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de Oliveira FM, Ferraz GAES, André ALG, Santana LS, Norton T, Ferraz PFP. Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis. Animals (Basel) 2024; 14:1832. [PMID: 38929450 PMCID: PMC11201094 DOI: 10.3390/ani14121832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/03/2024] [Accepted: 06/15/2024] [Indexed: 06/28/2024] Open
Abstract
The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are "Computers and Electronics in Agriculture" and "Journal of Dairy Science". It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production.
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Affiliation(s)
- Franck Morais de Oliveira
- Department of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil; (F.M.d.O.); (P.F.P.F.)
| | - Gabriel Araújo e Silva Ferraz
- Department of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil; (F.M.d.O.); (P.F.P.F.)
| | | | - Lucas Santos Santana
- Department of Agricultural and Environmental Engineering (EEA), Institute of Agricultural Sciences (ICA), Federal University of Vales Jequitinhonha and Mucuri—Campus Unaí, Avenida Universitária, nº 1.000, B. Universitários, Unai 38610-000, Brazil;
| | - Tomas Norton
- M3-BIORES-Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium;
| | - Patrícia Ferreira Ponciano Ferraz
- Department of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil; (F.M.d.O.); (P.F.P.F.)
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Li J, Liu Y, Zheng W, Chen X, Ma Y, Guo L. Monitoring Cattle Ruminating Behavior Based on an Improved Keypoint Detection Model. Animals (Basel) 2024; 14:1791. [PMID: 38929410 PMCID: PMC11200719 DOI: 10.3390/ani14121791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 06/09/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle's rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle's rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming.
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Affiliation(s)
- Jinxing Li
- College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China; (J.L.); (Y.L.)
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100080, China
- Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China
- Ministry of Education Engineering Research Centre for Intelligent Agriculture, Urumqi 830052, China
| | - Yanhong Liu
- College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China; (J.L.); (Y.L.)
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100080, China
- Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China
- Ministry of Education Engineering Research Centre for Intelligent Agriculture, Urumqi 830052, China
| | - Wenxin Zheng
- Institute of Animal Husbandry Quality Standards, Xinjiang Academy of Animal Science, Urumqi 830011, China; (W.Z.); (X.C.)
| | - Xinwen Chen
- Institute of Animal Husbandry Quality Standards, Xinjiang Academy of Animal Science, Urumqi 830011, China; (W.Z.); (X.C.)
| | - Yabin Ma
- Hebei Animal Husbandry and Breeding Work Station, Shijiazhuang 050049, China
| | - Leifeng Guo
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100080, China
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Simoni A, König F, Weimar K, Hancock A, Wunderlich C, Klawitter M, Breuer T, Drillich M, Iwersen M. Evaluation of sensor-based health monitoring in dairy cows: Exploiting rumination times for health alerts around parturition. J Dairy Sci 2024:S0022-0302(24)00632-5. [PMID: 38554821 DOI: 10.3168/jds.2023-24313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 02/25/2024] [Indexed: 04/02/2024]
Abstract
The use of sensor-based measures of rumination time as a parameter for early disease detection has received significant attention in scientific research. This study aimed to assess the accuracy of health alerts triggered by a sensor-based accelerometer system within 2 different management strategies on a commercial dairy farm. Multiparous Holstein cows were enrolled during the dry-off period and randomly allocated to conventional (CON) or sensor-based (SEN) management groups at calving. All cows were monitored for disorders for a minimum of 10 DIM following standardized operating procedures (SOPs). The CON group (n = 199) followed an established monitoring protocol on the farm. The health alerts of this group were not available during the study but were later included in the analysis. The SEN group (n = 197) was only investigated when the sensor system triggered a health alert, and a more intensive monitoring approach according to the SOPs was implemented. To analyze the efficiency of the health alerts in detecting disorders, the sensitivity (SE) and specificity (SP) of health alerts were determined for the CON group. In addition, all cows were divided into 3 subgroups based on the status of the health alerts and their health status, to retrospectively compare the course of rumination time. Most health alerts (87%, n = 217) occurred on DIM 1. For the confirmation of diagnoses, health alerts showed SE and SP levels of 71% and 47% for CON cows. In SEN cows, a SE of 71% and 75% and SP of 48% and 43% were found for the detection of ketosis and hypocalcemia, respectively. The rumination time of the subgroups was affected by DIM and the interaction between DIM and the status of health alert and health condition.
