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Machine Learning In Livestock Disease Management
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Book Synopsis Machine Learning in Livestock Disease Management by : Dr. G.Hannah Grace
Download or read book Machine Learning in Livestock Disease Management written by Dr. G.Hannah Grace and published by SK Research Group of Companies. This book was released on 2023-11-09 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr. G.Hannah Grace, Assistant Professor Senior Grade, Division of Mathematics, School of Advanced Sciences, VIT Chennai Campus, Tamil Nadu, India. Dr.Nivetha Martin, Assistant Professor, Department of Mathematics, Arul Anandar College (Autonomous), Karumathur, Madurai, Tamil Nadu, India. Dr.N.Ramila Gandhi, Associate Professor, Department of Mathematics, PSNA College of Engineering and Technology (Autonomous), Dindigul, Tamil Nadu, India. Dr.P.Pandiammal, Assistant Professor, Department of Mathematics, G.T.N Arts College (Autonomous), Dindigul, Tamil Nadu, India.
Book Synopsis Biotechnological Interventions Augmenting Livestock Health and Production by : Chandra Sekhar Mukhopadhyay
Download or read book Biotechnological Interventions Augmenting Livestock Health and Production written by Chandra Sekhar Mukhopadhyay and published by Springer Nature. This book was released on 2023-10-24 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively discusses the applications of molecular genetics, functional and structural genomics, and proteomics vis-a-vis bioinformatics, artificial intelligence, and robotics in livestock healthfulness and productivity. It reviews the biotechnological approaches in veterinary sciences for increasing productivity and resistance to disease. The book emphasizes the approaches based on artificial intelligence to analyze the data collected on animals, pathogens, and their environment. It underscores artificial intelligence applications in disease diagnosis, epidemiological studies, and detecting biological phenomena, including heat-detection, pregnancy, docility, and infections. Further, the book examines the genomics and proteomics approaches for understanding the gut microbiota and the role of pathogen-host interactions in animal health and disease. Lastly, it explores both pathogenic and non-pathogenic microbial transfer between humans, animals, and the environment across one health spectrum.
Book Synopsis Data-Driven Farming by : Syed Nisar Hussain Bukhari
Download or read book Data-Driven Farming written by Syed Nisar Hussain Bukhari and published by CRC Press. This book was released on 2024-06-13 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies. Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
Book Synopsis Internet of Things and Machine Learning in Agriculture by : Jyotir Moy Chatterjee
Download or read book Internet of Things and Machine Learning in Agriculture written by Jyotir Moy Chatterjee and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-02-08 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. Machine Learning and the Internet of Things can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.
Book Synopsis Artificial Intelligence and Internet of Things in Smart Farming by : Mohamed Abdel-Basset
Download or read book Artificial Intelligence and Internet of Things in Smart Farming written by Mohamed Abdel-Basset and published by CRC Press. This book was released on 2024-04-01 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of the areas of artificial intelligence (AI) that can be used for smart farming applications, through either successful engineering or ground-breaking research. Among them, the highlighted tactics are soil management, water management, crop management, livestock management, harvesting, and the integration of Internet of Things (IoT) in smart farming. Artificial Intelligence and Internet of Things in Smart Farming explores different types of smart framing systems for achieving sustainability goals in the real environment. The authors discuss the benefits of smart harvesting systems over traditional harvesting methods, including decreased labor requirements, increased crop yields, increased probabilities of successful harvests, enhanced visibility into crop health, and lower overall harvest and production costs. It explains and describes big data in terms of its potential five dimensions—volume, velocity, variety, veracity, and valuation—within the framework of smart farming. The authors also discuss the recent IoT technologies, such as fifth-generation networks, blockchain, and digital twining, to improve the sustainability and productivity of smart farming systems. The book identifies numerous issues that call for conceptual innovation and has the potential to progress machine learning (ML), resulting in significant impacts. As an illustration, the authors point out how smart farming offers an intriguing field for interpretable ML. The book then delves into the function of AI techniques, such as AI in accelerating the development of nano-enabled agriculture, thereby facilitating safe-by-design nanomaterials for various consumer products and medical applications. This book is for undergraduate students, graduate students, researchers, and AI engineers who pursue a strong understanding of the practical methods of machine learning in the agriculture domain. Practitioners and stakeholders would be able to follow this book to understand the potential of ML in their farming projects and agricultural solutions. Features: • Explores different types of smart framing systems for achieving sustainability goals in the real environment • Explores ML-based analytics such as generative adversarial networks (GAN), autoencoders, computational imaging, and quantum computing • Examines the development of intelligent machines to provide solutions to real-world problems, emphasizing smart farming applications, which are not modeled or are extremely difficult to model mathematically • Emphasizes methods for better managing crops, soils, water, and livestock, urging investors and businesspeople to occupy the existing vacant market area • Discusses AI-empowered Nanotechnology for smart farming
Book Synopsis Sustainable Farming Through Machine Learning by : Suneeta Satpathy
Download or read book Sustainable Farming Through Machine Learning written by Suneeta Satpathy and published by . This book was released on 2024-12-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the transformative potential of ML technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how AI/ML can optimize resource management and improve overall productivity in farming practices. Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. They also cover applications in livestock management, including feed formulation and disease detection, they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore ethical and social implications of using such technologies. This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications.
