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Klingström T, Zonabend König E, Zwane AA. Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping. Brief Funct Genomics 2024:elae032. [PMID: 39158344 DOI: 10.1093/bfgp/elae032] [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: 02/21/2024] [Revised: 07/19/2024] [Accepted: 08/01/2024] [Indexed: 08/20/2024] Open
Abstract
Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.
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Affiliation(s)
- Tomas Klingström
- Department of Animal Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | | | - Avhashoni Agnes Zwane
- Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa
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Massari JM, Moura DJD, Nääs IDA, Pereira DF, Oliveira SRDM, Branco T, Barros JDSG. Sequential Behavior of Broiler Chickens in Enriched Environments under Varying Thermal Conditions Using the Generalized Sequential Pattern Algorithm: A Proof of Concept. Animals (Basel) 2024; 14:2010. [PMID: 38998121 PMCID: PMC11240803 DOI: 10.3390/ani14132010] [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: 06/09/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/14/2024] Open
Abstract
Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, the potential of which has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective here was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through a proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at the comfort temperature showed high motivation to perform the behaviors of preening (P), foraging (F), lying down (Ld), eating (E), and walking (W); the sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}>; and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally "Ll" is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.
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Affiliation(s)
- Juliana Maria Massari
- School of Agricultural Engineering, State University of Campinas, 501 Candido Rondon Avenue, Campinas 13083-875, SP, Brazil
| | - Daniella Jorge de Moura
- School of Agricultural Engineering, State University of Campinas, 501 Candido Rondon Avenue, Campinas 13083-875, SP, Brazil
| | - Irenilza de Alencar Nääs
- Graduate Program in Production Engineering, Paulista University, R. Dr. Bacelar 1212, São Paulo 04026-002, SP, Brazil
| | - Danilo Florentino Pereira
- School of Sciences and Engineering, Department of Management, Development and Technology, São Paulo State University (UNESP), Tupã 17602-496, SP, Brazil
| | - Stanley Robson de Medeiros Oliveira
- School of Agricultural Engineering, State University of Campinas, 501 Candido Rondon Avenue, Campinas 13083-875, SP, Brazil
- Embrapa Digital Agriculture, State University of Campinas, 209 Andre Tosello, Campinas 13083-886, SP, Brazil
| | - Tatiane Branco
- Agricultural and Forestry/Animal Scientist Analyst, Secretariat of Agriculture, Livestock, Sustainable Production, and Irrigation-SEAPI, 1384 Getulio Vargas Avenue, Porto Alegre 90820-150, RS, Brazil
| | - Juliana de Souza Granja Barros
- School of Agricultural Engineering, State University of Campinas, 501 Candido Rondon Avenue, Campinas 13083-875, SP, Brazil
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Kaewbang J, Lohanawakul J, Ketnuam N, Prapakornmano K, Khamta P, Raza A, Swangchan-Uthai T, Makararpong D, Inchaisri C. Smart sensors in Thai dairy reproduction: A case study. Vet World 2024; 17:1251-1258. [PMID: 39077443 PMCID: PMC11283598 DOI: 10.14202/vetworld.2024.1251-1258] [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: 03/14/2024] [Accepted: 05/15/2024] [Indexed: 07/31/2024] Open
Abstract
Background and Aim Movement activity sensors are known for their potential to boost the reproductive performance of dairy cows. This study evaluated the effectiveness of these sensors on three Thai dairy farms (MK, NF, and CC), each using different sensor brands. We focused on reproductive performance at these farms and expanded our evaluation to include farmer satisfaction with sensor technology on five farms (MK, NF, CC, AP, and IP), allowing for a thorough analysis of both operational outcomes and user feedback. Materials and Methods A total of 298 lactation records and interviewing five experienced farm owners with over a year of sensor usage were our research methods. To measure the effect on the first service timing and post-parturition pregnancy rates, Cox regression models were utilized for sensor usage. Results Biosensors' implementation enhanced data precision while quickening the first service within 100 days and pregnancy within 200 days. The MK and NF farms showed significant progress. Within 100 and 200 days post-implementation, the overall improvement was 30%-34% in the first service rate and 39%-67% in the conception rate across all assessed farms. Farmers acknowledged improved reproductive performance from the sensors, overcoming language barriers. Conclusion The study highlighted the advantages of using movement activity sensors in enhancing both cattle reproductive success and farmers' satisfaction on Thai dairy farms. These sensors led to more accurate management decisions, increasing overall farm productivity.
