Shiraz University

Shiraz, Iran

Department Of Mechanics of Agricultural Machiner EngineeringDepartment Of Mechanics of Agricultural Machinery Engineering

NONDESTRUCTIVE ESTIMATION OF RELATIVE CONTENTS OF CHICKEN EGG BY MACHINE VISION AND

NEURAL NETWORK TECHNIQUESNEURAL NETWORK TECHNIQUES

M. H. RAOUFAT

V. ASADI

Egg-producing countries worldwide show a growing trend.Egg world production in 2005 was more than 64,394,000tons and in 2006 showed an increase 1/7 percentage with65,500,000 tones.Iran having the fifteenth rank global and fifth in Asian,b f l b l d i fabout one percent of global production of eggs.

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tIntro

Egg is a rich source of protein, nutrients (sodium,potassium, calcium, phosphorus, magnesium, iron, zinc,po ass u , ca c u , p osp o us, ag es u , o , c,copper, iodine, chlorine, sulphur and selenium) andvitamins (A, B1, B2, B3, B6, B12, D and E). Egg( , , , , , , ) ggcontains 65-70% moisture, 11-12.5% protein and 9.5-10.8% fat. Investigations have show that the protein ing pthe egg has a high nutrient value. This worthy nutritiveegg is capable of developing embryo and chicken baby.gg p p g y y

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Nutrient content of an eggtion

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Egg white and egg yolk are extensively utilised asingredients because of their unique functionalproperties, such as gelling and foaming. Foams areused in the food industry for the production ofbread, cakes, crackers, ice creams, etc. Hen egg yolkhas good emulsifying properties.

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Objectives:

To develop and evaluate a number of algorithms for To develop and evaluate a number of algorithms for estimating egg volume

To develop an algorithm for estimating relative contents of eggcontents of egg

To investigate the effects of storage time  temperature To investigate the effects of storage time, temperature and moisture on relative contents of egg

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Literature review

Application of machine vision in agricultural engineering:

Quality control and grading of various products(Kondo et al. 2000 & Blasco et al. 2003)

Automatic guidance of agricultural machinery

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(Fehr & Gerish 1995. Brandon 1992, Gerish et al. 1997)

A i h i f d

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t Automatic harvesting of products(Fitzpatrick et al. 1997 & Benson et al. 2003)

Intro

Forbes, K. (2000)

Forbes (2000) used radial photo images  to Forbes (2000) used radial photo images  to estimate surface and volume of apple, strawberry and tomato and quanta lope  The pictures were and tomato and quanta lope. The pictures were captured using mirrors installed at 45o around fruit  Then pictures were used to establish a three fruit. Then pictures were used to establish a three dimensional representation of the fruit, the data were used to predict surface and volume of the were used to predict surface and volume of the fruit using neural network technique. Although the method was accurate but it was time ti

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the method was accurate but it was time consuming  and therefore not suitable for real‐time applicationsoduc

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time applications.

Intro

Lee, D.  J., Lane, R. M. and Chang, G. H. (2004)

L   t  l ( )  d l  b  t  d t t    Lee et al (2004) used laser beam to detect any  surface unevenness in the muscles and to establish  th  di i l i   f th   l  Th  a three dimensional image of the muscles. The image could be used to estimate the volume.

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tIntro

Diaz, R., Gil, L., Serrano, C., Blasco, M., Molto, E. and Blasco, J. (2004).

Diaz et al (2004) used combination of image processing and neural network  techniques for processing and neural network  techniques for grading of table olive. The image processing could identify the bruised areas on the fruit surface and identify the bruised areas on the fruit surface and give information on bruised area and color intensity. The data were then fed to a neural network. The The data were then fed to a neural network. The network could separate the fruits into four distinct  groups different in quality.ti

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groups different in quality.

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Blasco, J. Aleixos, N. and Moltó, E. (2007).

Blasco et al (2007) developed a region oriented ( 7) p gsegmentation algorithm to detect orange fruit surface bruise and also to differentiate fruit bruise from  calyx. The algorithm was based on recognition of localized color intensities and was not merely ybased on single pixel color.

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tIntro

Studies on quality grading of egg can be classified into two  groups:1. Those based on apparent defects and2. Those based on internal defects

The studies have  been mainly conducted  by adopting image processing techniques and sound ti

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wave propagation.

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Das, K. and M. D. Evans. (1992).

Das and Evans (1992) used the image processing techniques to separate fertile from unfertile eggs. They captured images f    d i   h  i b  f     d   d  Hi  of eggs stored in the incubator for 3 and 4 days. Histogram 

enhancement  could segregate the samples  into two groups; fertile and unfertile  The accuracy for 3 and 4 days stored fertile and unfertile. The accuracy for 3 and 4 days stored samples were 89 and 98 %, respectively.They also trained a neural network system to analyze the y y yhistograms. They divided the histograms into 8 regions and considered the pixel intensities as network inputs. They 

d    i     f    d  % f   h     d   tion

reported a sorting accuracy of 93.5 and 93.9% for the 3 and 4 days stored eggs, respectively.

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tIntro

Patel, V. C., Mcclendon, R. W. and Gooddru, J. W. (1994), (1998)

Patel et al. developed a color computer visionbi d ith l t k t fcombined with a neural network system for

detection of eggs with defect. They joined threehi t f h d f d hi thistograms of each egg and formed a histogramwith 768 cells. Each cell was considered as an inputt th l t k Th t bl fto the neural network. The system was capable fordirt stain detection with 97.8% accuracy, 91.1%

f bl d t d t ti d 96 7%tion

accuracy for blood spot detection and 96.7%accuracy for crack detection.

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Jindal, V. K.,  Sritham,  E.,  (2003).

