to the weight parameter, weighing
system should be concerned.
Figure 1. Almost similar size of pistachios having different weights left, middle, right, 0.85, 0.98, 1.21 gram, respectively.
Weight measurement can be handled by traditional or
sensor based approaches such as balance scale, spring
scale, strain gauge and impact acoustic sensors,
respectively. Basic weighing approaches generally
provide accurate results with high precision. But they are
slow and integrating to the grading system is rather
difficult. In order to address the problem fast and efficient
weighing system is proposed in this work.
Reference [2] and [3] proposed development of weigh
in motion system using acoustic emission sensors.
Reference [3] also dealt with wireless capability of the
system and both of them tried to estimate weights of
International Journal of Electronics and Electrical Engineering Vol. 4, No. 4, August 2016
©2016 Int. J. Electron. Electr. Eng. 313doi: 10.18178/ijeee.4.4.313-317
trucks based on road vibration signals. Piezoelectric
vibration sensors can be used for detection of impact
acoustic signals. In the literature various vibration sensor
based systems have been studied. Pearson et al. showed
that by building microphone, Digital Signal Processing
(DSP) device and air rejection nozzle separator system,
almost 97% classification accuracy for open-shell and
closed shell pistachio nuts can be achieved [4]. Other
studies related to the impact acoustic can be read [5]-[7].
For image based sorting systems, Haff et al. studied
sorting of in-shell pistachios from kernels using color
images and achieved 99.9% accuracy for regular in-shell
pistachio from kernels. However for smaller in-shell
pistachio this accuracy rate drops to 85% and 96% for
Discriminant Analysis (DA) and K-Nearest Neighbor
(KNN) approaches, respectively [8]. Ghazanfari et al.
used Fourier descriptors and MLP as features and a
classifier for grading the pistachios into three United
States Department of Agriculture (USDA) size grades
and closed-shell class, respectively. They achieved 94.8%
overall classification accuracy [9]. Another image based
study was conducted by Kouchakzadeh and Adel for
discriminating five different varieties of pistachios and
obtained 99.6% accuracy rate [10]. It should be noted that
both studies performed the classification process in an
off-line manner and generated image dataset was actually
made up of ideal pistachio postures and positions. Thus
for a real time operation classification performance might
be adversely affected due to the challenging cases that
may be arisen from pistachio nuts positions while
dropping.
The objective of this study is to assess the feasibility
and the efficiency of the impact acoustic based pistachio
sorting system that aims to grade pistachios by using
Vibration Sensor Array (VSA) in the real-time manner.
Section II describes detailed information about impact
acoustic signal generation, major components of the
proposed system and feature extraction methods.
Calibration process, experimental results and discussions
are presented in Section III. Consequently, a couple of
concluding remarks and future projections are drawn at
final section.
II. MATERIAL AND METHODS
A. Impact Acoustic Signal
For achieving maximum throughput, pistachio nuts are
released from the elevated position as a free fall
movement under the gravitational force. Systems that use
conveyor belt are usually slower than the aforementioned
approach. When an object hits the material, impact
acoustic sound signals are propagated. Previous studies
[4]-[7] employed those sound signals. Another alternative
is using vibration sensor to acquire impact/hit energy.
Impact energy is proportional to the momentum. As (1)
suggests with nearly constant velocity mass parameter
would be discriminative.
P m v (1)
here P denotes momentum, m is the mass of the object
and v designates velocity. Almost all samples have
similar velocity and can be considered as constant and
therefore velocity difference among different samples can
be ignored. Hence, we can say that P is directly
proportional to the mass of the falling object. Therefore it
is reasonable to use impact energy to measure the mass of
the falling object.
There exist several types of vibration sensors including
piezoelectric accelerometer, velocity sensor, proximity
probes and laser displacement sensors [11]. Due to its
low price, small size and convenient to integrate to setup,
piezoelectric vibration sensor MEAS is preferred. Fig. 2
illustrates MEAS vibration sensor.
Figure 2. A horizontal type the MiniSense 100 from measurement specialties piezoelectric vibration sensor.
