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Monitoring Activity for Recognition of Illness in Experimentally Infected Weaned Piglets Using Received Signal
Sonia Tabasum Ahmed1, Hong-Seok Mun1, Md Manirul Islam1
1Department of Information and Communication Engineering, Sunchon National University, Suncheon 540-950, Korea .
Asian-Australasian Journal of Animal Sciences
|January 7, 2016
Summary
A new disease forecasting system using wireless sensors and 3-axis acceleration sensors successfully monitored piglet movement to detect early-stage Salmonella enteritidis (SE) and Escherichia coli (EC) infections. Altered movement patterns in infected piglets indicate the system
Area of Science:
- Animal Science
- Biomedical Engineering
- Agricultural Technology
Background:
- Early disease detection in livestock is crucial for preventing outbreaks and economic losses.
- Monitoring animal behavior and physiological parameters can provide early indicators of illness.
- Wireless sensor networks offer a non-invasive method for continuous animal health surveillance.
Purpose of the Study:
- To develop and implement a disease forecasting system for early detection of piglet infections.
- To utilize a ZigBee-based wireless network with 3-axis acceleration sensors to monitor piglet movement.
- To analyze movement data for identifying behavioral changes associated with Salmonella enteritidis (SE) and Escherichia coli (EC) infections.
Main Methods:
- Twenty-seven weaned piglets were divided into control, SE, and EC infection groups.
- Wireless sensor nodes equipped with 3-axis acceleration sensors were attached to monitor piglet movement for five days.
- Physiological parameters (weight gain, feed intake, body temperature) and movement data (X, Y, Z axes) were collected and analyzed.
Main Results:
- Infected piglets exhibited lower weight gain, feed intake, and higher feed conversion ratios compared to controls (p<0.05).
- Body temperature was reduced in SE and EC infected piglets at specific time points (p<0.05).
- Significant alterations in piglet movement patterns (Y-axis and Z-axis) were observed in infected groups compared to the control group across different time periods (p<0.05).
Conclusions:
- The developed disease forecasting system effectively utilized wireless sensor data to detect early-stage infections in piglets.
- Acceleration sensor data revealed significant changes in piglet movement patterns, correlating with SE and EC infections.
- The system demonstrates potential for real-time, non-invasive monitoring of animal health and early disease outbreak prediction in swine farming.
