Living Off Uncertainty The Intelligent Animal Production Of Dryland Pastoralists
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Antifragile: Things That Gain From Disorder
By Suresh Neethirajan Suresh Neethirajan Scilit Preprints.org Google Scholar View Publications * and Bas Kemp Bas Kemp Scilit Preprints.org Google Scholar View Publications
A digital twin can be described as a digital replica of a real-world entity. It simulates the physical state and maybe the biological state and behavior of the real-world entity based on input data. It helps in predicting, optimizing, and improving decision making. It has revolutionized the industrial world, particularly the manufacturing industry, construction and healthcare sector, smart cities, and energy industry. In this perspectives paper, we explore the development and implementation of the digital twin in modern animal farming. In addition to showcasing potential applications, this review provides in-depth insights about the potential implementation and characterization of digital twins in modern animal farming.
Artificial intelligence (AI), machine learning (ML) and big data are consistently called upon to analyze and comprehend many facets of modern daily life. AI and ML in particular are widely used in animal husbandry to monitor both the animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and effective business decisions for the farmer. One particularly promising area that advances upon AI is digital twin technology, which is currently used to improve efficiencies and reduce costs across multiple industries and sectors. In contrast to a model, a digital twin is a digital replica of a real-world entity that is kept current with a constant influx of data. The application of digital twins within the livestock farming sector is the next frontier and has the potential to be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. The mental and emotional states of animals can be monitored using recognition technology that examines facial features, such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers to build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis of individual farms. Our goal in this review is to assess the progress toward the use of digital twin technology in livestock farming, with the goal of revolutionizing animal husbandry in the future.
Sap One Billion Live In 2020: Making A Difference
Today, more than ever before, vast quantities of information are being captured, stored, processed, and used digitally. In 1990, before the first internet browser was released by Berners-Lee, less than 0.5% of the world’s population was online. Over the last three decades, the internet exploded, and today, more than half of the world’s population uses the internet [1].
Alternatively, computing power and storage technologies have also increased several folds over the last five decades. To put things into perspective, the latest smartphones that we use typically have 4 GB of RAM. When compared to the Apollo guidance computer [2] on Apollo T human-crewed spacecraft to land on the moon, this is more than one million times its RAM capacity [3]. This explosive growth in computing power, storage capacity and the internet has paved the way for numerous smart devices to exist today. The miniaturization of modern devices/technology has contributed to the advent of the smart devices. In 2020, it is estimated that almost 30 billion smart devices were connected via the internet [4]. This is almost a hundred-fold increase in smart devices since 2006. In other words, we now have almost 26 smart devices per person on this planet, on average.
It is estimated that we now generate about 2.5 quintillion bytes of data every day. Clearly, this volume of data is beyond human comprehension. However, newer advances in artificial intelligence (AI), big data and machine learning (ML) have the ability to process such a large volume of data and help us make sense of it (Figure 1). This has opened up new possibilities that never existed before. One such opportunity is digital twins.
Cultured Meat: Processing, Packaging, Shelf Life, And Consumer Acceptance
This review looks at the concept of digital twins from two perspectives. First, it looks at the origins and practical applications of digital twins that have been adopted across industries and sectors. Secondly, but more importantly, it looks at how digital twins technology can benefit livestock farming in the near future. Traditionally, livestock farming has been a highly experiential and manual industry. Experienced farmers use their knowledge or the knowledge of previous generations to run their operations and care for their livestock. Farming may be more imperfect and unpredictable than other industries, perhaps because it is exposed to the occurrence of weather shocks and pests and diseases. Digital twins promise to revolutionize potentially all aspects of livestock farming. By combining big data, real-time information from the individual farm, and AI, farmers can obtain a much more precise picture of what is occurring with their livestock, housing structures, and equipment. As such, digital twins technology promises to help farmers better predict and discourage negative animal behaviors, track and prevent diseases from spreading or becoming serious, and improve energy efficiency, as well as animal comfort and well-being in housing structures, and reduce the costs of livestock losses and breeding operations.
