The identification of white fertile eggs prior to incubation based on machine vision and least square support vector machine

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1 Afrcan Journal of Agrcultural Research Vol. 6(1), pp , 18 June, 011 Avalable onlne at DOI: /AJAR ISSN X 011 Academc Journals Full Length Research Paper The dentfcaton of whte fertle eggs pror to ncubaton based on machne vson and least square support vector machne Zhhu Zhu 1, and Mehu Ma 1,3 * 1 Natonal R&D Center for Egg Processng, Huazhong Agrcultural Unversty, Wuhan, Hube , Chna. College of Engneerng, Huazhong Agrcultural Unversty, Wuhan, Hube , Chna. 3 College of Food Scence and Technology, Huazhong Agrcultural Unversty, Wuhan, Hube , Chna. Accepted 19 May, 011 The ablty to automatcally dentfyng fertle eggs pror to ncubaton would allow tmely removal of the nfertle eggs, whch could brng hgh profts to hatcheres wth better chck qualty and lower pathogen contamnaton of chcks. A method based on machne vson and least square support vector machne (LS-SVM) for fertle eggs dentfcaton pror to ncubaton was proposed. Dgtal mages were acqured by hgh-resoluton dgtal cameras wth cold lght back llumnaton, and egg shapes (e.g. egg shape ndex, roundness, elongaton, geometrc moment) and color mean nformaton of the egg yolk regon such as hue (H), ntensty (I), saturaton (S) from mage characters were extracted. LS-SVM algorthm was used to establsh fertle egg classfcaton model from nfertle eggs. The test results obtaned from the 40 testng sets showed that the best classfcaton accuracy was 9.5%. Wth usng a same data set, the performance comparson between LS-SVM classfer wth dfferent kernel functons and the other dfferent classfers was conducted. Compared wth other kernels, LS-SVM classfer wth radus bass functonal (RBF) kernel was found to obtan the best accuracy and provde better accuracy, hgher speed compared wth support vector machne (SVM) and back-propagaton (BP) artfcal neural networks classfer. Key words: Fertle egg, dentfcaton, machne vson, least square support vector machne, pror to ncubaton. INTRODUCTION In the egg ndustry, hatchng egg selecton s the manly drect nfluencng factor of hatchng effect. Qualty of hatchng eggs s drectly related to the young brds hatchng rate, survval rate and poultry qualty. Hatchery statstcs show that about 8 to 9% of all ncubated eggs do not hatch due to egg nfertlty (Das and Evans, 199a). Infertle eggs detecton pror to ncubaton s one of the dffcult problems n the hatchery ndustry, whch has no reasonable soluton so far. In practcal applcatons, candlng eggs at 7 to 1 days of ncubaton are always used, but the breakout nfertle eggs have lost edble value and errors n candlng often occur at ths tme. The automated detecton of fertle eggs and nfertle eggs pror to ncubaton can lead to tmely removal, *Correspondng author. E-mal: mamehuhn@yahoo.com.cn Tel: Fax: optmzng space and labor, avodng contamnatng of other eggs and brngng better profts to hatcheres. In recent years, many methods of nondestructve detecton of fertle egg have been proposed n the techncal lterature. Das and Evans (199b) detected fertle embryo wth machne vson and neural network, the accuracy s 93% at day 3 and 4 of the ncubaton, but only 67% at day of the ncubaton. Yu et al. (007) detected fertlty of hatchng eggs automatcally by machne vson and mproved PSO neural network system. Bamels et al. (00) used two lght wavelengths to detect embryo development at 4.5 to 5 days of ncubaton. There were many new methods of detectng egg fertlty or montorng embryo development such as acoustc resonance frequency (Coucke et al., 1997), magnetc resonance magng (Klen et al., 00), hgh frequency ultrasound magng (Schellpfeffer et al., 005), and hyperspectral magng (Lawrence et al., 006; Smth et al., 008). Former reports showed that efforts have

