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Citations of recent PLF publications authored by UTK-PLF Team Members

E.T. Psota, E.K. Luc, G.M. Pighetti, L.G. Schneider, R.T. Trout Fryxell, J.W. Keele, L.A. Kuehn. Development and validation of a neural network for the automated detection of horn flies on cattle, Computers and Electronics in Agriculture, Volume 180, 2021, 105927, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2020.105927.

  • Yang, X., Y. Zhao, H. Qi, G.T. Tabler. 2021. Characterizing sounds of different sources in a commercial broiler house. Animals, 11(3), 916.
  • Li, G., Y. Zhao, Z. Porter, J.L. Purswell. 2021. Automated measurement of broiler stretching behaviors under four stocking densities via faster region-based convolutional neural network. Animal, 15, 100059.
  • Li, G., X. Hui, Z. Chen, G.D. Chesser, Y. Zhao. 2021. Development and Evaluation of a Method to Detect Broilers Continuously Walking around Feeder as an Indication of Restricted Feeding Behaviors. Computers and Electronics in Agriculture, 181, 105982.
  • Yang, X., X. Huo, G. Li, J.L. Purswell, G.T. Tabler, G.D. Chesser, C.L. Magee, Y. Zhao. 2020. Effects of elevated perching platform and robotic vehicle on broiler production, welfare, and housing environment. Transactions of the ASABE 63(6): 1981-1990.
  • Parajuli, P., Y. Zhao, G.T. Tabler. 2020. Evaluating avoidance distance and fleeing speed of broilers exposed to aerial systems. International Journal of Agricultural and Biological Engineering, 13(6): 34-40.
  • Li, G., X. Hui, F. Lin, Y. Zhao. 2020. Developing and evaluating poultry preening behavior detectors via mask region-based convolutional neural network. Animals, 10(10), 1762.
  • Li, G., Y. Zhao, J.L. Purswell, Q. Du, G.D. Chesser, J.W. Lowe. 2020. Analysis of feeding and drinking behaviors of group-reared broilers via image processing and machine learning. Computers and Electronics in Agriculture, 175, 105596.
  • Li, G., B. Ji, B. Li, Z. Shi, Y. Zhao, Y. Dou, J. Brocato, Y. Liu. 2020. Assessment of layer pullet drinking behaviors under selectable light colors via convolutional neural network. Computers and Electronics in Agriculture, 172, 105333.
  • Yang, X., Y. Zhao, G.T. Tabler. 2020. Accuracy of broiler activity index as affected by sampling time interval. Animals, 10(6), 1102.
  • Li, G., Y. Zhao, J.L. Purswell, G.D. Chesser, J.W. Lowe, T. Wu. 2020. Effects of antibiotic-free diet and stocking density on male broilers reared to 35 days of age. Part 2: Feeding and drinking behaviors of broilers. Journal of Applied Poultry Research, 29(2): 391-401.
  • Li, G., B. Li, Y. Zhao, Z. Shi, Q. Tong, Y. Liu. 2020. Diurnal rhythms of group-housed layer pullets with free choices between light and dim environments. Canadian Journal of Animal Science, 100(1): 37-46.
  • Parajuli, P., Y. Huang, G.T. Tabler, J.L. Purswell, J.L. DuBien, Y. Zhao. 2020. Comparative evaluation of poultry-human and poultry-robot avoidance distances. Transactions of the ASABE, 63(2): 477-484.
  • Li, G., Y. Xu, Y. Zhao, Q. Du, Y. Huang. 2020. Evaluating convolutional neural networks for cage-free floor egg detection. Sensors, 20(2), 332.

  • Eckelkamp, E. and J. Bewley. 2020. On-farm use of disease alerts generated by precision dairy technology. J. Dairy Sci. 103:1566-1582.
  • Eckelkamp, E. A. 2019. Invited Review: Current state of wearable precision dairy technologies in disease detection. Appl. Anim. Sci. 35:209-220.

  • Li, G., Y. Huang, Z. Chen, G.D. Chesser, J.L. Purswell, J. Linhoss, Y. Zhao. 2021. Practices and applications of convolutional neural network-based computer vision systems in animal farming: A review. Sensors, 21, 1492.