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Rebez EB, Sejian V, Silpa MV, Kalaignazhal G, Thirunavukkarasu D, Devaraj C, Nikhil KT, Ninan J, Sahoo A, Lacetera N, Dunshea FR. Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock. SENSORS (BASEL, SWITZERLAND) 2024; 24:5890. [PMID: 39338635 PMCID: PMC11435989 DOI: 10.3390/s24185890] [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: 07/29/2024] [Revised: 08/24/2024] [Accepted: 09/09/2024] [Indexed: 09/30/2024]
Abstract
Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital age, adoption of advanced and developing Artificial Intelligence (AI) technologies is gaining traction for efficient heat stress management. AI has widely penetrated the climate sensitive ruminant livestock sector due to its promising and plausible scope in assessing production risks and the climate resilience of ruminant livestock. Significant improvement has been achieved alongside the adoption of novel AI algorithms to evaluate the performance of ruminant livestock. These AI-powered tools have the robustness and competence to expand the evaluation of animal performance and help in minimising the production losses associated with heat stress in ruminant livestock. Advanced heat stress management through automated monitoring of heat stress in ruminant livestock based on behaviour, physiology and animal health responses have been widely accepted due to the evolution of technologies like machine learning (ML), neural networks and deep learning (DL). The AI-enabled tools involving automated data collection, pre-processing, data wrangling, development of appropriate algorithms, and deployment of models assist the livestock producers in decision-making based on real-time monitoring and act as early-stage warning systems to forecast disease dynamics based on prediction models. Due to the convincing performance, precision, and accuracy of AI models, the climate-smart livestock production imbibes AI technologies for scaled use in the successful reducing of heat stress in ruminant livestock, thereby ensuring sustainable livestock production and safeguarding the global economy.
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Affiliation(s)
- Ebenezer Binuni Rebez
- Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India
- ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India
| | - Veerasamy Sejian
- Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India
- ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India
| | | | - Gajendirane Kalaignazhal
- Department of Animal Breeding and Genetics, College of Veterinary Science and Animal Husbandry, Odisha University of Agriculture and Technology, Bhubaneshwar 751003, India
| | - Duraisamy Thirunavukkarasu
- Department of Veterinary and Animal Husbandry Extension Education, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University, Namakkal 637002, India
| | - Chinnasamy Devaraj
- ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India
| | - Kumar Tej Nikhil
- Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India
| | - Jacob Ninan
- Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India
| | - Artabandhu Sahoo
- ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India
| | - Nicola Lacetera
- Department of Agriculture and Forest Sciences, University of Tuscia, 01100 Viterbo, Italy
| | - Frank Rowland Dunshea
- School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Parkville, Melbourne, VIC 3010, Australia
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Hassoun A, Garcia-Garcia G, Trollman H, Jagtap S, Parra-López C, Cropotova J, Bhat Z, Centobelli P, Aït-Kaddour A. Birth of dairy 4.0: Opportunities and challenges in adoption of fourth industrial revolution technologies in the production of milk and its derivatives. Curr Res Food Sci 2023; 7:100535. [PMID: 37448632 PMCID: PMC10336415 DOI: 10.1016/j.crfs.2023.100535] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/11/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Embracing innovation and emerging technologies is becoming increasingly important to address the current global challenges facing many food industry sectors, including the dairy industry. Growing literature shows that the adoption of technologies of the fourth industrial revolution (named Industry 4.0) has promising potential to bring about breakthroughs and new insights and unlock advancement opportunities in many areas of the food manufacturing sector. This article discusses the current knowledge and recent trends and progress on the application of Industry 4.0 innovations in the dairy industry. First, the "Dairy 4.0" concept, inspired by Industry 4.0, is introduced and its enabling technologies are determined. Second, relevant examples of the use of Dairy 4.0 technologies in milk and its derived products are presented. Finally, conclusions and future perspectives are given. The results revealed that robotics, 3D printing, Artificial Intelligence, the Internet of Things, Big Data, and blockchain are the main enabling technologies of Dairy 4.0. These advanced technologies are being progressively adopted in the dairy sector, from farm to table, making significant and profound changes in the production of milk, cheese, and other dairy products. It is expected that, in the near future, new digital innovations will emerge, and greater implementations of Dairy 4.0 technologies is likely to be achieved, leading to more automation and optimization of this dynamic food sector.