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Affiliation(s)
- A Simoni
- Clinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine, 1210 Vienna, Austria
| | - F König
- Clinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine, 1210 Vienna, Austria
| | - K Weimar
- Clinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine, 1210 Vienna, Austria
| | - A Hancock
- Zoetis International, Dublin, Ireland
| | | | | | - T Breuer
- Zoetis Deutschland GmbH, Berlin, Germany
| | - M Drillich
- Unit for Reproduction Medicine and Udder Health, Clinic for Farm Animals, Faculty of Veterinary Medicine, Freie Universität Berlin, 14163 Berlin, Germany
| | - M Iwersen
- Clinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine, 1210 Vienna, Austria.
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Schneidewind SJ, Al Merestani MR, Schmidt S, Schmidt T, Thöne-Reineke C, Wiegard M. Rumination Detection in Sheep: A Systematic Review of Sensor-Based Approaches. Animals (Basel) 2023; 13:3756. [PMID: 38136794 PMCID: PMC10740880 DOI: 10.3390/ani13243756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 12/24/2023] Open
Abstract
The use of sensors to analyze behavior in sheep has gained increasing attention in scientific research. This systematic review aims to provide an overview of the sensors developed and used to detect rumination behavior in sheep in scientific research. Moreover, this overview provides details of the sensors that are currently commercially available and describes their suitability for sheep based on the information provided in the literature found. Furthermore, this overview lists the best sensor performances in terms of achieved accuracy, sensitivity, precision, and specificity in rumination detection, detailing, when applicable, the sensor position and epoch settings that were used to achieve the best results. Challenges and areas for future research and development are also identified. A search strategy was implemented in the databases PubMed, Web of Science, and Livivo, yielding a total of 935 articles. After reviewing the summaries of 57 articles remaining following filtration (exclusion) of repeated and unsuitable articles, 17 articles fully met the pre-established criteria (peer-reviewed; published between 2012 and 2023 in English or German; with a particular focus on sensors detecting rumination in sheep) and were included in this review. The guidelines outlined in the PRISMA 2020 methodology were followed. The results indicate that sensor-based systems have been utilized to monitor and analyze rumination behavior, among other behaviors. Notably, none of the sensors identified in this review were specifically designed for sheep. In order to meet the specific needs of sheep, a customized sensor solution is necessary. Additionally, further investigation of the optimal sensor position and epoch settings is necessary. Implications: The utilization of such sensors has significant implications for improving sheep welfare and enhancing our knowledge of their behavior in various contexts.
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Affiliation(s)
- Stephanie Janet Schneidewind
- Institute of Animal Welfare, Animal Behaviour and Laboratory Animal Science, Freie Universität Berlin, 14163 Berlin, Germany; (C.T.-R.); (M.W.)
| | - Mohamed Rabih Al Merestani
- Department of Biosystems Engineering, Albrecht Daniel Thaer Institute for Agriculture, Humboldt University of Berlin, 10117 Berlin, Germany
| | | | | | - Christa Thöne-Reineke
- Institute of Animal Welfare, Animal Behaviour and Laboratory Animal Science, Freie Universität Berlin, 14163 Berlin, Germany; (C.T.-R.); (M.W.)
| | - Mechthild Wiegard
- Institute of Animal Welfare, Animal Behaviour and Laboratory Animal Science, Freie Universität Berlin, 14163 Berlin, Germany; (C.T.-R.); (M.W.)
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