Cho et al (2000) used the sound wave propagation h l d h h ll k htechnology to detect the egg shell cracks. The system 

accuracy was 95% and could detect cracks at the rate f f hof 200 ms for each egg.

Jindal et al (2003) continued the study and could d h h ll k h hdetect the shell cracks at higher accuracy.

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tIntro

Nakano, K., Motonaga, Y. (2003).

Nakona and Motonaga (2003) used the ultra i l   d NIRS   d   h     i h bl d violet and NIRS to detect the eggs with blood spots . The blood  could interact with the wave  hi h  ld b   d    i   f  bl d   Th  which could be used as sign of  blood spots. The 

system could detect the healthy eggs with 100% d  h     i h bl d    i h  6 8% and the eggs with blood spots with 96.8% 

accuracy.

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tIntro

Evaluation of egg freshness

Estimating egg quality (Freshness):(Freshness):

Destructive techniques

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Destructive techniques

Non‐destructive techniques

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Intro

Destructive techniques for egg freshness evaluation

Destructive methods:Haugh UnitThe most widely method for estimating egg quality is the Haugh Unit whichis calculated based on the egg weight and the albumen height of a brokengg g gegg.Air cell heightAir cell height is the only quantitative egg freshness parameter consideredAir cell height, is the only quantitative egg freshness parameter consideredby the European Union regulation. Theoretically, a grade A egg at packaginghas to keep the characteristics of its grade (air cell height <6 mm) up toexpiring dateexpiring datepH of albumenThe pH of albumen from a newly laid egg is between 7.6 and 8.5. Duringh f h ll h H f lb iti

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the storage of shell eggs, the pH of albumen increases at a temperature-dependent rate to a maximum value of about 9.7.

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Nondestructive techniques for egg freshness evaluation

Non‐destructive methods:

Berardinelli et al (2005) used the infrared waves to evaluate the albumin height, this method could estimate the height up to 1mm±1mm

Abunajmi et al (2010) used the ultraviolet waves to estimate the freshness (age) of eggs. They stored 300 estimate the freshness (age) of eggs. They stored 300 eggs at 4 and 300 eggs at 25 oC for 6 weeks. The study revealed that as the egg freshness decreases, the speed ti

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of wave propagation through egg decreases too.`

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Development of Image Acquisition system (Non-destructive approach).

Main parts:light source main compartment egg holder and cameraod

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light source, main compartment, egg holder and camera.For non-destructive observation of internal parts of egg a highintensity light beam is necessary to penetrate into egg membrane m

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y g y p ggthrough its longer axis. In this system a halogen bulb with yellowlight was used. light source was placed inside the chamber to

f li h i h di i h ld i h and m

prevent scatter of light in other directions. Egg holder in themain compartment was a horizontal plane with a hole 5 mm india through which light beam pass and penetrate into eggrials a

dia. through which light beam pass and penetrate into egg.

Mate

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 and m

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Egg storage conditions

Temperature levels5 oc202030

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 metho

 and m

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Relative humidity levels, %

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 metho

 and m

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Estimating egg volumeod

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 and m

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Egg volume calculation methods

1.  Ellipsoid approximation

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  S   f di k  l t metho

2.  Sum of disk elements

 and m

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Spiegel, M., Lipschutz, S. and  Liu, J. (2008) 

Mate

f l ( )3. Sum of cone elements (SCE)

Flowchart for egg content volume estimationod

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 and m

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Flowchart for estimating egg contents volumevolume

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 metho

 and m

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Artificial Neural Networkod

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Artificial Neural Networkod

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Laboratory examinations

Egg volume measurement. Three mentioned methods were used to estimate egg volume. Control measurement was conducted in the lab.s

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Egg components volume measurement. Sum of disk element method was used to estimate d 

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component volumes using two images for each data point.

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RESULTS & DISCUSSIONRESULTS & DISCUSSION

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Evaluation of egg volume algorithms

Diff  b t   i   th d  Difference between various methods 

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Effect of number of views on estimated volume

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Difference between calculated volume and estimated volume

EV: ellipsoid method,SDE: sum of disk elements,io

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SCE: sum of cone elements,

MV: measured volume

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Performance of algorithmsion

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Bland–Altman plot for the comparison of measured and ti t d l t li i di t th 95% li it festimated volumes outer lines indicate the 95% limits of

agreement.SDE2SDE2

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Evaluation of the algorithm for estimating contents Evaluation of the algorithm for estimating contents volume

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Air cell Albumen

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yolk

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Investigate the effect of time, moisture and content on content egg

Change of air cell volume during storage time

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sind dis Moisture 11% Moisture 32%

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Moisture 52%

Change of albumen volume during storage time

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Moist re 11%

nd dis Moisture 11% Moisture 32%

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Moisture 52%

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Change of yolk volume during storage time

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Moist re 11%

nd dis Moisture 11% Moisture 32%

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Moisture 52%

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Conclusions

11. No significant difference was noticed among h l i i h d l h h hthree volume estimating methods, although the SDE could predict egg volume more accurately.

12. Use of two views for image analyses1Of h l i ld t 1Of each egg sample yields more accurate

results although not necessarily in a significant manner

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Ad i   l   ifi i   d  l 3‐ Adopting color specifications and Neural network technique can help  separate three regions i id      ( lk   lb   d  i   ll)  Th  inside an egg (yolk, albumen and air cell). The volumes can be estimated using SDE method.

4. As egg life and air temperature increases, the air ll  l  i    Si il  i  i   i  cell volume increases too. Similar increase in air 

cell volume is noticed for decrease in humidity. Y lk  l   d  lb  d     h   i  Yolk volume and albumen decreases as the air temperature increases. These findings are i di i   f   f h

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indications of egg freshness.

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