Automated detection of sick pigs based on machine vision

Author

Zhu, Weixing ; Pu, Xuefeng ; Li, Xincheng ; Zhu, Xiaofang

Author_Institution

Sch. of Electr. & Inf. Eng., Jiangsu Univ., Zhenjiang, China

Volume

2

fYear

2009

fDate

20-22 Nov. 2009

Firstpage

790

Lastpage

794

Abstract

An automated detection method for sick pig behavior was proposed after analyzing the disadvantages of traditional observation methods for abnormal behavior of pigs. An embedded detecting system based on computer vision and ARM (Advanced RISC (Reduced Instruction Set Computer) Machines) was designed to monitor the pig excretion behavior. The improved moving objecting detection method and symmetrical pixel block image identification algorithm is designed to recognize the suspected sick pigs. The relevant pictures are taken and sent to the surveillance center through GPRS (General Packet Radio Service) networks after finding the suspected sick pigs. The experiment results for 10 Yorkshire pigs showed that the detection accuracy is about 78.38%. The designed method and monitoring system will be helpful for improving production automation in modern pig farm.

Keywords

computer vision; farming; image recognition; packet radio networks; Yorkshire pigs; abnormal behavior pigs; automated detection method; embedded detecting system; general packet radio service; image identification algorithm; improving production automation; machine vision; modern pig farm; objecting detection method; pig excretion behavior; reduced instruction set computer; sick pigs based; symmetrical pixel block; traditional observation methods; Algorithm design and analysis; Computer aided instruction; Computer vision; Computerized monitoring; Condition monitoring; Embedded computing; Machine vision; Object recognition; Pixel; Reduced instruction set computing; GPRS; behavior detection; embedded system; machine vision; pig;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-4754-1

Electronic_ISBN

978-1-4244-4738-1

Type

conf

DOI

10.1109/ICICISYS.2009.5358295

Filename

5358295