Nondestructive detection of infertile hatching eggs based on spectral and imaging information
DOI:
https://doi.org/10.25165/ijabe.v8i4.1672Keywords:
hyperspectral image, hatching eggs, information fusion, nondestructive detectionAbstract
In order to quickly distinguish infertile eggs from fertile eggs, the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs. Before hatching eggs were incubated, a hyperspectral imaging system (wavelength between 400 to 1 000 nm) was used to acquire the images one-by-one manually. The characteristic information of ratios of length to short axis, elongation, roundness and the ratios of the yolk area to the whole area was extracted based on the images. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimensions of image-spectrum fusion information; the top six principal components were extracted. Support vector machine (SVM) method was used to establish classification of fertile and infertile eggs models, which are based on image, spectrum and image-spectrum fusion information respectively. The accuracy rates of the SVM models were 84.00%, 90.00% and 93.00% respectively. The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model. Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation. Keywords: hyperspectral image, hatching eggs, information fusion, nondestructive detection DOI: 10.3965/j.ijabe.20150804.1672 Citation: Zhu Z H, Liu T, Xie D J, Wang Q H, Ma M H. Nondestructive detection of infertile hatching eggs based on spectral and imaging information. Int J Agric & Biol Eng, 2015; 8(4): 69-76.References
USDA. Poultry: Chickens and eggs. USDA National Agricultural Statistics Service, 2006.
Tian L, Ma X L. Design and implementation of unfertilized eggs verification system based on computer vision. Journal of Agricultural Mechanization Research, 2011; 33(8): 153–155. (in Chinese with English abstract)
Zou X R, Pan L Q, Tu K, Liu P. Research progresses in non-destructive measurements on incubation quality of eggs. Science and Technology of Food Industry, 2010; 31(2): 342–344. (in Chinese with English abstract)
Zhu Z H, Ma M H. The identification of white fertile eggs prior to incubation based on machine vision and least square support vector machine. African Journal of Agricultural Research, 2011; 6(12): 2699–2704.
Das K, Evans M D. Detecting fertility of hatching eggs using machine vision I: Histogram characterization method. Transactions of the ASAE, 1992; 35(4): 1335–1341.
Zhang W, Tu K, Liu P, Pan L Q, Zhan G. Early fertility detection of hatching duck egg based on fusion between computer vision and impact excitation. Transactions of the CSAM, 2012; 43(2): 140–145. (in Chinese with English abstract)
Bamelis F R, Tona K, De Baerdemaeker J G, Decuypere E M. Detection of early embryonic development in chicken eggs using visible light transmission. Br. Poult. Sci., 2002; 43(2): 204–212.
Kemps B J, De Ketelaere B, Bamelis F R, Decuypere E M, De Baerdemaeker J G. Vibration analysis on incubating eggs and its relation to embryonic development. Biotechnology Progress, 2003; 19(3): 1022–1025.
Coucke P M, Room G M, Decuypere E M, De Baerdemaeker J G. Monitoring embryo development in chicken eggs using acoustic resonance analysis. Biotechnology Progress, 1997; 13(4): 474–478.
Jones S T, Shattuck R E. Detection of Early Embryonic Development in hatching eggs: a hyperspectral imaging systems and neural network approach. Johns Hopkins APL Technical Digest, 2005; (1): 67–73.
Lawrence K C, Smith D P, Windham W R, Heitschmidt G W, Park B. Egg embryo development detection with hyperspectral imaging. International Journal of Poultry Science, 2006; 5(10): 964–969.
Zhang W, Pan L Q, Tu K. Detecting early embryo development of chicken hatching eggs by hyperspectral transmittance imaging. Transactions of the CSAE, 2012; 28(21): 149–155. (in Chinese with English abstract)
Zhu Z H ,Wang Q H, Wang S C, Dai M Y, Ma M H. The detection of hatching eggs prior to incubation by the near infrared spectrum. Spectroscopy and Spectral Analysis, 2012; 32(4): 962–965. (in Chinese with English abstract)
Ma X L, Yi S J. Unfertilized eggs verification system before hatching based on embedded system and machine vision. Transactions of the CSAM, 2011; 42(5): 187–192. (in Chinese with English abstract)
Shan J J, Peng Y K, Wang W, Li Y Y, Wu J H, Zhang L L. Simultaneous detection of external and internal quality parameters of apples using hyperspectral technology. Transactions of the CSAM, 2011; 42(3): 140–144. (in Chinese with English abstract)
Kim D, Burks T F, Ritenour M A, Qin J W. Citrus black spot detection using hyperspectral imaging. Int J Agric & Biol Eng, 2014; 7(6): 20-27.
Zhao J W, Liu J H, Chen Q S, Saritporn V. Detecting subtle bruises on fruits with hyperspectral imaging. Transactions of the CSAM, 2008; 39(1): 106–109. (in Chinese with English abstract)
Vapnik V N. The nature of statistical learning theory. New York: Springer-Verlag, 1995.
Wang Q H, Wen Y X, Lin X D, Wang S C. Correlation between egg freshness and morphological characteristics of light transmission image of eggs. Transactions of the CSAE, 2008; 24(3): 179–183. (in Chinese with English abstract)
Zhao W L, Liu M D, Yuan J C, Zhou W J, Zhang H J. System for automatic recognizing unfertilized egg. Agriculture Network Information, 2011; (9): 37–40. (in Chinese with English abstract)
Yu J J. Fast and Nondestructive Detection of Gray Mold on Tomato Plant Using High Spectrum Imaging Technology. Doctoral dissertation, Zhejiang: Zhejiang University, 2012 (in Chinese with English abstract)
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