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Lemmens L, Schodl K, Fuerst-Waltl B, Schwarzenbacher H, Egger-Danner C, Linke K, Suntinger M, Phelan M, Mayerhofer M, Steininger F, Papst F, Maurer L, Kofler J. The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle. Animals (Basel) 2023; 13:ani13071180. [PMID: 37048436 PMCID: PMC10093521 DOI: 10.3390/ani13071180] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/23/2023] [Accepted: 03/25/2023] [Indexed: 03/30/2023] Open
Abstract
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.
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Affiliation(s)
- Lena Lemmens
- Department of Farm Animals and Veterinary Public Health, University Clinic for Ruminants, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
| | - Katharina Schodl
- Department of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, Austria
| | - Birgit Fuerst-Waltl
- Department of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, Austria
| | | | | | - Kristina Linke
- ZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, Austria
| | | | | | | | | | - Franz Papst
- Institute of Technical Informatics, Graz University of Technology, 8010 Graz, Austria
- Austria and Complexity Science Hub Vienna, 1080 Vienna, Austria
| | - Lorenz Maurer
- Department of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, Austria
| | - Johann Kofler
- Department of Farm Animals and Veterinary Public Health, University Clinic for Ruminants, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
- Correspondence:
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Bresolin T, Ferreira R, Reyes F, Van Os J, Dórea J. Assessing optimal frequency for image acquisition in computer vision systems developed to monitor feeding behavior of group-housed Holstein heifers. J Dairy Sci 2022; 106:664-675. [DOI: 10.3168/jds.2022-22138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/02/2022] [Indexed: 11/05/2022]
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Shadpour S, Chud TC, Hailemariam D, Oliveira HR, Plastow G, Stothard P, Lassen J, Baldwin R, Miglior F, Baes CF, Tulpan D, Schenkel FS. Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. J Dairy Sci 2022; 105:8257-8271. [DOI: 10.3168/jds.2021-21297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 05/31/2022] [Indexed: 11/19/2022]
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Research on Students' Mental Health Based on Data Mining Algorithms. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:1382559. [PMID: 34733450 PMCID: PMC8560244 DOI: 10.1155/2021/1382559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/14/2021] [Accepted: 10/08/2021] [Indexed: 11/24/2022]
Abstract
With the diversification and rapid development of society, people's living conditions, learning and friendship conditions, and employment conditions are facing increasing pressure, which greatly challenges people's psychological endurance. Therefore, strengthening the mental health education of students has become an urgent need of society and a hot issue of common concern. In order to solve the problems of high misjudgment rate and low work efficiency in the current mental health intelligence evaluation process, a mental health intelligence evaluation system based on a joint optimization algorithm is proposed. The joint optimization algorithm consists of an improved decision tree algorithm and an improved ANN algorithm. First, analyze the current research status of mental health intelligence evaluation, and construct the framework of mental health intelligence evaluation system; then collect mental health intelligence evaluation data based on data mining, use joint learning algorithm to analyze and classify mental health intelligence evaluation data, and obtain mental health intelligence evaluation results. Finally, through specific simulation experiments, the feasibility and superiority of the mental health intelligent evaluation system are analyzed. The results show that the system in the article overcomes the shortcomings of the existing mental health intelligence evaluation system, improves the accuracy of mental health intelligence evaluation, and improves the efficiency of mental health intelligence evaluation. It has good system stability and can meet the actual current situation, which are requirements for mental health intelligence evaluation.
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Lasser J, Matzhold C, Egger-Danner C, Fuerst-Waltl B, Steininger F, Wittek T, Klimek P. Integrating diverse data sources to predict disease risk in dairy cattle-a machine learning approach. J Anim Sci 2021; 99:6400292. [PMID: 34662372 DOI: 10.1093/jas/skab294] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 10/15/2021] [Indexed: 12/25/2022] Open
Abstract
Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making them impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia), and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1 = 0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, such as housing, nutrition, or climate, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Our findings pave the way toward data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.
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Affiliation(s)
- Jana Lasser
- Section for Science of Complex Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.,Institute for Interactive Systems and Data Science, Graz University of Technology, 8010 Graz, Austria.,Complexity Science Hub Vienna, 1080 Vienna, Austria
| | - Caspar Matzhold
- Section for Science of Complex Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.,Complexity Science Hub Vienna, 1080 Vienna, Austria
| | | | - Birgit Fuerst-Waltl
- Division of Livestock Sciences, University of Natural Resources and Life Sciences, 1180 Vienna, Austria
| | | | - Thomas Wittek
- Vetmeduni Vienna, University Clinic for Ruminants, 1210 Vienna, Austria
| | - Peter Klimek
- Section for Science of Complex Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.,Complexity Science Hub Vienna, 1080 Vienna, Austria
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