Basically sensor produces small AC and large voltage
(up to ±90V) when the film, piezoelectric element, is hit.
It is sensitive enough to capture any small impacts and
can be used for a flexible switch as well. 1MΩ resistor
should be wired to down voltage to the Analog Digital
Converter (ADC) levels.
B. Architecture and Major Components of Proposed
System
Proposed system consists of 15 vibration sensors, 15
1MΩ resistors, 3 Arduino UNO electronic cards, a plexi-
glass pipe with 80 cm length and 2 cm diameter and a
computer. Here Arduino UNO is preferred because
beyond it has low price, it supports analog inputs as well.
Besides, its ADC sampling frequency rate (9600HZ) is
higher than the sensor output frequency (40HZ) which
makes it convenient in terms of Nyquist theorem. VSA
module is made up of five sensors. Fig. 3 demonstrates
the developed VSA module.
Figure 3. A typical VSA module installed on the tube.
As it is seen from the Fig. 3, vibration sensors are
soldered with silicon on the ring so that they can catch the
dropping objects inside the plexi-glass tube. Three VSA
modules are positioned at certain altitude on the pipe.
Approximate distances between VSA modules are 25cm
in general. Each VSA module is wired to the specific
International Journal of Electronics and Electrical Engineering Vol. 4, No. 4, August 2016
©2016 Int. J. Electron. Electr. Eng. 314
Arduino UNO card. In this way, real-time parallel
processing can be handled by the simple Arduino code.
Code snippets of the Arduino are available below.
int thr=30;
int limit=50;
float t=0;
void setup()
Serial.begin(9600);
void loop()
int s1=analogRead(A0);
int s2=analogRead(A1);
int s3=analogRead(A2);
int s4=analogRead(A3);
int s5=analogRead(A4);
int signal=(s1+s2+s3+s4+s5)/5;
if (signal_1>thr)
for (int i=0;i<limit;i++)
signal =(analogRead(A0)+analogRead(A1)+
analogRead(A2)+analogRead(A3)+
analogRead(A4))/5;
t+= signal;
Serial.println(t);
t=0;
Figure 4. Captured impact signals from the VSA module as a time
series data.
Similarly, Fig. 4 shows digitized impact energy of hit
samples to the VSA module. Note that, Arduino UNO has
10 bit ADC and can produce maximum 0-1023 positive
values. With VSA module, sensor noise is suppressed by
averaging the signal as well. As (2) indicates, total
amount of energy is utilized as a feature in this study.
5
, ,3 50
1
1 1 5
3
i j k
k
i j
S
TE
(2)
here, TE and S denote the total energy and signal value
(amplitude) of ADC, respectively. For each VSA module,
signals of sensors are averaged and then summed up. As
we have three modules on the system, energies of VSA
modules are averaged to get the overall impact energy
that we utilize it as a feature vector in this study. Please
note that, there is no curse of dimensionality problem
here because only single input simplifies the developed
algorithm along with the system can be accounted as a
satisfactory confidence level.
III. EXPERIMENTAL RESULTS
A. Calibration Process
In order to produce consistent and reliable results
calibration process should be carried out. Developed
system may produce different results according to the
shape of the object and dropping conditions. That is, if
object is very small in size it may escape the VSA
module which may yields false reading. Similarly
sometimes object hit some sensors directly and remaining
are affected weakly from this impact. As a result there
may be slight divergence from the ideal read. In order to
address this particular problem we repeat the process and
then average them. In this study, we investigate the
optimum number of repetition under the consideration of
processing speed. To do that, 10 pistachio samples with
incremented weights range between 0.56 to 1.64 gr are
utilized for building calibration set. Extra two heavy
objects (4.05 and 5.65 gr) are participated to the
calibration set also. Fig. 5 depicts those samples.
Figure 5. Samples used in calibration process.
Each calibration sample is employed for one repetition
to ten repetition incrementally. Mean and standard
deviation are calculated to obtain percent coefficient of
variation (%CV) that we think it is more representative
than other statistics. Equation (3) shows the percent
coefficient of variation formula.