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Affiliation(s)
- Jirayus Kaewbang
- Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
- Chulalongkorn Animal Hospital, Faculty of Veterinary Science, Chulalongkorn University, 73000 Nakhonpathom Province, Thailand
| | - Jidapa Lohanawakul
- Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
| | - Napat Ketnuam
- Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
| | - Kachapas Prapakornmano
- Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
| | - Pongsanan Khamta
- Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
| | - Aqeel Raza
- International Graduate Program of Veterinary Science and Technology, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
| | - Theerawat Swangchan-Uthai
- CU-Animal Fertility Research Unit, Department of Veterinary Obstetrics and Gynecology, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
| | - Davids Makararpong
- Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
- Senovate AI Co., Ltd., 10240 Bangkok, Thailand
| | - Chaidate Inchaisri
- Research Unit of Data Innovation for Livestock, Department of Veterinary Medicine, Faculty of Veterinary Science, Chulalongkorn University, 10330 Bangkok, Thailand
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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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Ha S, Kang S, Jung M, Jeon E, Hwang S, Lee J, Kim J, Bae YC, Park J, Kim UH. Retrospective study using biosensor data of a milking Holstein cow with jejunal haemorrhage syndrome. VET MED-CZECH 2023; 68:375-383. [PMID: 37981941 PMCID: PMC10646544 DOI: 10.17221/73/2023-vetmed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/21/2023] [Indexed: 11/21/2023] Open
Abstract
Jejunal haemorrhage syndrome (JHS) is a sporadic and fatal enterotoxaemic disease in dairy cows associated with acute development and poor prognosis despite treatment. A 5-year-old Holstein cow with no reported pregnancy, three calving numbers, and 303 days in milk presented with hypothermia, discomfort, and inappetence. Anaemia, dehydration, faeces with blood clots, and absence of rumen and bowel movements were observed. We identified the presence of neutrophilia, hyperglycaemia, hypoproteinaemia, azotaemia, hyperlactatemia, hypocalcaemia, hypermagnesemia, hypokalaemia, and hypochloraemia through blood analyses. Necropsy and histopathologic examination revealed a dilated bluish-purple jejunum, blood clots within the jejunum, neutrophil infiltration into the submucosa of the jejunum, and vascular necrosis. Retrospective examination revealed extraordinary patterns of rumination time, activity, rumen mobility, and rumen temperature using biosensors and decreased milk yield. The abnormalities in the affected cow were detected before recognition by farm workers. To the best of our knowledge, this is the first report to examine data from biosensors in a cow with JHS. Our findings suggest that using biometric data may help understand the development of JHS.