Book Synopsis Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics by : Abhishek Kumar
Download or read book Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics written by Abhishek Kumar and published by CRC Press. This book was released on 2022-03-10 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges. This book is useful for research scholars and students involved in critical condition analysis and computation models.
Book Synopsis Machine Learning and Deep Learning for Smart Agriculture and Applications by : Mohamamd Farukh Hashmi
Download or read book Machine Learning and Deep Learning for Smart Agriculture and Applications written by Mohamamd Farukh Hashmi and published by . This book was released on 2023-08-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Deep Learning for Smart Agriculture and Applications delves into the captivating realm of artificial intelligence and its pivotal role in transforming the landscape of modern agriculture. With a focus on precision agriculture, digital farming, and emerging concepts, this book illuminates the significance of sustainable food production and resource management in the face of evolving digital hardware and software technologies. Geospatial technology, robotics, the Internet of Things (IoT), and data analytics converge with machine learning and big data to unlock new possibilities in agricultural management. This book explores the synergy between these disciplines, offering cutting-edge insights into data-intensive processes within operational agricultural environments. From automated irrigation systems and agricultural drones for field analysis to crop monitoring and precision agriculture, the applications of machine learning are far-reaching. Animal identification and health monitoring also benefit from these advanced techniques. With practical case studies on vegetable and fruit leaf disease detection, drone-based agriculture, and the impact of pesticides on plants, this book provides a comprehensive understanding of the applications of machine learning and deep learning in smart agriculture. It also examines various modeling techniques employed in this field and showcases how artificial intelligence can revolutionize plant disease detection. This book serves as a comprehensive guide for researchers, practitioners, and students seeking to harness the power of AI in transforming the agricultural landscape.
Book Synopsis Infectious Disease Management in Animal Shelters by : Lila Miller
Download or read book Infectious Disease Management in Animal Shelters written by Lila Miller and published by John Wiley & Sons. This book was released on 2011-11-16 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Infectious Disease Management in Animal Shelters is a comprehensive guide to preventing, managing, and treating disease outbreaks in shelters. Emphasizing strategies for the prevention of illness and mitigation of disease, this book provides detailed, practical information regarding fundamental principles of disease control and specific management of important diseases affecting dogs and cats in group living environments. Taking an in-depth, population health approach, the text presents information to aid in the fight against the most significant and costly health issues in shelter care facilities.
Book Synopsis Examining the Impact of Deep Learning and IoT on Multi-Industry Applications by : Raut, Roshani
Download or read book Examining the Impact of Deep Learning and IoT on Multi-Industry Applications written by Raut, Roshani and published by IGI Global. This book was released on 2021-01-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings. Examining the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers’ points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering.
