Automatic Detection and Recognition of Pig Wasting Diseases Using Sound Data in Audio Surveillance Systems

Sensors

Yongwha Chung²  Seunggeun Oh²  Jonguk Lee²  Daihee Park²  Hong-Hee Chang¹ 

[1] Department of Animal Science, Institute of Agriculture & Life Sciences, College of Agriculture and Life Sciences, Gyeongsang National University, Jinju 660-701, Korea; E-Mail:;Department of Computer and Information Science, College of Science and Technology, Korea University, Sejong 339-700, Korea; E-Mails:

关键词: pig wasting diseases;    sound data;    mel frequency cepstrum coefficient;    support vector data description;    sparse representation classifier;   

DOI  :  10.3390/s131012929

来源: mdpi

【 摘 要 】

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC) from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD) and the Sparse Representation Classifier (SRC) as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used) and accurately (94% detection and 91% classification accuracy), either as a standalone solution or to complement known methods to obtain a more accurate solution.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.