% 100CV
(3)
Table II demonstrates influence of the repetition on the
sensor measurement with respect to the percent
coefficient of correlation values. Similarly, improvement
on measurement can also be seen Fig. 6 and Fig. 7 as
each calibration samples and mean value, respectively.
International Journal of Electronics and Electrical Engineering Vol. 4, No. 4, August 2016
©2016 Int. J. Electron. Electr. Eng. 315
TABLE II. EFFECT OF REPETITION ON %CV
1x 2x 3x 4x 5x 6x 7x 8x 9x 10x
0,56 gr 22 13 11 9 9 7 6 6 4 4
0,66 gr 15 13 9 8 7 7 5 4 4 4
0,78 gr 18 9 7 7 6 6 6 5 4 4
0,90 gr 10 9 7 5 5 5 5 5 4 4
1,03 gr 18 15 10 10 7 7 6 5 5 4
1,15 gr 14 8 7 6 5 4 4 4 4 3
1,27 gr 13 12 10 9 8 6 5 4 5 5
1,40 gr 16 13 10 9 8 8 7 6 6 5
1,55 gr 6 5 4 3 3 3 2 2 2 2
1,64 gr 9 6 5 4 4 3 2 2 2 2
4,05 gr 3 3 1 2 2 1 1 2 1 1
5,65 gr 8 5 7 6 5 4 4 3 3 3
Average 13 9 7 6 6 5 4 4 4 3
Figure 6. Repetition vs %CV for each calibration sample.
Figure 7. Average repetition vs %CV line.
Table II, Fig. 6 and Fig. 7 show that as number of
repetition increase, mean value of sensor arrays are
become more consistent and reliable because standard
deviation and %CV value become smaller and smaller.
IV. CONCLUSIONS
Main objective of this study is to discuss the design
and evaluation of a real time fast weighing system for
pistachio nuts. Proposed system can be extended to other
agricultural or industrial products as well. In order to
achieve better Signal to Noise Ratio (SNR), piezoelectric
sensor arrays containing 15 piezoelectric vibration
sensors were utilized. Total impact acoustic signal energy
was determined by averaging the sensor array signals. 10
pistachio samples with incremented weights ranging from
0.56 to 1.64 gr were utilized for calibration process of the
sensor array. Extra two heavy objects (4.05 and 5.65 gr)
were participated to the calibration set also. In order to
improve accuracy and achieve consistent measurements
repetitive trials scheme was adopted. Experiments
revealed that 10 times repetition scheme produces
satisfactory results with 3% coefficient of variation and
5ms of computational cost indicates that proposed system
can be applicable for fast weighing of pistachio nuts. In
the future, it is intended to predict weight correction
factor of the proposed system for weighing in gram scale.
Detailed test will be performed to determine generalized
performance of the developed system.
ACKNOWLEDGMENT
This study was funded by the Scientific and
Technological Research Council of Turkey (TÜBİTAK)
under grant no. 113E620. Special thanks to the El-Cezeri
laboratory stuff Muhammed Said Ataş for his valuable
efforts on conducting repetitive and exhaustive
experiments.
REFERENCES
[1] U.S.D.A., “U.S. standards for grades of pistachio nuts in the shell,”
Technical Report, 2004.
[2] J. M. Bowie, “Development of a weigh-in-motion system using acoustic emission sensors,” Ph.D. dissertation, Dept. Civil, Env.
and Constr. Eng., Univ. of Central Florida, Orlando Florida, 2011.
[3] R. Bajwa, “Wireless weigh-in-motion: using road vibrations to estimate truck weights,” Ph.D. dissertation, Dept. Elect. Eng. and
Computer Science, Univ. of California, Berkeley, 2013.
[4] T. C. Pearson, “Detection of pistachio nuts with closed shells using impact acoustics,” Applied Engineering in Agriculture, vol.
17, no. 2, pp. 249-253, 2001.
