참고문헌
- Alawneh, J. I., N. B. Williamson, and D. Bailey. 2006. Comparison of a camera - software system and typical farm management for detecting oestrus in dairy cattle at pasture. N.Z. Vet. J. 54:73-77. https://doi.org/10.1080/00480169.2006.36615
- At-Taras, E. E., and S. L. Spahr. 2001. Detection and characterization of estrus in dairy cattle with an electronic heatmount detector and an electronic activity tag. J. Dairy Sci. 84:792-798. https://doi.org/10.3168/jds.S0022-0302(01)74535-3
- Berckmans, D. 2004. Automatic on-line monitoring of animals by precision livestock farming. Keynote in the ISAH conference "Animal Production in Europe: The way forward in a changing world". 1:27-30.
- Brehme, U., U. Stollberg, R. Holz, and T. Schleusener. 2008. ALT pedometer - new sensor-aided measurement system for improvement in oestrus detection. Comput. Electron. Agric. 62:73-80. https://doi.org/10.1016/j.compag.2007.08.014
- Cox, S. 2003. Precision livestock farming. Wageningen Academic Pub, AE Wageningen, Netherlands.
- Cristianini, N., and J. Shawe-Taylor. 2000. An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, Cambridge.
- Davies, E. 2009. The application of machine vision to food and agriculture: a review. Imaging Sci. J. 57:197-217. https://doi.org/10.1179/174313109X454756
- De Mol, R. M., A. Keen, G. H. Kroeze, and J. M. F. H. Achten. 1999. Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filter. Comput. Electron. Agric. 22:171-185. https://doi.org/10.1016/S0168-1699(99)00016-2
- Firk, R., E. Stamer, W. Junge, and J. Krieter. 2002. Automation of oestrus detection in dairy cows: a review. Livest. Prod. Sci. 75: 219-232. https://doi.org/10.1016/S0301-6226(01)00323-2
- Firk, R., E. Stamer, W. Junge, and J. Krieter. 2003. Oestrus detection in dairy cows based on serial measurements using univariate and multivariate analysis. Arch. Tierz. 46:127-142.
- Fisher, A. D., R. Morton, J. M. Dempsey, J. M. Henshall, and J. R. Hill. 2008. Evaluation of a new approach for the estimation of the time of the LH surge in dairy cows using vaginal temperature and electrodeless conductivity measurements. Theriogenology 70:1065-1074. https://doi.org/10.1016/j.theriogenology.2008.06.023
- Friggens, N. C., M. Bjerring, C. Ridder, S. Hojsgaard, and T. Larsen. 2008. Improved detection of reproductive status in dairy cows using milk progesterone measurements. Reprod. Domest. Anim. 43:113-121. https://doi.org/10.1111/j.1439-0531.2008.01150.x
- Frost, A., C. Schofield, S. Beaulah, T. Mottram, J. Lines, and C. Wathes. 1997. A review of livestock monitoring and the need for integrated systems. Comput. Electron. Agric. 17:139-159. https://doi.org/10.1016/S0168-1699(96)01301-4
- Gutiérrez, A., C. Gonzalez, J. Jimenez-Leube, S. Zazo, N. Dopico, and I. Raos. 2009. A heterogeneous wireless identification network for the localization of animals based on stochastic movements. Sensors 9:3942-3957. https://doi.org/10.3390/s90503942
- Hall, M. 1998. Correlation-based Feature Selection for Machine Learning, Ph.D. Thesis, Department of Computer Science, Waikato University, Hamilton, NZ.
- Han, J., M. Kamber, and J. Pei. 2012. Data Mining: concepts and Techniques. 3rd Ed. Morgan Kaufman Publishers, Wyman Street, Waltham.
- Hancock, R., D. Swain, G. Bishop-Hurley, K. Patison, T. Wark, P. Valencia, P. Corke, and C. ONeill. 2009. Monitoring animal behavior and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing. Sensors 9: 3586-3603. https://doi.org/10.3390/s90503586
- Hockey, C., J. Morton, S. Norman, and M. McGowan. 2010. Evaluation of a neck mounted 2-hourly activity meter system for detecting cows about to ovulate in two paddock-based Australian dairy herds. Reprod. Domest. Anim. 45:107-117.