Book Synopsis Applied Technology for Disease Prediction and Health Management in Dairy Cattle by : Emil A. Walleser
Download or read book Applied Technology for Disease Prediction and Health Management in Dairy Cattle written by Emil A. Walleser and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dairy cows undergo enormous metabolic changes following parturition and with the onset of lactation. Failure to execute these adaptations results in disease, reduced production, and loss of animal welfare. This period has been a major focus of research for the past twenty years. At the same time, the dairy industry has undergone a data revolution. Advancements in computational tools, predictive modeling, and data analytics tools offer new opportunities for early disease diagnosis and monitoring. This dissertation explores applications of data tools for monitoring and predicting disease with a primary focus on the transition period. Specific technologies investigated are milk analysis for prediction of transition period health disorders, herd data analysis platforms, and nanopore technology for infectious disease management. Milk analysis is common on dairies for monitoring production and milk composition. Infrared spectroscopy is applied to a milk sample and the resulting data can be used to determine milk components such as fat and protein, blood biomarkers, and other expressed phenotypes. The first objective of chapter 2 was to develop predictive models for blood beta-hydroxybutyrate (BHB) and blood nonesterified fatty acids (NEFA) using milk Fourier-transform infrared spectroscopy (FTIR) data and known blood levels. The second objective was to categorize cows as either metabolically disordered or healthy using a combination of blood BHB, blood NEFA, and milk fat protein quotient. We demonstrated screening level diagnostic performance for milk FTIR prediction of BHB and NEFA and reasonable performance for classification of metabolic disturbance. In chapter 3 we developed novel prediction models for hyperketonemia prediction using milk FTIR data. We collected a large and diverse group of milk FTIR samples from multiple locations, breeds, and lactations. We hypothesized that applying deep learning and ensembling or averaging model results together would improve performance compared to conventional predictive algorithms. We then implemented and evaluated multiple algorithms for their performance on hyperketonemia classification. Predictive performance was similar across modeling options and performed at screening test level. We demonstrated prediction models can be developed using more diverse groups of cattle than previously evaluated. While BHB and hyperketonemia have been a key research area in the transition period for many years, the effect of inflammation on transition cow health is becoming a key topic. However, the lack of cowside testing tools makes routine monitoring of inflammation levels extremely challenging. Milk FTIR-based predictions are a possible tool for monitoring inflammation on a cow and herd level. Multiple biomarkers of inflammation exist, with some of the most commonly measured being the acute phase proteins. Acute phase proteins are either elevated or reduced in response to inflammatory stimuli, haptoglobin is one of the most commonly measured due to its high peak levels and long period of elevation. In chapter 4 we developed regression and classification models for blood and milk haptoglobin models. Predictive performance was limited for both blood and milk haptoglobin suggesting inflammatory monitoring using milk FTIR may be better utilized on a herd-level. Applications of data analysis on the dairy extend beyond sample analysis into farm records. Large scale data analysis and predictive modeling are the future of cow management, but the tools of today limit the usability of on-farm data. Dairy record tools limit the ability to perform herd health analysis and improve cow health. In chapter 5 we implemented and provided a case study for Dairy-Copilot, reducing time to inference from hours to minutes. We believe providing user-accessible tools will drive improved cow health and producer profitability simultaneously. In chapter 6, we look to the future, nanopore sequencing allows real-time sequencing for samples using only a handheld sequencer and laptop computer. We conducted a proof-of-concept project to investigate predicting gene sequences using convolutional neural networks. In the second part we generated a synthetic metagenomic dataset to predict salmonella genome sequences vs other bacterial species. Our work demonstrates that convolutional neural networks are capable of predicting short conserved sequences, but prediction of salmonella sequences in metagenomic samples requires more investigation. The final chapter contains thoughts on the future extending beyond literature. We explore the numerous opportunities data brings to the dairy but caution that without proper oversight many negatives exist. Finally we remark on the dairy cow, the surrogate mother of humanity, calling for her continued care and respect.
