fast weighing of pistachio nuts by vibration sensor array · 2016-08-31 · fast weighing of...

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


©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 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.



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



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.



fast weighing of pistachio nuts by vibration sensor array · 2016-08-31 · fast weighing of...

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