2,319
Views
14
CrossRef citations to date
0
Altmetric
Other

Automated early detection of drops in commercial egg production using neural networks

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 739-747 | Received 03 Jun 2017, Accepted 09 Aug 2017, Published online: 17 Oct 2017

References

  • Abdi, H., and L. J. Williams. 2010. “Tukey’s Honestly Significant Difference (HSD) Test.” In Encyclopedia of Research Design, 1–5. Thousand Oaks, CA: Sage.
  • Amiri, A. S., T. A. Niaki, and A. T. Moghadam. 2014. “A Probabilistic Artificial Neural Network-Based Procedure for Variance Change Point Estimation.” Soft Computing 19 (3): 691–700. doi:10.1007/s00500-014-1293-x.
  • Antonov, L. V., K. V. Makarov, and A. A. Orlov. 2015. “Development and Experimental Research on Production Data Analysis Algorithm in Livestock Enterprises.” Procedia Engineering 129: 664–669. doi:10.1016/j.proeng.2015.12.088.
  • Anwar, H. M., B. V. Ayodele, C. K. Cheng, and M. R. Khan. 2016. ““Artificial Neural Network Modeling of Hydrogen-Rich Syngas Production from Methane Dry Reforming over Novel Ni/CaFe 2 O 4 Catalysts.” International Journal of Hydrogen Energy 41 (26): 11119–11130.
  • Bennett, K. P., and C. Campbell. 2000. “Support Vector Machines.” ACM SIGKDD Explorations Newsletter 2 (2): 1–13. doi:10.1145/380995.380999.
  • Blagus, R., and L. Lusa. 2010. “Class Prediction for High-Dimensional Class-Imbalanced Data.” BMC Bioinformatics 11: 523. doi:10.1186/1471-2105-11-523.
  • Cameron, A. 2012. Manual of Basic Animal Disease Surveillance. Kenya: Interafrican Bureau for Animal Resources.
  • De Vries, A., and J. K. Reneau. 2010. “Application of Statistical Process Control Charts to Monitor Changes in Animal Production Systems.” Journal of Animal Science 88 (13 Suppl): E11–24. doi:10.2527/jas.2009-2622.
  • Elkan, C. 2001. “The Foundations of Cost-Sensitive Learning.” International joint conference on artificial intelligence, 2001.
  • Flanders, F., and J. R. Gillespie. 2015. Modern Livestock & Poultry Production. Boston, MA: Cengage Learning.
  • Frank, R. J., N. Davey, and S. P. Hunt. 2001. “Time Series Prediction and Neural Networks.” Journal of Intelligent and Robotic Systems 31 (1/3): 91–103. doi:10.1023/a:1012074215150.
  • Frost, A. R., C. P. Schofield, S. A. Beaulah, T. T. Mottram, J. A. Lines, and C. M. Wathes. 1997. “A Review of Livestock Monitoring and the Need for Integrated Systems.” Computers and Electronics in Agriculture 17 (2): 139–159. doi:10.1016/S0168-1699(96)01301-4.
  • Gates, M. C., L. K. Holmstrom, K. E. Biggers, and T. R. Beckham. 2015. “Integrating Novel Data Streams to Support Biosurveillance in Commercial Livestock Production Systems in Developed Countries: Challenges and Opportunities.” Front Public Health 3: 74. doi:10.3389/fpubh.2015.00074.
  • Guo, L., D. Rivero, and A. Pazos. 2010. “Epileptic Seizure Detection Using Multiwavelet Transform Based Approximate Entropy and Artificial Neural Networks.” Journal of Neuroscience Methods 193 (1): 156–163. doi:10.1016/j.jneumeth.2010.08.030.
  • Guyon, I., S. Gunn, M. Nikravesh, and L. A. Zadeh. 2008. Feature Extraction: Foundations and Applications. Studies in Fuzziness and Soft Computing. Heidelberg: Springer..
  • Guyon, I., and A. Elisseeff. 2003. “An Introduction to Variable and Feature Selection.” Journal of Machine Learning Research 3: 1157–1182.
  • Hepworth, P. J., A. V. Nefedov, I. B. Muchnik, and K. L. Morgan. 2012. “Broiler Chickens Can Benefit from Machine Learning: Support Vector Machine Analysis of Observational Epidemiological Data.” Journal of the Royal Society Interface 9 (73): 1934–1942. doi:10.1098/rsif.2011.0852.