2 700 Afr. J. Agrc. Res. Fgure 1. Machne vson magng system. been made to detect egg fertlty durng mddle and late stage of ncubaton. Few reports focused on the perod pror to ncubaton showed poor result n accuracy. In ths study, usng the machne vson acqured egg mages and evaluatng egg shapes and hue (H), ntensty (I), saturaton (S) color nformaton of egg yolk regon as characterstc parameters, novel classfcaton model for fertlty and nfertlty was establshed n order to obtan better accuracy and effcency. MATERIALS AND METHODS Egg samples One hundred eggs ncludng 60 fertle eggs and 40 nfertle eggs were collected from Huazhong Agrcultural Unversty Hatchery wthn one week. These eggs were obtaned from 45 week old Sngle Comb Whte Leghorn chckens. After numbered and maged, the eggs were placed nto the ncubator at 38.5 C and 65% relatve humdty. At day 6, the eggs were candled and broken out to assess vsually for fertlty or nfertlty. We randomly chose, for each class, 60% of the subset to buld the tranng set (36 fertle eggs and 4 nfertle eggs) and the remanng 40% was put asde for testng set (4 fertle eggs and 16 nfertle eggs). Three replcates of each treatment were performed; correspondng tranng set and testng set were obtaned. The average of 3 tmes results was regarded as the fnal result. shown n Fgure a. In order to obtan whole egg regon, consderng background of the eggs mage was black, mage processng (Gonzalez, 008) was appled as followng: to convert the color mage nto gray scale mage (Fgure b); to use bnary method for gray scale mage (Fgure c); to use Gaussan flter to smooth bnary mage (Fgure d). Thus, the whole egg regon was clearly separated. The pxel area and length of the whole egg regon could be easly calculated and measured. Accordng to prelmnary research results of Research Group (Wang et al., 009), the separatng method of the egg regon from the orgnal pcture was processed as follows: frstly, to convert the color mage nto gray scale mage (Fgure (e)); the yolk regon could be more clearly apparent by gray balance processng (Fgure f). However, there were a lot of nose ponts and the pxel value was not stable enough after gray balanced mage. For ths reason, medan flter wth 3 3 template and Gaussan smoothng were used for denosng and keepng stable (Fgure g). From Fgure g, egg yolk was clearly to be seen, but separaton of egg yolk mage could not be well obtaned by threshold method. Here, hybrd of color reverse (Fgure h) and And algorthm was appled to obtan egg yolk regon. Fgure was the result of Fgure h And Fgure d. Eroson played the role of removng the object boundary ponts n mathematcal morphology. Eroson wth 3 3 structure elements was appled to the result of And (Fgure j). Then removng the boundary of egg mage was conducted (Fgure k) by Fgure h And Fgure j. Lastly, the egg yolk regon was extracted by auto threshold method (Fgure l). Therefore, the coordnates of the egg yolk regon were obtaned by the bnary mage, and then H, I, S color nformaton of egg yolk regon could be calculated n orgnal color space. Imagng system The machne vson magng system used to acqure egg mages was shown n Fgure 1. It was conssted of a lght source, a lghtgatherng tube, a dark room, a Canon EOS 550D dgtal camera, and a computer. The lght source provded an llumnaton of 7700 lux at the pont where the egg was placed. A sngle 150W, 4V-DC tungsten-halogen lamp was served as lght source. All mages n ths study were acqured by usng the magng system. Image processng The orgnal egg mage obtaned by the machne vson system was Feature extracton In general, there were mnute dfferences n shapes between fertle eggs and nfertle eggs. Infertle eggs may be more crcular, whereas fertle eggs may be long and thn (Tanguch, 007). Therefore, some shape parameters were extracted as character parameters. And there were dfferences n color nformaton of transmsson between fertle eggs and nfertle eggs. Thus, color nformaton of egg yolk was also extracted as character parameters. In the research, permeter, area, major axs and mnor axs of egg mages were measured. Calculaton of those parameters was explaned n detaled by Zhou et al. (007). To reduce error, some shape parameters were defned as followng:

3 Zhu and Ma 701 (a) Orgnal mage (b) Gray scale mage (c) Bnary mage (d) Gaussan smoothng (e) G component of (f) Gray balance (g) Medan flter and (h) Color reverse mage Gaussan smoothng the grayscale mage () The result of (d) (j) Eroson of the egg (k) The result of (l) Egg yolk "And"(h) algorthm (h)"and"(j) algorthm Fgure. Egg mage processng. Egg shape ndex (SI): t s defned as: SI a = (1) b where, a s pxel major axs and b s pxel mnor axs. Roundness ( D ): Roundness s used to characterze the R complexty of the object boundary. The closer to round shape of the egg, the greater the degree of ts crcular. The mathematcal expresson s as followng: characterstcs of the mage (Ramteke, 010). Invarant moments p + q th order twodmensonal geometrc central moments are denoted by whch s expressed as: pq ( ) µ pq, p q ( x x ) ( y y ) f x, y dxdy p, q 0 0 0,1,,... (4) S µ = = where, s the regon of pxel space n whch the mage ntensty functon f ( x, y ) s defned. ( x0, y 0) s the mage ntensty centrod. Seven moment nvarants are gven below: 4πA D R P = () where, A s pxel area and P s pxel permeter. Elongaton ( E ): Elongaton descrbed the slender nature of the egg. The slender the egg, the smaller the elongaton value. Elongaton s defned as followng: E b = (3) A where, A s pxel area and b s pxel mnor axs. And, moment features wth scale, translaton and rotatng nvarance can be easly expressed and analyzed n the regon are selected as character parameters. ( ) ϕ = η + η ϕ = ( η η ) + 4η ( ) = ( 3 ) ϕ η η η η ( ) = ( + ) ϕ η η η η ( ) ( ) ( ) ( 3η η )( η η ) 3( η η ) ( η η ) ( ) ( ) ( η )( η + η ) ϕ = ( η 3 η ) η + η η + η η + η ϕ = ( η η ) η + η η + η η η ( ) ( ) ( ) ϕ = (3 η η ) η + η η + η η + η ( 3η η )( η η ) 3( η η ) ( η η ) (5)

4 70 Afr. J. Agrc. Res. µ pq where, η pq t µ 00 t = ( p + q) / + 1, p + q =,3,... = s standardzed central moment. Therefore, there are 13 parameters used to form the feature vector. It s defned as: G =[ SI, D, E, ϕ, ϕ, ϕ, ϕ, ϕ, ϕ, ϕ, H,S,I] T (6) R where, α are Lagrange multplers. k( x, x ) s kernel functon. There are many forms of kernel functons such as lnear kernel, the polynomal kernel, radus bass functonal (RBF) kernel. In the paper, we have used RBF kernel functon as followng: k( x, x ) = exp( x x / σ ) (11) where, σ s a kernel functon parameter. Least square support vector machne (LS-SVM) classfer To mprove classfcaton accuracy, an artfcal ntellgence method was selected as classfer. Support vector machne (SVM) s a new machne learnng technque based on the statstcal learnng theory, whch can avod the problems of over learnng, dmenson dsaster and local mnmum n the classcal study method (Vapnk,1995). SVM s characterzed by a (convex) quadratc programmng (QP) problem. LS-SVM, evolved from the SVM, translates the quadratc optmzaton problem nto that of solvng lnear equaton set (Suykens and Vandewalle, 1999). Compared wth SVM, LS-SVM s a much smpler algorthm wth hgher operaton speed, whch s wdely appled to pattern recognton and nonlnear regresson. Consequently, LS-SVM was selected as classfer n the paper. A smple bnary classfcaton problem s gven as follows: Consder a gven tranng set {( x, y ), 1,,..., l} data x = wth nput n R and correspondng bnary class output y +, and the classfer takes the followng form: labels { 1, 1} y = sgn[ ω T ϕ( x) + b] (7) n