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Affiliation(s)
- Seungmin Ha
- National Institute of Animal Science, Rural Development Administration, Cheonan-si, Chungcheongnam-do, Republic of Korea
| | - Seogjin Kang
- National Institute of Animal Science, Rural Development Administration, Cheonan-si, Chungcheongnam-do, Republic of Korea
| | - Mooyoung Jung
- National Institute of Animal Science, Rural Development Administration, Cheonan-si, Chungcheongnam-do, Republic of Korea
| | - Eunjeong Jeon
- National Institute of Animal Science, Rural Development Administration, Cheonan-si, Chungcheongnam-do, Republic of Korea
| | - Seongsoo Hwang
- National Institute of Animal Science, Rural Development Administration, Cheonan-si, Chungcheongnam-do, Republic of Korea
| | - Jihwan Lee
- National Institute of Animal Science, Rural Development Administration, Cheonan-si, Chungcheongnam-do, Republic of Korea
| | - Jongho Kim
- Pathologic Diagnostic Laboratory, Animal Disease Diagnostic Division, Animal and Plant Quarantine Agency, Gimcheon-si, Gyeongsangbuk-do, Republic of Korea
| | - You-Chan Bae
- Pathologic Diagnostic Laboratory, Animal Disease Diagnostic Division, Animal and Plant Quarantine Agency, Gimcheon-si, Gyeongsangbuk-do, Republic of Korea
| | - Jinho Park
- College of Veterinary Medicine, Jeonbuk National University, Iksan-si, Jeollabuk-do, Republic of Korea
| | - Ui-Hyung Kim
- National Institute of Animal Science, Rural Development Administration, Cheonan-si, Chungcheongnam-do, Republic of Korea
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Almansour AM, Alhadlaq MA, Alzahrani KO, Mukhtar LE, Alharbi AL, Alajel SM. The Silent Threat: Antimicrobial-Resistant Pathogens in Food-Producing Animals and Their Impact on Public Health. Microorganisms 2023; 11:2127. [PMID: 37763971 PMCID: PMC10537193 DOI: 10.3390/microorganisms11092127] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/10/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023] Open
Abstract
The emergence of antimicrobial resistance (AMR) is a global health problem without geographic boundaries. This increases the risk of complications and, thus, makes it harder to treat infections, which can result in higher healthcare costs and a greater number of deaths. Antimicrobials are often used to treat infections from pathogens in food-producing animals, making them a potential source of AMR. Overuse and misuse of these drugs in animal agriculture can lead to the development of AMR bacteria, which can then be transmitted to humans through contaminated food or direct contact. It is therefore essential to take multifaceted, comprehensive, and integrated measures, following the One Health approach. To address this issue, many countries have implemented regulations to limit antimicrobial use. To our knowledge, there are previous studies based on AMR in food-producing animals; however, this paper adds novelty related to the AMR pathogens in livestock, as we include the recent publications of this field worldwide. In this work, we aim to describe the most critical and high-risk AMR pathogens among food-producing animals, as a worldwide health problem. We also focus on the dissemination of AMR genes in livestock, as well as its consequences in animals and humans, and future strategies to tackle this threat.
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Affiliation(s)
- Ayidh M. Almansour
- Molecular Biology Division, Reference Laboratory for Microbiology, Executive Department of Reference Laboratories, Research and Laboratories Sector, Saudi Food and Drug Authority (SFDA), Riyadh 11671, Saudi Arabia; (M.A.A.); (K.O.A.); (A.L.A.)
| | - Meshari A. Alhadlaq
- Molecular Biology Division, Reference Laboratory for Microbiology, Executive Department of Reference Laboratories, Research and Laboratories Sector, Saudi Food and Drug Authority (SFDA), Riyadh 11671, Saudi Arabia; (M.A.A.); (K.O.A.); (A.L.A.)
| | - Khaloud O. Alzahrani
- Molecular Biology Division, Reference Laboratory for Microbiology, Executive Department of Reference Laboratories, Research and Laboratories Sector, Saudi Food and Drug Authority (SFDA), Riyadh 11671, Saudi Arabia; (M.A.A.); (K.O.A.); (A.L.A.)
| | - Lenah E. Mukhtar
- Antimicrobial Resistance Division, Reference Laboratory for Microbiology, Executive Department of Reference Laboratories, Research and Laboratories Sector, Saudi Food and Drug Authority (SFDA), Riyadh 11671, Saudi Arabia;
| | - Abdulmohsen L. Alharbi
- Molecular Biology Division, Reference Laboratory for Microbiology, Executive Department of Reference Laboratories, Research and Laboratories Sector, Saudi Food and Drug Authority (SFDA), Riyadh 11671, Saudi Arabia; (M.A.A.); (K.O.A.); (A.L.A.)
| | - Sulaiman M. Alajel
- Reference Laboratory for Microbiology, Executive Department of Reference Laboratories, Research and Laboratories Sector, Saudi Food and Drug Authority (SFDA), Riyadh 11671, Saudi Arabia;
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Neethirajan S. Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. SENSORS (BASEL, SWITZERLAND) 2023; 23:7045. [PMID: 37631580 PMCID: PMC10458494 DOI: 10.3390/s23167045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023]
Abstract
This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in 'shy feeder' cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector.