  • Herrera, F., C. Hervas, J. Otero, and S. Luciano 2004. “Un Estudio Empırico Preliminar Sobre Los Tests Estadısticos Más Habituales En El Aprendizaje Automático.” Tendencias de la Minerıa de Datos en Espana, Red Espanola de Minerıa de Datos y Aprendizaje (TIC2002-11124-E):403–412.
  • Huang, Y.-M., C.-M. Hung, and H. C. Jiau. 2006. “Evaluation of Neural Networks and Data Mining Methods on a Credit Assessment Task for Class Imbalance Problem.” Nonlinear Analysis: Real World Applications 7 (4): 720–747. doi:10.1016/j.nonrwa.2005.04.006.
  • Kalhor, T., A. Rajabipour, A. Akram, and M. Sharifi. 2016. “Modeling of Energy Ratio Index in Broiler Production Units Using Artificial Neural Networks.” Sustainable Energy Technologies and Assessments 17: 50–55. doi:10.1016/j.seta.2016.09.002.
  • Kapoor, P., and S. S. Bedi. 2013. “Weather Forecasting Using Sliding Window Algorithm.” International Scholarly Research Network Signal Processing 2013: 1–5. doi:10.1155/2013/156540.
  • Kruse, R., C. Borgelt, F. Klawonn, C. Moewes, M. Steinbrecher, and P. Held. 2013. “Multi-Layer Perceptrons.” In Computational Intelligence, 47–81. London: Springer.
  • Kuhn, M., and K. Johnson. 2013. Applied Predictive Modeling. New York: Springer.
  • Lindsay, D., and S. Cox. 2005. Effective Probability Forecasting for Time Series Data Using Standard Machine Learning Techniques, 35–44. Berlin: Springer.
  • Lokhorst, C., and E. J. J. Lamaker. 1996. “An Expert System for Monitoring the Daily Production Process in Aviary Systems for Laying Hens.” Computers and Electronics in Agriculture 15 (3): 215–231. doi:10.1016/0168-1699(96)00017-8.
  • Long, A., and S. Wilcox. 2011. “Optimizing Egg Revenue for Poultry Farmers.” Poultry, 1–10. Science.
  • Ma, X., Y. Zhang, and Y. Wang. 2015. “Performance Evaluation of Kernel Functions Based on Grid Search for Support Vector Regression.” 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2015/7.
  • Martens, D., and B. Baesens. 2010. “Building Acceptable Classification Models, 53–74. Boston, MA: Springer.
  • Martinerie, J., C. Adam, M. Le Van Quyen, M. Baulac, S. Clemenceau, B. Renault, and F. J. Varela. 1998. “Epileptic Seizures Can Be Anticipated by Non-Linear Analysis.” Nature Medicine 4 (10): 1173–1176. doi:10.1038/2667.
  • Mertens, K., I. Vaesen, J. Löffel, B. Kemps, B. Kamers, J. Zoons, P. Darius, E. Decuypere, J. De Baerdemaeker, and B. De Ketelaere. 2009. “An Intelligent Control Chart for Monitoring of Autocorrelated Egg Production Process Data Based on a Synergistic Control Strategy.” Computers and Electronics in Agriculture 69 (1): 100–111. doi:10.1016/j.compag.2009.07.012.
  • Møller, M. F. 1993. “A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning.” Neural Networks 6 (4): 525–533. doi:10.1016/s0893-6080(05)80056-5.
  • Mucherino, A., P. J. Papajorgji, and P. M. Pardalos. 2009. Data Mining in Agriculture. Vol. 34, Springer Optimization and Its Applications. Edited by Panos M. Pardalos. New York, NY: Springer.
  • Narinc, D., F. Uckardes, and E. Aslan. 2014. “Egg Production Curve Analyses in Poultry Science.” Worlds Poultry Sciences Journal 70 (04): 817–828. doi:10.1017/S0043933914000877.
  • Pazzani, M., C. Merz, P. Murphy, K. Ali, T. Hume, and C. Brunk. 1994. “Reducing Misclassification Costs.” Proceedings of the Eleventh International Conference on Machine Learning, 1994.
  • Pica-Ciamarra, U., D. Baker, N. Morgan, and A. Zezza. 2014. Investing in the Livestock Sector: Why Good Numbers Matter, A Sourcebook for Decision Makers on How to Improve Livestock Data. Washington, DC: The World Bank and FAO.
