Automatic recording of individual oestrus vocalisation in group-housed dairy cattle: development of a cattle call monitor
Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate...
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| Veröffentlicht in: | Animal (Cambridge, England) England), 2020-01, Vol.14 (1), p.198-205, Article 1751731119001733 |
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| creator | Röttgen, V. Schön, P. C. Becker, F. Tuchscherer, A. Wrenzycki, C. Düpjan, S. Puppe, B. |
| description | Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the ‘gold standard’. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. The results are promising for the future use of vocalisation in automatic oestrus detection systems. |
| doi_str_mv | 10.1017/S1751731119001733 |
| format | Article |
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C. ; Becker, F. ; Tuchscherer, A. ; Wrenzycki, C. ; Düpjan, S. ; Puppe, B.</creator><creatorcontrib>Röttgen, V. ; Schön, P. C. ; Becker, F. ; Tuchscherer, A. ; Wrenzycki, C. ; Düpjan, S. ; Puppe, B.</creatorcontrib><description>Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the ‘gold standard’. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. 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C.</creatorcontrib><creatorcontrib>Becker, F.</creatorcontrib><creatorcontrib>Tuchscherer, A.</creatorcontrib><creatorcontrib>Wrenzycki, C.</creatorcontrib><creatorcontrib>Düpjan, S.</creatorcontrib><creatorcontrib>Puppe, B.</creatorcontrib><title>Automatic recording of individual oestrus vocalisation in group-housed dairy cattle: development of a cattle call monitor</title><title>Animal (Cambridge, England)</title><addtitle>ANIMAL</addtitle><addtitle>Animal</addtitle><description>Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the ‘gold standard’. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. 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C.</au><au>Becker, F.</au><au>Tuchscherer, A.</au><au>Wrenzycki, C.</au><au>Düpjan, S.</au><au>Puppe, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic recording of individual oestrus vocalisation in group-housed dairy cattle: development of a cattle call monitor</atitle><jtitle>Animal (Cambridge, England)</jtitle><stitle>ANIMAL</stitle><addtitle>Animal</addtitle><date>2020-01</date><risdate>2020</risdate><volume>14</volume><issue>1</issue><spage>198</spage><epage>205</epage><pages>198-205</pages><artnum>1751731119001733</artnum><issn>1751-7311</issn><eissn>1751-732X</eissn><abstract>Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the ‘gold standard’. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. 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| subjects | Agriculture Agriculture, Dairy & Animal Science Algorithms Animals Audio data Behavior bioacoustics Biological Variation, Individual Bos taurus caller identification Cattle Change detection Dairy cattle Dairy industry Dairying - methods Estrus Farms Female Identification Life Sciences & Biomedicine Livestock Farming Systems Malfunctions Microphones Neck oestrus detection Pilot Projects Recording Research Article Science & Technology Sensitivity analysis Sensory integration Software Sound Tape Recording - methods Veterinary Sciences vocalisation Vocalization, Animal |
| title | Automatic recording of individual oestrus vocalisation in group-housed dairy cattle: development of a cattle call monitor |
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