Application of computer vision technology on raising sow and procreating of processing

Yun Yang

School of Election and Information Engineering, Ningbo University of Technology, Ningbo City, China.

DOI: 10.4236/as.2013.412093                

Abstract

This paper expounds the application of machine vision theory, composition and technology in the sow breeding process monitoring, auxiliary judgment, and growth of young monitoring. It also points out the problems and deficiency in the application of machine vision technology, and discusses the development trends and prospects of the machine vision technology in agricultural engineering. The application of machine vision is a process in which dynamic original image from the sows estrus is collected with a CCD camera, and then black and white ash three binarization image in adjournments of the threshold value is made by using image acquisition card, through the median filtering and gray processing. The practitioners can extract respective image information from the sow estrus, pregnancy and birth delivery. Applying the computer vision system in the sow farm effectively enhances the practitioners’ objectivity and precision in their efforts to assess the whole process of sow birth delivery.

Keywords

Computer Vision System; Infrared Sensor; Image Processing; Raising Sow

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Gao, H. and Wang, Y.Q. (2004) Study on the shape classification of farm produce based on computer vision. Computer engineering and Applications, 227-229.
[2]	He, D.J., Yang, Q., Xue, S.P. and Geng, N. (1998) Computer vision for sorting of fresh fruits. Transactions of CSAE (The Chinese Society of Agricultural Engineering), 14, 202-205
[3]	Liu, Z.T. (2011) Sensor principles and application. Xi’an Electronic and Science University Press, Xi’an.
[4]	Fang, C.Y. and Xie, L.H. (2008) Study on identification of rice varieties using computer vision. Food Science, Beijing.
[5]	(Canada) Parker (Translated by Jing Li) (2012) Algorithms for image processing and computer vision. 2nd Edition, Tsinghua University Press, Beijing.
[6]	Guo, C.L. (2009) Fundament of digital signal obtaining and information processing. Tsinghua University Press, Beijing.
[7]	Ma, Y.Z., Tao, L.Y. and Wang, X.H. (2006) Application of computer vision technology. The Technology and Testing Machine, 61-65.
[8]	Patel, K.K., Kar, A., Jha, S.N. and Khan, M.A. (2012) Machine vision system: A tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 49, 123-141.
[9]	Raji A.O. and Alamutu, A.O. (2005) Prospects of computer vision automated sorting systems in agricultural process operations in Nigeria. Invited Overview.
[10]	Zazueta, F.S., Bucklin, R. and Jones, P.H. Basic concepts in environmental computer control of agricultural systems.
[11]	Khojastenazh, M., Omidand, M. and Tabatabaee-far A. (2010) Development of lemon sorting system based on color and size. African Journal Plant Science, 4, 122-127.
[12]	Dong, Y.W. and Ji, B.P. (2007) CCD-NIR analysis of acidity and sugar content in apples. Food Science, 28.
[13]	Zhang, Y.J. (2011) A course of computer vision. People’s Posts and Telecommunications Press.
[14]	Narendra, V.G. and Hareesh Sr., K.S. (2010) Lecturer, quality inspection and grading of agricultural and food products by computer vision—A review. International Journal of Computer Applications, 2.
[15]	Johnson, L.F., Herwitz, S., Dunagan, S., Lobitz, B., Sullivan, D. and Slye, R. (2003) Collection of ultra high spatial and spectral resolution image data over California Vineyards with a small UAV. Proceedings of the Int’l Symposium on Remote Sensing of Environment. Earth Science Division, NASA Ames Research Center 242-4, Moffett Field.
[16]	Adewumi, B.A. (2012) Transformation of field to food materials: Agro-food process engineering approach. Funaab Inaugural Lecture Series No. 36.