Stress detection and classification of laying hens by sound analysis

Abstract

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.

Keywords: Laying Hens; Monitoring System; Sound Analysis; Stress Recognition.

Figure 1

Overall structure of the stress recognition system. CFS, correlation-based feature selection; SVM, support vector machine.

Figure 2

Architecture for the stress detection and recognition module based on the hierarchical SVM. SVM, support vector machine.

Figure 3

Waveforms and spectrograms from normal and stressed sound samples acquired from Korean laying hens.