Lee Jonguk, Noh Byeongjoon, Jang Suin, Park Daihee, Chung Yongwha, Chang Hong-Hee
Department of Animal Science, Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Gyeongsang National University, Jinju 660-701, Korea .
Asian-Australas J Anim Sci. 2015 Apr;28(4):592-8. doi: 10.5713/ajas.14.0654.
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.
应激会对商品肉鸡的健康产生不利影响，并且给该行业带来不可忽视的经济成本。在本文中，我们首先开发了一种低成本、非侵入式的自动在线监测原型，该原型利用声音数据向商业家禽养殖场的生产者通报应激情况。所提出的系统由三个二分类支持向量机分层构建。首先，它从蛋鸡发出的声音中选择一个最优声学特征子集。检测与分类模块从声音变化中检测应激并将其分类为附属声音类型，例如温度变化导致的身体应激和恐惧导致的心理应激。最后，使用来自音频监控系统的真实声音数据进行了实验评估。应激检测准确率接近96.2%，分类模型得到验证，确认平均分类准确率为96.7%，其召回率和精确率指标令人满意。