- Hwang, J., and H. Yoe. 2010. Study of the ubiquitous hog farm system using wireless sensor networks for environmental monitoring and facilities control. Sensors 10:10752-10777. https://doi.org/10.3390/s101210752
- Hwang, J., C. Shin, and H. Yoe. 2010. Study on an agricultural environment monitoring server system using wireless sensor networks. Sensors 10:11189-11211. https://doi.org/10.3390/s101211189
- Ikeda, Y., and Y. Ishii. 2008. Recognition of two psychological conditions of a single cow by her voice. Comput. Electron. Agric. 62:67-72. https://doi.org/10.1016/j.compag.2007.08.012
- Jahns, G. 2008. Call recognition to identify cow conditions-a call-recogniser translating calls to text. Comput. Electron. Agric. 62:54-58. https://doi.org/10.1016/j.compag.2007.09.005
- Jimenez, A., F. Bautista, C. S. Galina, J. J. Romero, and I. Rubio. 2011. Behavioral characteristics of Bos indicus cattle after a superovulatory treatment compared to cows synchronized for estrus. Asian-Aust. J. Anim. Sci. 24:1365-1371. https://doi.org/10.5713/ajas.2011.11032
- Jonsson, R., M. Blanke, N. K. Poulsen, F. Caponetti, and S. Hojsgaard. 2011. Oestrus detection in dairy cows from activity and lying data using on-line individual models. Comput. Electron. Agric. 76:6-15. https://doi.org/10.1016/j.compag.2010.12.014
- Koelsch, R. K., D. J. Aneshansley, and W. R. Butler. 1994. Analysis of activity measurement for accurate oestrus detection in dairy cattle. J. Agric. Eng. Res. 58:107-114. https://doi.org/10.1006/jaer.1994.1040
- Lehrer, A. R., G. S. Lewia, and E. Aizinbud. 1992. Oestrus detection in cattle: recent developments. Anim. Reprod. Sci. 28:355-362. https://doi.org/10.1016/0378-4320(92)90121-S
- Lyimo, Z., M. Nielen, W. Ouweltjes, T. Kruip, and F. Van Eerdenburg. 2000. Relationship among estradiol, cortisol and intensity of estrous behavior in dairy cattle. Theriogenology 53:1783-1795. https://doi.org/10.1016/S0093-691X(00)00314-9
- Maatje, K., R. M. De Mol, and W. Rossing 1997. Cow status monitoring (health and oestrus) using detection sensors. Comput. Electron. Agric. 16:245-254. https://doi.org/10.1016/S0168-1699(96)00052-X
- Mahdi, S., and T. Azizollah. 2009. Voice command recognition system based on MFCC and VQ algorithms, World Acad. Sci. Eng. Technol. 534-538.
- Mitchell, R. S., R. A. Sherlock, and L. A. Smith. 1996. An investigation into the use of machine learning for determining oestrus in cows. Comput. Electron. Agric. 15:195-213. https://doi.org/10.1016/0168-1699(96)00016-6
- Nebel, R. L., M. G. Dransfield, S. M. Jobst, and J. H. Bame 2000. Automated electronic systems for the detection of oestrus and timing of AI in cattle. Anim. Reprod. Sci. 60-61:713-723. https://doi.org/10.1016/S0378-4320(00)00090-7
- Peipei, S., C. Zhou, and C. Xiong. 2011. Automatic speech emotion recognition using support vector machine. International Conference on Electronic & Mechanical Engineering and Information Technology. 621-625.
- Roelofs, J. B., F. J. Van Eerdenburg, N. H. Soede, and B. Kemp. 2005. Pedometer readings for estrous detection and as predictor for time of ovulation in dairy cattle. Theriogenology 64:1690-1703. https://doi.org/10.1016/j.theriogenology.2005.04.004
- Ruiz-Garcia, L., L. Lunadei, P. Barreiro, and J. Robla. 2009. A review of wireless sensor technologies and applications in agriculture and food industry: state-of-the-art and current trends. Sensors 9:4728-4750. https://doi.org/10.3390/s90604728
- Saint-Dizier, M., and S. Chastant-Maillard. 2012. Towards an automated detection of oestrus in dairy cattle. Reprod. Domest. Anim. 47:1056-1061 doi: 10.1111/j.1439-0531.2011.01971.x.
- Saumande, J. 2002. Electronic detection of oestrus in postpartum dairy cows: efficiency and accuracy of the DEC (showheat) system. Livest. Prod. Sci. 77:265-271. https://doi.org/10.1016/S0301-6226(02)00036-2
- Van Asseldonk, M. A. P. M., R. B. M. Huirne, and A. A. Dijkhuizen. 1998. Quantifying characteristics of information-technology applications based on expert knowledge for detection of oestrus and mastitis in dairy cows. Prev. Vet. Med. 36:273-286. https://doi.org/10.1016/S0167-5877(98)00096-8
- Wathes, C., H. Kristensen, J. Aerts, and D. Berckmans. 2008. Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall? Comput. Electron. Agric. 64:2-10. https://doi.org/10.1016/j.compag.2008.05.005
- Xu, Z. Z., D. J. McKnight, R. Vishwanath, C. J. Pitt, and L. J. Burton. 1998. Estrus detection using radiotelemetry or visual observation and tail painting for dairy cows on pasture. J. Dairy Sci. 81:2890-2896. https://doi.org/10.3168/jds.S0022-0302(98)75849-7
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