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Affiliation(s)
- Suresh Neethirajan
- Department of Animal Science and Aquaculture, Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
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Heins B, Pereira G, Sharpe K. Precision technologies to improve dairy grazing systems. JDS COMMUNICATIONS 2023; 4:318-323. [PMID: 37521056 PMCID: PMC10382829 DOI: 10.3168/jdsc.2022-0308] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 01/03/2023] [Indexed: 08/01/2023]
Abstract
Pasture-based dairy herds continue to grow around the world as demand increases for sustainable farming practices. Grazing dairy farmers may benefit from the utilization of precision dairy technologies because these technologies have the potential to improve animal welfare, increase farm efficiency, and reduce costs. Precision dairy technologies have provided novel information about activity, rumination, and grazing behavior of various breeds in pasture-based systems. Previous research with wearable technologies has indicated that rumination, eating, and no activity have moderate to high correlations (r = 0.65 to 0.88) with visual observation; however, activity may be difficult to record in grazing herds. However, many grazing dairy farmers around the world are using activity monitors with generally positive success. Grazing is a complex behavior to define because cows may walk to an area and stop to eat or continuously walk and take bites of grass from the pasture. Wearable technologies can detect whether a cow is grazing with reasonable accuracy. However, the challenge is to determine pasture intake as bite rate and bite size because these can vary as the pasture is grazed to a low residual height. Nevertheless, grazing behavior data collected with wearable technologies was highly correlated (r = 0.92 to 0.95) with visual observations. Grazing is a behavior that should continue to be explored, especially with precision dairy technologies. As healthy and productive pastures are integral to grazing systems, accurate forage biomass measurements can improve efficiency and production of pastured dairy cows. However, few farms use technology to determine forage availability. Therefore, using dairy technologies to monitor forage dry matter from pasture may provide a potential benefit for grazing-based dairy farms. Current satellite technology with the normalized difference vegetation index and electronic rising plate meters may provide new technologies for farms to monitor forage biomass and fine-tune grazing within pastures. In the future, pasture-based dairy farms may rely on virtual fencing, drones to detect animal health issues and forage availability, and autonomous vehicles to move cattle and to detect weeds on pasture.
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Affiliation(s)
- B.J. Heins
- West Central Research and Outreach Center, University of Minnesota, Morris 56267
| | - G.M. Pereira
- Maine Food and Agriculture Center, University of Maine, Orono 04469
| | - K.T. Sharpe
- West Central Research and Outreach Center, University of Minnesota, Morris 56267
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Fuentes A, Han S, Nasir MF, Park J, Yoon S, Park DS. Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning. Animals (Basel) 2023; 13:2020. [PMID: 37370530 PMCID: PMC10295180 DOI: 10.3390/ani13122020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/09/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Cattle behavior recognition is essential for monitoring their health and welfare. Existing techniques for behavior recognition in closed barns typically rely on direct observation to detect changes using wearable devices or surveillance cameras. While promising progress has been made in this field, monitoring individual cattle, especially those with similar visual characteristics, remains challenging due to numerous factors such as occlusion, scale variations, and pose changes. Accurate and consistent individual identification over time is therefore essential to overcome these challenges. To address this issue, this paper introduces an approach for multiview monitoring of individual cattle behavior based on action recognition using video data. The proposed system takes an image sequence as input and utilizes a detector to identify hierarchical actions categorized as part and individual actions. These regions of interest are then inputted into a tracking and identification mechanism, enabling the system to continuously track each individual in the scene and assign them a unique identification number. By implementing this approach, cattle behavior is continuously monitored, and statistical analysis is conducted to assess changes in behavior in the time domain. The effectiveness of the proposed framework is demonstrated through quantitative and qualitative experimental results obtained from our Hanwoo cattle video database. Overall, this study tackles the challenges encountered in real farm indoor scenarios, capturing spatiotemporal information and enabling automatic recognition of cattle behavior for precision livestock farming.
