Development of animal collar for state of health determination of livestock. Issue 2 (17th February 2020)

• Record Type: Journal Article
• Authors: Yasmine Begum, A.; Balaji, M.; Vishnuvardhan, G.; Harshavardhan, G.; Lazer Mathew, T.
• Is Part Of: Journal of information & optimization sciences. Volume 41:Issue 2(2020)
• Page Start: 489
• Page End: 497
• Publication Date: 2020-02-17
• Subjects: 68U99; Animal collar -- Physiological parameters -- Arduino -- Temperature sensing -- Rumination measurement -- THI index; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2020.1724613
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: The variation of Livestock physiological parameters such as stress level, rumination, body temperature has harmful effect in reproduction cycle and also causes diseases such as Mastitis, Milk fever, Lameness etc. Development of animal collar for monitoring the physiological parameters may help in better management of cattle diseases and reproduction cycle. The advantages of this system include simplicity, portability and inexpensive health care of livestock. The cattle surface body temperature was measured continuously for one day with an interval of one hour. The body temperature of the cattle measured using DHT11 sensor and mercury thermometer is compared and the average difference between the temperature measured using DHT11 sensor and mercury thermometer is 0.03°C indicating the feasibility of this method to monitor state of health of a cattle. With the temperature data and humidity data obtained from DHT11 sensor THI index is calculated and stress level of the cattle is predicted without using any sensor. The ADXL335 sensor is fixed under the collar near the left side of the mouth of the livestock to monitor rumination of cattle. This system is implemented in the Rangampet village, Chittoor district in Andhra Pradesh. The prototype of the collar based cattle health monitoring system shows that it is helpful for livestock farmers for recognizing various diseases accurately.