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Affiliation(s)
- Abdo Hassoun
- Univ. Littoral Côte D’Opale, UMRt 1158 BioEcoAgro, USC ANSES, INRAe, Univ. Artois, Univ. Lille, Univ. Picardie Jules Verne, Univ. Liège, Junia, F-62200, Boulogne-sur-Mer, France
- Sustainable AgriFoodtech Innovation & Research (SAFIR), F-62000, Arras, France
| | - Guillermo Garcia-Garcia
- Department of Agrifood System Economics, Centre ‘Camino de Purchil’, Institute of Agricultural and Fisheries Research and Training (IFAPA), P.O. Box 2027, 18080, Granada, Spain
| | - Hana Trollman
- School of Business, University of Leicester, Leicester, LE2 1RQ, UK
| | - Sandeep Jagtap
- Sustainable Manufacturing Systems Centre, School of Aerospace, Transport & Manufacturing, Cranfield University, Cranfield, MK43 0AL, UK
| | - Carlos Parra-López
- Department of Agrifood System Economics, Centre ‘Camino de Purchil’, Institute of Agricultural and Fisheries Research and Training (IFAPA), P.O. Box 2027, 18080, Granada, Spain
| | - Janna Cropotova
- Department of Biological Sciences, Ålesund, Norwegian University of Science and Technology, Larsgårdsvegen 4, 6025, Ålesund, Norway
| | | | - Piera Centobelli
- Department of Industrial Engineering, University of Naples Federico II, P.le Tecchio 80, 80125, Naples, Italy
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Ozella L, Brotto Rebuli K, Forte C, Giacobini M. A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems. Animals (Basel) 2023; 13:1916. [PMID: 37370426 DOI: 10.3390/ani13121916] [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: 05/14/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows' health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows' health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows' behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.
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Affiliation(s)
- Laura Ozella
- Department of Veterinary Sciences, University of Turin, 10095 Grugliasco, TO, Italy
| | - Karina Brotto Rebuli
- Department of Veterinary Sciences, University of Turin, 10095 Grugliasco, TO, Italy
| | - Claudio Forte
- Department of Veterinary Sciences, University of Turin, 10095 Grugliasco, TO, Italy
| | - Mario Giacobini
- Department of Veterinary Sciences, University of Turin, 10095 Grugliasco, TO, Italy
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de Assis Vilela F, Times VC, de Campos Bernardi AC, de Paula Freitas A, Ciferri RR. A non-intrusive and reactive architecture to support real-time ETL processes in data warehousing environments. Heliyon 2023; 9:e15728. [PMID: 37215774 PMCID: PMC10196447 DOI: 10.1016/j.heliyon.2023.e15728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 05/24/2022] [Accepted: 04/19/2023] [Indexed: 05/24/2023] Open
Abstract
Nowadays, organizations are very interested to gather data for strategic decision-making. Data are disposable in operational sources, which are distributed, heterogeneous, and autonomous. These data are gathered through ETL processes, which occur traditionally in a pre-defined time, that is, once a day, once a week, once a month or in a specific period of time. On the other hand, there are special applications for which data needs to be obtained in a faster way and sometimes even immediately after the data are generated in the operation data sources, such as health systems and digital agriculture. Thus, the conventional ETL process and the disposable techniques are incapable of making the operational data delivered in real-time, providing low latency, high availability, and scalability. As our proposal, we present an innovative architecture, named Data Magnet, to cope with real-time ETL processes. The experimental tests performed in the digital agriculture domain using real and synthetic data showed that our proposal was able to deal in real-time with the ETL process. The Data Magnet provided great performance, showing an almost constant elapsed time for growing data volumes. Besides, Data Magnet provided significant performance gains over the traditional trigger technique.