[5] A. E. Cetin, T. C. Pearson, and A. H. Tewfik, “Classification of closed- and open-shell pistachio nuts using voice-recognition
technology,” Transactions American Society of Agricultural
Engineers, vol. 47, no. 2, pp. 659-664, 2004. [6] H. Kalkan, N. F. Ince, A. H. Tewfik, Y. Yardimci, and T. C.
Pearson, “Classification of hazelnut kernels by using impact
acoustic time-frequency patterns,” EURASIP Journal on Advances in Signal Processing, vol. 2008, January 2008.
[7] T. Pearson and N. Toyofuku, “Automated sorting of pistachio nuts
with closed shells,” Applied Engineering in Agriculture, vol. 16, no. 1, pp. 91-94, 2000.
[8] R. P. Haff, T. C. Pearson, and N. Toyofuku, “Sorting of in-shell
pistachio nuts from kernels using color imaging,” Applied Engineering in Agriculture, vol. 26, no. 4, pp. 633-638, 2010.
[9] A. Ghazanfari, J. Irudayaraj, A. Kusalik, and M. Romaniuk,
“Machine vision grading of pistachio nuts using Fourier descriptors,” Journal of Agricultural Engineering Research, vol.
68, no. 3, pp. 247-252, 1997.
[10] A. Kouchakzadeh and B. Adel, “Discrimination of pistachios varieties with neural network using some physical characteristic,”
International Journal of Emerging Sciences, vol. 2, no. 2, pp. 259-
267, 2012. [11] H. N. Norton, Handbook of Transducer, Prentice Hall, 1989, ch.
5-7.
Musa Ataş is an Assistant Professor in the
Department of Computer Engineering at the University of Siirt where he has been a faculty
member since 2012. He is a founder and
principal coordinator of the El-Cezeri Cybernetics and Robotic Laboratory.
Musa completed his undergraduate, MS and
Ph.D. at Middle East Technical University/ Turkey. His research interests lie in the area of
artificial intelligence, autonomous systems,
machine and computer vision, machine learning, robotics, virtual reality
International Journal of Electronics and Electrical Engineering Vol. 4, No. 4, August 2016
©2016 Int. J. Electron. Electr. Eng. 316
and programming languages specifically domain specific languages as Open Cezeri Library framework ranging from theory to design to
implementation, with a focus on improving software quality. In recent
years, he has focused on machine vision systems and impact acoustic. He has collaborated actively with researchers in several other
disciplines of computer science, agricultural and food science. Currently,
he conducts two projects, classification of pistachio nuts by machine vision and aflatoxin detection in pistachio nuts by hyper spectral
imaging and machine vision, respectively.
Musa has served on roughly ten conference and workshop program committees both national and international.
Yahya Doğan works as an assistant and he is
a graduated student in the Department of
Computer Engineering at the University of Siirt where he has been a faculty member
since 2012. He is a co-founder and stuff of the
El-Cezeri Cybernetics and Robotic Laboratory. Yahya completed his undergraduate at
Sakarya University. Currently his MS is at
Fırat University, Turkey. His research interests lie in the area of machine vision,
machine learning. In recent years, he has focused on industrial cameras. His MS thesis is related to the prediction of ideal exposure time of
industrial cameras for multispectral/hyper spectral imaging and machine
vision. Yahya has served on roughly five conference and workshop program
committees both national and international.
İsa Ataş works as a lecturer and he is a Ph.D.
student in the Department of Electrical and Electronics Engineering at the University of
Dicle where he has been a faculty member
since 2006. İsa completed his undergraduate and MS at
Dicle University with Electrical and
Electronics Engineering. Currently his Ph.D. is at Dicle University / Turkey too. His
research interests lie in the area of machine
learning and patch antenna design. In recent years, he has focused on design of patch antenna. His Ph.D. thesis is related to the design and
implementation of high gain aperture coupled microstrip patch antenna.
İsa has served on roughly five conference and workshop program committees both national and international.
International Journal of Electronics and Electrical Engineering Vol. 4, No. 4, August 2016
©2016 Int. J. Electron. Electr. Eng. 317