Book Synopsis Future of AI in Biomedicine and Biotechnology by : Khade, Shankar Mukundrao
Download or read book Future of AI in Biomedicine and Biotechnology written by Khade, Shankar Mukundrao and published by IGI Global. This book was released on 2024-05-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The healthcare industry is grappling with numerous challenges, including rising costs, inefficiencies in service delivery, and the need for personalized treatment approaches. Traditional healthcare management and delivery methods must be improved in addressing these issues, leading to a growing demand for innovative solutions. Additionally, the exponential growth of medical data and the complexity of biomedical research and biotechnology presents a daunting challenge in harnessing this data effectively for improved patient care and medical advancements. There is a pressing need for a comprehensive understanding of how artificial intelligence (AI) can be leveraged to tackle these challenges and drive meaningful change in the healthcare sector. Future of AI in Biomedicine and Biotechnology offers a timely and insightful solution to the challenges faced by the healthcare industry. This book is not just a theoretical exploration; it is a practical roadmap for healthcare professionals, researchers, policymakers, and entrepreneurs seeking to navigate the complexities of AI in healthcare. By exploring the intersection of AI with biomedical sciences and biotechnology, this book provides a comprehensive guide to harnessing the power of AI for transformative healthcare innovation.
Author :Dr M Ganesh Babu, Dr C Suresh, Dr B Nagarjuna, Dr G Vani, Dr Amb.ParinSomani Publisher :Archers & Elevators Publishing House ISBN 13 :9390996430 Total Pages : pages Book Rating :4.3/5 (99 download)
Book Synopsis Business Environment and Technological Innovation- Emerging Trends by : Dr M Ganesh Babu, Dr C Suresh, Dr B Nagarjuna, Dr G Vani, Dr Amb.ParinSomani
Download or read book Business Environment and Technological Innovation- Emerging Trends written by Dr M Ganesh Babu, Dr C Suresh, Dr B Nagarjuna, Dr G Vani, Dr Amb.ParinSomani and published by Archers & Elevators Publishing House. This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Periparturient Diseases of Cattle by : Tanmoy Rana
Download or read book Periparturient Diseases of Cattle written by Tanmoy Rana and published by John Wiley & Sons. This book was released on 2024-12-24 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: Manage the health of cattle at a critical stage with this essential reference Milk is one of the backbones of the global food economy, with its high vitamin content and key contribution to bone health. As a result, dairy farming is one of the most essential sectors of the global agricultural market, and the health of cattle is an issue of global importance. Periparturient diseases, those sustained in the period immediately before, during, and after giving birth, have a potentially devastating impact on the reproductive cycle of cattle, and an understanding of these conditions is a critical aspect of food production. Periparturient Diseases of Cattle offers a comprehensive overview of these diseases, their pathogenesis, and their treatments. Summarizing all of the major periparturient disorders, their etiology, and their management, it is a critical resource for veterinary practitioners and others for whom cattle health is of fundamental importance. As a reference, a diagnostic aid, and a tool in farm management, this volume is indispensable. Periparturient Diseases of Cattle readers will also find: In-depth description of disease advancement Detailed treatment of disorders including metritis, mastitis, ketosis, and many more Color figures and line drawings to illustrate key concepts Periparturient Diseases of Cattle is ideal for student and working veterinarians, academicians, farm managers, industrialists, farm owners, and many more.
Book Synopsis Deep Learning for Sustainable Agriculture by : Ramesh Chandra Poonia
Download or read book Deep Learning for Sustainable Agriculture written by Ramesh Chandra Poonia and published by Academic Press. This book was released on 2022-01-09 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. Introduces new deep learning models developed to address sustainable solutions for issues related to agriculture Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture Illustrates through case studies how deep learning has been used to address a variety of agricultural diseases that are currently on the cutting edge Delivers an accessible explanation of artificial intelligence algorithms, making it easier for the reader to implement or use them in their own agricultural domain
Book Synopsis Application of Machine Learning in Agriculture by : Mohammad Ayoub Khan
Download or read book Application of Machine Learning in Agriculture written by Mohammad Ayoub Khan and published by Academic Press. This book was released on 2022-05-14 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning. As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development. This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics. Addresses the technology of smart agriculture from a technical perspective Reveals opportunities for technology to improve and enhance not only yield and quality, but the economic value of a food crop Discusses physical instruments, simulations, sensors, and markets for machine learning in agriculture
Book Synopsis Artificial Intelligence in Healthcare by : Adam Bohr
Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