  • Ramírez-Morales, I., D. Rivero-Cebrián, E. Fernández-Blanco, and A. Pazos-Sierra. 2016. “Early Warning in Egg Production Curves from Commercial Hens: A SVM Approach.” Computers and Electronics in Agriculture 121: 169–179. doi:10.1016/j.compag.2015.12.009.
  • Refaeilzadeh, P., L. Tang, and H. Liu. 2009. “Cross-Validation.” In Encyclopedia of Database Systems, edited by L. Liu and M. Tamer Özsu. 532-538. New York: Springer US.
  • Rivero, D., E. Fernandez-Blanco, J. Dorado, and A. Pazos. 2011. “Using Recurrent ANNs for the Detection of Epileptic Seizures in EEG Signals.” 2011 IEEE Congress of Evolutionary Computation (CEC), 2011/6.
  • Saeed, K., and S. Václav 2014. Computer Information Systems and Industrial Management: 13th IFIP TC 8 International Conference, CISIM 2014, Ho Chi Minh City, Vietnam, November 5-7, 2014, Proceedings: Springer.
  • Saeys, Y., I. Inza, and P. Larranaga. 2007. “A Review of Feature Selection Techniques in Bioinformatics.” Bioinformatics 23 (19): 2507–2517. doi:10.1093/bioinformatics/btm344.
  • Samborska, I. A., V. Alexandrov, L. Sieczko, B. Kornatowska, V. Goltsev, M. D. Cetner, and H. M. Kalaji. 2014. “Artificial Neural Networks and Their Application in Biological and Agricultural Research.” NanoPhotoBioSciences 2: 14–30.
  • Schaefer, A. L., N. Cook, S. V. Tessaro, D. Deregt, G. Desroches, P. L. Dubeski, A. K. W. Tong, and D. L. Godson. 2004. “Early Detection and Prediction of Infection Using Infrared Thermography.” Canadian Journal of Animal Science 84 (1): 73–80. doi:10.4141/a02-104.
  • Singh, P. K., R. Sarkar, and M. Nasipuri. 2015. “Statistical Validation of Multiple Classifiers over Multiple Datasets in the Field of Pattern Recognition.” International Journal of Applied Pattern Recognition 2 (1): 1. doi:10.1504/ijapr.2015.068929.
  • Sun, Y., A. K. C. Wong, and M. S. Kamel. 2009. “Classification of Imbalanced Data: A Review.” International Journal of Pattern Recognition and Artificial Intelligence 23 (04): 687–719. doi:10.1142/s0218001409007326.
  • Vannucci, M., and V. Colla. 2016. “Smart Under-Sampling for the Detection of Rare Patterns in Unbalanced Datasets.” In Intelligent Decision Technologies 2016, edited by I. Czarnowski, A. M. Caballero, R. J. Howlett, and L. C. Jain, 395–404, Basel: Springer International Publishing.
  • Venkatesan, M., A. Thangavelu, and P. Prabhavathy. 2013. Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Vol. 202, Advances in Intelligent Systems and Computing. India: Springer India.
  • Wheeler, E. F., K. D. Casey, J. S. Zajaczkowski, P. A. Topper, R. S. Gates, H. Xin, Y. Liang, and A. Tanaka. 2003. “Ammonia Emissions from US Poultry Houses: Part III–broiler Houses.” Air Pollution from Agricultural Operations-III, Raleigh, NC, October 12–15.
  • Woudenberg, S. P., D. Linda Gaag, A. Feelders, and A. R. Elbers. 2014. “Real-Time Adaptive Problem Detection in Poultry.” In Ecai 2014, 1217–1218. Amsterdam: IOS Press.
  • Xiao, J., H. Wang, L. Shi, L. Mingzhe, and M. Haikun. 2011. “The Development of Decision Support System for Production of Layer.” Computer and Computing Technologies in Agriculture V, Beijing, October 29-31. Berlin: Springer.
  • Zahirnia, K., M. Teimouri, R. Rahmani, and A. Salaq. 2015. “Diagnosis of Type 2 Diabetes Using Cost-Sensitive Learning.” 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE), 2015/10.
  • Zhi-Hua, Z., and L. Xu-Ying. 2006. “Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem.” IEEE Transactions on Knowledge and Data Engineering 18 (1): 63–77. doi:10.1109/tkde.2006.17.