Stress Detection and Classification of Laying Hens by Sound Analysis

  • Lee, Jonguk (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Noh, Byeongjoon (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Jang, Suin (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Park, Daihee (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Chung, Yongwha (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Chang, Hong-Hee (Department of Animal Science, Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Gyeongsang National University)
  • Received : 2014.08.26
  • Accepted : 2014.12.15
  • Published : 2015.04.01


Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.


Grant : 과학벨트 연계형 융합 SW 고급 인재 양성 사업팀


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