nh where nonlnear functon ϕ( ) : R R s the mappng the nput space to the hgh dmensonal and potentally nfnte dmensonal feature space. ω s the weght vector and b s a bas term. In the prmal weght space of LS-SVM, the optmzaton problem can be expressed as followng: l 1 T γ mn ω ω + e (8) = 1 Subject to the equalty constrants: y [ ω T ϕ ( x ) + b] = 1 e = 1,,..., l (9) e are slack varables and γ s a postve real constant. A lnear equatons can be obtaned by ntroducng the Lagrangan where, functon and the correspondng condton of Karush-Kuhn-Tucker (KKT). The optmum parameters of the model can be found by solvng the set of lnear equatons. Fnally, the LS-SVM classfer s constructed as follows: l α (10) = 1 y ( x ) = sgn[ y k ( x, x ) + b] RESULTS AND DISCUSSION The mage samples used n the experment were conssted of 100 egg mages, ncludng 60 fertle egg mages and 40 nfertle egg mages. The above mentoned feature vectors G were obtaned usng MATLAB7.0 (the Math Works, Natck, Massachusetts, USA) for each mage wth sze to buld the feature data set, ncludng fertle egg subset and nfertle egg subset. The freely avalable LS-SVM toolbox (LS- SVM v.1.5, Suykens, Leuven, Belgum) was appled wth Matlab to develop the LS-SVM classfcaton models. σ The hyperparameter γ and the kernel parameter were obtaned n condton that the total error was mnmum: γ = 000 and σ = 0.5. Thus, the two parameters were used n all experments. The correct dentfcaton accuracy was gven by the correct dentfed numbers ncludng the fertle eggs and the nfertle eggs dvded by the total sample numbers. Performance of the bnary LS-SVM classfer In order to analyze the performance of the bnary LS- SVM classfer, dfferent kernel functons and dfferent classfers were appled to same samples set n the research. Identfcaton wth dfferent kernel functon Kernel functon plays a decsve role n the performance of the LS-SVM classfer (Vapnk, 1999). Three LS-SVM classfers respectvely based on the lnear kernel, the polynomal kernel, and the RBF kernel were proposed. The classfcaton accuraces wth lnear, polynomal and RBF kernel functons were shown n Table 1. As shown n Table 1, the lnear kernel performed worst, the lnear kernels performed a bt better, and RBF kernel acheved the best accuracy of 99.1 and 9.5% n tranng set and testng set respectvely. RBF kernel was used to map the orgnal non-lnear feature space to hgh dmenson space, whch ftted well wth the dea of SVM. Therefore, RBF kernel was selected n the paper.

5 Zhu and Ma 703 Table 1. Classfcaton results based on LS-SVM classfer wth dfferent kernel functon. Kernel functon Tranng accuracy (%) Testng accuracy (%) Lnear Polynomal RBF Table. Comparson of the classfcaton ndces wth dfferent classfer. Classfer Tranng tme (s) Testng tme (s) Classfcaton accuracy (%) LS-SVM SVM BP Table 3. Identfcaton accuracy of fertle eggs based on LS-SVM wth RBF kernel. Egg type Rght rato Mstake rato Fertle egg /4 91.7% /4 8.3% Infertle egg 15/ % 1/16 6.% Total 37/40 9.5% 3/40 7.5% Comparng classfcaton results usng dfferent classfers For comparson purposes, classfcaton results usng SVM and back-propagaton (BP) wth the same data set were obtaned. The comparng results of the three classfers were shown n Table. The results show the performance of BP classfer was worst, wth the lowest correct classfcaton rate of 87.5%, longest tranng tme and testng tme, LS-SVM classfer and SVM classfer acheved the best performances, wth the same correct classfcaton accuracy of 9.5%, whle the computatonal speed of LS-SVM classfer was hgher than that of SVM classfer. In ths sense, the proposed LS-SVM classfer performed better than the standard SVM classfer and BP classfer. LS-SVM classfer ensured the accuracy, greatly reduced the computatonal complexty and sped up the solvng speed. The results