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Liu Y, Zhuang Y, Yu L, Li Q, Zhao C, Meng R, Zhu J, Guo X. A Machine Learning Framework Based on Extreme Gradient Boosting to Predict the Occurrence and Development of Infectious Diseases in Laying Hen Farms, Taking H9N2 as an Example. Animals (Basel) 2023; 13:1494. [PMID: 37174531 PMCID: PMC10177545 DOI: 10.3390/ani13091494] [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: 03/04/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months' laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day - 1, day - 2, day - 3, day - 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day - 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable.
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Affiliation(s)
- Yu Liu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Yanrong Zhuang
- College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
| | - Ligen Yu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Qifeng Li
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Chunjiang Zhao
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Rui Meng
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Jun Zhu
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Xiaoli Guo
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
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Kaur U, Malacco VMR, Bai H, Price TP, Datta A, Xin L, Sen S, Nawrocki RA, Chiu G, Sundaram S, Min BC, Daniels KM, White RR, Donkin SS, Brito LF, Voyles RM. Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming. J Anim Sci 2023; 101:skad206. [PMID: 37335911 PMCID: PMC10370899 DOI: 10.1093/jas/skad206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/17/2023] [Indexed: 06/21/2023] Open
Abstract
Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.
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Affiliation(s)
- Upinder Kaur
- School of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA
| | - Victor M R Malacco
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Huiwen Bai
- School of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA
| | - Tanner P Price
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Arunashish Datta
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Lei Xin
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Shreyas Sen
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Robert A Nawrocki
- School of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA
| | - George Chiu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Shreyas Sundaram
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Byung-Cheol Min
- Department of Computer and Information Technology, West Lafayette, IN, 47907, USA
| | - Kristy M Daniels
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Robin R White
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Shawn S Donkin
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Richard M Voyles
- School of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA
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Wang J, Lovarelli D, Rota N, Shen M, Lu M, Guarino M. The Potentialities of Machine Learning for Cow-Specific Milking: Automatically Setting Variables in Milking Machines. Animals (Basel) 2022; 12:ani12131614. [PMID: 35804513 PMCID: PMC9265131 DOI: 10.3390/ani12131614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 11/16/2022] Open
Abstract
In dairy farming, milking-related operations are time-consuming and expensive, but are also directly linked to the farm’s economic profit. Therefore, reducing the duration of milking operations without harming the cows is paramount. This study aimed to test the variation in different parameters of milking operations on non-automatic milking machines to evaluate their effect on a herd and finally reduce the milking time. Two trials were set up on a dairy farm in Northern Italy to explore the influence of the pulsation ratio (60:40 vs. 65:35 pulsation ratio) and that of the detachment flow rate (600 g/min vs. 800 g/min) on milking performance, somatic cell counts, clinical mastitis, and teats score. Moreover, the innovative aspect of this study relates to the development of an optimized least-squares support vector machine (LSSVM) classification model based on the sparrow search algorithm (SSA) to predict the proper pulsation ratio and detachment flow rate for individual cows within the first two minutes of milking. The accuracy and precision of this model were 92% and 97% for shortening milking time at different pulsation ratios, and 78% and 79% for different detachment rates. The implementation of this algorithm in non-automatic milking machines could make milking operations cow-specific.
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Affiliation(s)
- Jintao Wang
- Laboratory of Modern Facility Agriculture Technology and Equipment Engineering, College of Engineering, Nanjing Agricultural University, 40, Dianjiangtai Road, Nanjing 210031, China; (J.W.); (M.S.); (M.L.)
| | - Daniela Lovarelli
- Department of Environmental Science and Policy, University of Milan, Via G. Celoria 2, 20133 Milan, Italy;
- Correspondence:
| | - Nicola Rota
- Agribovis s.r.l., Via B. Luini 73, 20821 Meda, Italy;
| | - Mingxia Shen
- Laboratory of Modern Facility Agriculture Technology and Equipment Engineering, College of Engineering, Nanjing Agricultural University, 40, Dianjiangtai Road, Nanjing 210031, China; (J.W.); (M.S.); (M.L.)
| | - Mingzhou Lu
- Laboratory of Modern Facility Agriculture Technology and Equipment Engineering, College of Engineering, Nanjing Agricultural University, 40, Dianjiangtai Road, Nanjing 210031, China; (J.W.); (M.S.); (M.L.)