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Affiliation(s)
- Alvaro Fuentes
- Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; (A.F.)
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Shujie Han
- Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; (A.F.)
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Muhammad Fahad Nasir
- Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; (A.F.)
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Jongbin Park
- Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; (A.F.)
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Sook Yoon
- Department of Computer Engineering, Mokpo National University, Muan 58554, Republic of Korea
| | - Dong Sun Park
- Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea; (A.F.)
- Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Republic of Korea
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Leliveld LMC, Lovarelli D, Finzi A, Riva E, Provolo G. Effects of cow reproductive status, parity and lactation stage on behaviour and heavy breathing indications of a commercial accelerometer during hot weather conditions. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023:10.1007/s00484-023-02496-2. [PMID: 37246987 DOI: 10.1007/s00484-023-02496-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/04/2023] [Accepted: 05/12/2023] [Indexed: 05/30/2023]
Abstract
Heat stress presents one of the most urgent challenges to modern dairy farming, having major detrimental impacts on cow welfare, health, and production. Understanding the effect of cow factors (reproductive status, parity, and lactation stage) on the physiological and behavioural response to hot weather conditions is essential for the accurate detection and practical application of heat mitigation strategies. To study this, collars with commercial accelerometer-based sensors were fitted on 48 lactation dairy cows to record behaviour and heavy breathing from late spring to late summer. The temperature-humidity index (THI) was calculated from measurements of 8 barn sensors. We found that, above a THI of 84, cows in advanced pregnancy (>90 days) spent more time breathing heavily and less time eating and in low activity than other cows, while cows in early pregnancy (≤90 days) spent less time breathing heavily, more time eating and in low activity. Cows with 3+ lactations showed less time breathing heavily and in high activity and more time ruminating and in low activity than cows with fewer lactations. Although lactation stage interacted significantly with THI on time spent breathing heavily, ruminating, eating, and in low activity, there was no clear indication at which lactation stage cows were more sensitive to heat. These findings show that cow factors affect the cow's physiological and behavioural response to heat, which could be used to provide group-specific heat abatement strategies, thereby improving heat stress management.
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Affiliation(s)
- Lisette M C Leliveld
- Department of Agricultural and Environmental Sciences, University of Milan, via G. Celoria 2, 20133, Milan, Italy.
| | - Daniela Lovarelli
- Department of Environmental Science and Policy, University of Milan, via G. Celoria 2, 20133, Milan, Italy
| | - Alberto Finzi
- Department of Agricultural and Environmental Sciences, University of Milan, via G. Celoria 2, 20133, Milan, Italy
| | - Elisabetta Riva
- Department of Agricultural and Environmental Sciences, University of Milan, via G. Celoria 2, 20133, Milan, Italy
| | - Giorgio Provolo
- Department of Agricultural and Environmental Sciences, University of Milan, via G. Celoria 2, 20133, Milan, Italy
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Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases. Animals (Basel) 2023; 13:ani13050780. [PMID: 36899637 PMCID: PMC10000156 DOI: 10.3390/ani13050780] [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: 01/11/2023] [Revised: 02/19/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.
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Mohteshamuddin K, Hamdan L, Estrada LM, Nadeem MF, Kim H, Lee S, Kang H, Hamad ME, Dakheel A. A pilot study on the hemato-biochemical parameters of cattle administered with advanced healthcare bio-capsules connected through a customized long-range network in the United Arab Emirates. SENSING AND BIO-SENSING RESEARCH 2022. [DOI: 10.1016/j.sbsr.2022.100530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Stachowicz J, Nasser R, Adrion F, Umstätter C. Can we detect patterns in behavioral time series of cows using cluster analysis? J Dairy Sci 2022; 105:9971-9981. [DOI: 10.3168/jds.2022-22140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
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Recent Advances in Smart Farming. Animals (Basel) 2022; 12:ani12060705. [PMID: 35327101 PMCID: PMC8944814 DOI: 10.3390/ani12060705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 02/04/2023] Open
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