of dentfcaton fertle egg based on LS-SVM wth RBF kernel were shown n Table 3. The results showed 15 of the 16 nfertle eggs were judged correctly. Only one egg was erroneously judged. The mstake rato of nfertle eggs s lower than that of fertle eggs. It was encouragng, whch could reduce takng up space and mprove yelds. kernel. The results n ths study demonstrated the capablty of LS-SVM classfer for dentfyng fertle eggs pror to ncubaton. LS-SVM classfer provded better accuracy, hgher speed compared wth SVM and BP classfer. Classfcaton accuracy n ths study was 9.5%, whch was encouragng. Further research ncludng many more egg samples and brown shell eggs wll be carred through and more accurate classfcaton models wth other technques for dentfcaton fertle eggs pror to ncubaton wll be obtaned. ACKNOWLEDGEMENTS Ths work was supported by the earmarked fund for Modern Agro-ndustry Technology Research System (Project Code No.nycytx-41-g), Doctoral Scentfc Research Foundaton of Huazhong Agrcultural Unversty of Chna (Grant No ), and the Fundamental Research Funds for the Central Unverstes of Huazhong Agrculture Unversty (Grant No ). The authors would lke to thank the revewers of ths paper for ther constructve comments and advce, whch greatly helped us to mprove the qualty of the paper. Conclusons In ths research, an attempt has been made to extract eggs shape parameters and color nformaton of egg yolk and to classfy the fertle eggs usng LS-SVM wth RBF REFERENCES Bamels FR, Tona K, De Baerdemaeker JG, Decuypere EM (00). Detecton of early embryonc development n chcken eggs usng vsble lght transmsson. Br. Poult. Sc., 43: Coucke PM, Room GM, Decuypere EM, De Baerdemaeker JG (1997).

6 704 Afr. J. Agrc. Res. Montorng embryo development n chcken eggs usng acoustc resonance analyss. Botechnol. Prog., 13: Das K, Evans MD (199a). Detectng fertlty of hatchng eggs usng machne vson I: Hstogram characterzaton method. Trans. ASABE.35(4): Das K, Evans MD (199b). Detectng fertlty of hatchng eggs usng machne vson II: neural network classfers. Trans. ASABE, 35(6): Gonzalez RC (008). Dgtal Image Processng Usng MATLAB. Translated by Yuan QQ. Bejng, NY: Electronc Industry Press. Klen S, Roktta M, Baulan U, Theleben A, Haase A, Ellendorf F(00).Localzaton of the fertlzed germnal dsc n the chcken egg before ncubaton.poult.sc., 81: Lawrence KC, Smth DP, Wndham WR, Hetschmdt GW, Park B (006). Egg embryo development detecton wth hyperspectral magng. Int. J. Poult. Sc., 5(10): Ramteke RJ (010). Invarant moments based feature extracton for handwrtten devanagar vowels recognton. Int. J. Comput. Appl., 1(18): 1-5. Schellpfeffer MA, Kuhlmann RS, Bolender DL, Ruffolo CG,Kolesar GL (005). Prelmnary nvestgaton of the use of hgh frequency ultrasound magng n the chck embryo. Brth Def. Res., 73: Smth DP, Lawrence KC, Hetschmdt GW (008). Fertlty and embryo development of broler hatchng eggs evaluated wth a hyperspectral magng and predctve modelng system. Int. J. Poult. Sc., 7(10): Suykens JAK, Vandewalle J (1999). Least squares support vector machne classfers. Neural Process. Lett. 9(3): Tanguch R (007). Method and apparatus for determnng the sex of a fertlzed egg. US Patent No Vapnk V (1995). The Nature of Statstcal Learnng Theory. New York. USA: Sprnger-Verlag. Vapnk V (1999). An overvew of statstcal learnng theory. IEEE Trans. Neural Netw., 10(5): Wang QH, Deng XY, Ren YL, Dng YC, Xong LR, Zhou P, Wen YX, Wang SC (009). Egg freshness detecton based on dgtal mage technology. Sc. Res. Essays, 4(10): Yu ZH, Wang CG, Zhang XF, Zhang L (007). Improved PSO neural network system for automatc detectng fertlty of hatchng eggs. Comput. Eng. Des., 8(): Zhou P, Lu JY, Wang QH, Wen YX (007). Egg mage detecton method and weght predcton modelng. Trans. Chnese Soc. Agrc. Mach., 38(11):