| | - Marcella Guarino
- Department of Environmental Science and Policy, University of Milan, Via G. Celoria 2, 20133 Milan, Italy;
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The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence. Anim Health Res Rev 2022; 23:59-71. [PMID: 35676797 DOI: 10.1017/s1466252321000177] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Livestock welfare assessment helps monitor animal health status to maintain productivity, identify injuries and stress, and avoid deterioration. It has also become an important marketing strategy since it increases consumer pressure for a more humane transformation in animal treatment. Common visual welfare practices by professionals and veterinarians may be subjective and cost-prohibitive, requiring trained personnel. Recent advances in remote sensing, computer vision, and artificial intelligence (AI) have helped developing new and emerging technologies for livestock biometrics to extract key physiological parameters associated with animal welfare. This review discusses the livestock farming digital transformation by describing (i) biometric techniques for health and welfare assessment, (ii) livestock identification for traceability and (iii) machine and deep learning application in livestock to address complex problems. This review also includes a critical assessment of these topics and research done so far, proposing future steps for the deployment of AI models in commercial farms. Most studies focused on model development without applications or deployment for the industry. Furthermore, reported biometric methods, accuracy, and machine learning approaches presented some inconsistencies that hinder validation. Therefore, it is required to develop more efficient, non-contact and reliable methods based on AI to assess livestock health, welfare, and productivity.
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Joy A, Taheri S, Dunshea F, Leury B, DiGiacomo K, Osei-Amponsah R, Brodie G, Chauhan S. Non-invasive measure of heat stress in sheep using machine learning techniques and infrared thermography. Small Rumin Res 2022. [DOI: 10.1016/j.smallrumres.2021.106592] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Shine P, Murphy MD. Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study. SENSORS (BASEL, SWITZERLAND) 2021; 22:52. [PMID: 35009593 PMCID: PMC8747441 DOI: 10.3390/s22010052] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/17/2021] [Accepted: 12/19/2021] [Indexed: 05/06/2023]
Abstract
Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.
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Affiliation(s)
| | - Michael D. Murphy
- Department of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, Ireland;
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11
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Fuentes S, Gonzalez Viejo C, Tongson E, Lipovetzky N, Dunshea FR. Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence. SENSORS 2021; 21:s21206844. [PMID: 34696059 PMCID: PMC8541531 DOI: 10.3390/s21206844] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/11/2021] [Accepted: 10/13/2021] [Indexed: 12/14/2022]
Abstract
New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow's heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day-1), cow milk productivity (kg-milk-cow-1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.
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Affiliation(s)
- Sigfredo Fuentes
- Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia; (C.G.V.); (E.T.); (F.R.D.)
- Correspondence:
| | - Claudia Gonzalez Viejo
- Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia; (C.G.V.); (E.T.); (F.R.D.)
| | - Eden Tongson
- Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia; (C.G.V.); (E.T.); (F.R.D.)
| | - Nir Lipovetzky
- School of Computing and Information Systems, Melbourne School of Engineering, University of Melbourne, Parkville, VIC 3010, Australia;
| | - Frank R. Dunshea
- Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia; (C.G.V.); (E.T.); (F.R.D.)
- Faculty of Biological Sciences, The University of Leeds, Leeds LS2 9JT, UK
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12
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Editorial: Special Issue "Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems". SENSORS 2021; 21:s21196383. [PMID: 34640702 PMCID: PMC8512665 DOI: 10.3390/s21196383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 09/18/2021] [Indexed: 11/17/2022]
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Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D. Machine Learning in Agriculture: A Comprehensive Updated Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:3758. [PMID: 34071553 PMCID: PMC8198852 DOI: 10.3390/s21113758] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/05/2023]
Abstract
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.
