The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle
This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30–42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlam...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/13/7/1180 |
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| author | Lena Lemmens Katharina Schodl Birgit Fuerst-Waltl Hermann Schwarzenbacher Christa Egger-Danner Kristina Linke Marlene Suntinger Mary Phelan Martin Mayerhofer Franz Steininger Franz Papst Lorenz Maurer Johann Kofler |
| author_facet | Lena Lemmens Katharina Schodl Birgit Fuerst-Waltl Hermann Schwarzenbacher Christa Egger-Danner Kristina Linke Marlene Suntinger Mary Phelan Martin Mayerhofer Franz Steininger Franz Papst Lorenz Maurer Johann Kofler |
| author_sort | Lena Lemmens |
| collection | DOAJ |
| description | This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30–42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data. |
| first_indexed | 2024-03-11T05:44:13Z |
| format | Article |
| id | doaj.art-d3e133ea069c43078751517f95ce01f3 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-11T05:44:13Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-d3e133ea069c43078751517f95ce01f32023-11-17T16:13:53ZengMDPI AGAnimals2076-26152023-03-01137118010.3390/ani13071180The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy CattleLena Lemmens0Katharina Schodl1Birgit Fuerst-Waltl2Hermann Schwarzenbacher3Christa Egger-Danner4Kristina Linke5Marlene Suntinger6Mary Phelan7Martin Mayerhofer8Franz Steininger9Franz Papst10Lorenz Maurer11Johann Kofler12Department of Farm Animals and Veterinary Public Health, University Clinic for Ruminants, University of Veterinary Medicine Vienna, 1210 Vienna, AustriaDepartment of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, AustriaDepartment of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaMSD Animal Health, D18X5K7 Dublin, IrelandZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, AustriaInstitute of Technical Informatics, Graz University of Technology, 8010 Graz, AustriaDepartment of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, AustriaDepartment of Farm Animals and Veterinary Public Health, University Clinic for Ruminants, University of Veterinary Medicine Vienna, 1210 Vienna, AustriaThis study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30–42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.https://www.mdpi.com/2076-2615/13/7/1180lamenessdairy cattleautomated monitoring sensorsautomated milking systemlocomotion scoreclaw-position score |
| spellingShingle | Lena Lemmens Katharina Schodl Birgit Fuerst-Waltl Hermann Schwarzenbacher Christa Egger-Danner Kristina Linke Marlene Suntinger Mary Phelan Martin Mayerhofer Franz Steininger Franz Papst Lorenz Maurer Johann Kofler The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle Animals lameness dairy cattle automated monitoring sensors automated milking system locomotion score claw-position score |
| title | The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle |
| title_full | The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle |
| title_fullStr | The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle |
| title_full_unstemmed | The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle |
| title_short | The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle |
| title_sort | combined use of automated milking system and sensor data to improve detection of mild lameness in dairy cattle |
| topic | lameness dairy cattle automated monitoring sensors automated milking system locomotion score claw-position score |
| url | https://www.mdpi.com/2076-2615/13/7/1180 |
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