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Affiliation(s)
- Lefteris Benos
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Aristotelis C. Tagarakis
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Georgios Dolias
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Remigio Berruto
- Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy;
| | - Dimitrios Kateris
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Dionysis Bochtis
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
- FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece
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Shu H, Wang W, Guo L, Bindelle J. Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review. Animals (Basel) 2021; 11:980. [PMID: 33915761 PMCID: PMC8066310 DOI: 10.3390/ani11040980] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/23/2021] [Accepted: 03/23/2021] [Indexed: 02/06/2023] Open
Abstract
In pursuit of precision livestock farming, the real-time measurement for heat strain-related data has been more and more valued. Efforts have been made recently to use more sensitive physiological indicators with the hope to better inform decision-making in heat abatement in dairy farms. To get an insight into the early detection of heat strain in dairy cows, the present review focuses on the recent efforts developing early detection methods of heat strain in dairy cows based on body temperatures and respiratory dynamics. For every candidate animal-based indicator, state-of-the-art measurement methods and existing thresholds were summarized. Body surface temperature and respiration rate were concluded to be the best early indicators of heat strain due to their high feasibility of measurement and sensitivity to heat stress. Future studies should customize heat strain thresholds according to different internal and external factors that have an impact on the sensitivity to heat stress. Wearable devices are most promising to achieve real-time measurement in practical dairy farms. Combined with internet of things technologies, a comprehensive strategy based on both animal- and environment-based indicators is expected to increase the precision of early detection of heat strain in dairy cows.
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Affiliation(s)
- Hang Shu
- Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China;
- AgroBioChem/TERRA, Precision Livestock and Nutrition Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium;
| | - Wensheng Wang
- Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China;
| | - Leifeng Guo
- Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China;
| | - Jérôme Bindelle
- AgroBioChem/TERRA, Precision Livestock and Nutrition Unit, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium;
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Fuentes S, Gonzalez Viejo C, Chauhan SS, Joy A, Tongson E, Dunshea FR. Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6334. [PMID: 33171995 PMCID: PMC7664231 DOI: 10.3390/s20216334] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 01/05/2023]
Abstract
Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting.
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Affiliation(s)
- Sigfredo Fuentes
- Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; (C.G.V.); (E.T.)
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; (C.G.V.); (E.T.)
| | - Surinder S. Chauhan
- Animal Nutrition and Physiology, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville 3010, Australia; (S.S.C.); (A.J.); (F.R.D.)
| | - Aleena Joy
- Animal Nutrition and Physiology, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville 3010, Australia; (S.S.C.); (A.J.); (F.R.D.)
| | - Eden Tongson
- Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; (C.G.V.); (E.T.)
| | - Frank R. Dunshea
- Animal Nutrition and Physiology, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville 3010, Australia; (S.S.C.); (A.J.); (F.R.D.)
- Faculty of Biological Sciences, The University of Leeds, Leeds LS2 9JT, UK
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Fuentes S, Torrico DD, Tongson E, Gonzalez Viejo C. Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data. SENSORS 2020; 20:s20133618. [PMID: 32605057 PMCID: PMC7374325 DOI: 10.3390/s20133618] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/21/2020] [Accepted: 06/25/2020] [Indexed: 12/20/2022]
Abstract
Important wine quality traits such as sensory profile and color are the product of complex interactions between the soil, grapevine, the environment, management, and winemaking practices. Artificial intelligence (AI) and specifically machine learning (ML) could offer powerful tools to assess these complex interactions and their patterns through seasons to predict quality traits to winegrowers close to harvest and before winemaking. This study considered nine vintages (2008–2016) using near-infrared spectroscopy (NIR) of wines and corresponding weather and management information as inputs for artificial neural network (ANN) modeling of sensory profiles (Models 1 and 2 respectively). Furthermore, weather and management data were used as inputs to predict the color of wines (Model 3). Results showed high accuracy in the prediction of sensory profiles of vertical wine vintages using NIR (Model 1; R = 0.92; slope = 0.85), while better models were obtained using weather/management data for the prediction of sensory profiles (Model 2; R = 0.98; slope = 0.93) and wine color (Model 3; R = 0.99; slope = 0.98). For all models, there was no indication of overfitting as per ANN specific tests. These models may be used as powerful tools to winegrowers and winemakers close to harvest and before the winemaking process to maintain a determined wine style with high quality and acceptability by consumers.
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Affiliation(s)
- Sigfredo Fuentes
- Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (E.T.); (C.G.V.)
- Correspondence: ; Tel.: +61-03-9035 9670
| | - Damir D. Torrico
- Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New Zealand;
| | - Eden Tongson
- Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (E.T.); (C.G.V.)
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (E.T.); (C.G.V.)
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