At the simplest level, digital twins are realistic virtual representations of a physical entity (Figure 2). This physical entity can be anything from an automobile, windmill, or a manufacturing unit [5]. Sometimes it can even be something as complex as an entire city such as Singapore [6]. To better understand the concept behind digital twins, it is necessary to examine the origins of digital twins and how the concept has evolved to date.
As early as 1993, in his book Mirror Worlds, David Gelernter wrote about the possibility of software models that represent some chunk of reality [7]. However, even before that, NASA was one of the first organizations that used complex simulations of spacecrafts [8]. In 1970, the Apollo 13 mission had an unexpected explosion in its oxygen tank [9], which damaged their main engine and pushed the spacecraft away from its trajectory by about 400 miles a minute. To make things worse, the oxygen supply for the crew was slowly leaking into space. However, the mission team quickly modified several high-fidelity simulators to match the real-world conditions of the damaged spacecraft and used this to help the astronauts pick the right moves to land safely back on earth [10]. This was probably one of the first real-world applications of a digital twin.
Are Plants Intelligent?
However, it is important to note that digital twins were not a familiar concept back in 1970. Even so, this specific example met several key characteristics of a digital twin. For instance, the simulators sensed the real-world condition of the spacecraft and used that information to modify themselves. More importantly, it helped the team address what-if scenarios that were never considered in the design plan.
Space crafts are extremely costly, mission-critical and inaccessible by anyone not on it. Therefore, in a sense, they were the perfect real-world applications for digital twins—because the high costs were well worth the potential benefits it could offer. At least this was true until a few decades ago. However, as discussed earlier, the cost of sensing, sending, storing, and processing real-world changes in physical entities has become exponentially lower. This opened newer opportunities for several other industries, including the biomedical and agricultural livestock sectors, to also benefit from digital twins.
John Vickers of NASA first coined the term “digital twin” in 2002 [11]. Around the same time, the research professor Dr Michael Grieves worked with Vickers to adapt the concept of digital twins as a way to improve product lifecycle management (PLM) in the manufacturing sector [12]. Initially, he called it the “Conceptual Ideal for PLM”. However, even during this early stage, he touched upon several key properties of digital twins [13]. In his paper, Grieves spoke about the difference between real and virtual spaces and highlighted the
Superintelligence Summary Of Key Ideas And Review
This review looks at the concept of digital twins from two perspectives. First, it looks at the origins and practical applications of digital twins that have been adopted across industries and sectors. Secondly, but more importantly, it looks at how digital twins technology can benefit livestock farming in the near future. Traditionally, livestock farming has been a highly experiential and manual industry. Experienced farmers use their knowledge or the knowledge of previous generations to run their operations and care for their livestock. Farming may be more imperfect and unpredictable than other industries, perhaps because it is exposed to the occurrence of weather shocks and pests and diseases. Digital twins promise to revolutionize potentially all aspects of livestock farming. By combining big data, real-time information from the individual farm, and AI, farmers can obtain a much more precise picture of what is occurring with their livestock, housing structures, and equipment. As such, digital twins technology promises to help farmers better predict and discourage negative animal behaviors, track and prevent diseases from spreading or becoming serious, and improve energy efficiency, as well as animal comfort and well-being in housing structures, and reduce the costs of livestock losses and breeding operations.
At the simplest level, digital twins are realistic virtual representations of a physical entity (Figure 2). This physical entity can be anything from an automobile, windmill, or a manufacturing unit [5]. Sometimes it can even be something as complex as an entire city such as Singapore [6]. To better understand the concept behind digital twins, it is necessary to examine the origins of digital twins and how the concept has evolved to date.
As early as 1993, in his book Mirror Worlds, David Gelernter wrote about the possibility of software models that represent some chunk of reality [7]. However, even before that, NASA was one of the first organizations that used complex simulations of spacecrafts [8]. In 1970, the Apollo 13 mission had an unexpected explosion in its oxygen tank [9], which damaged their main engine and pushed the spacecraft away from its trajectory by about 400 miles a minute. To make things worse, the oxygen supply for the crew was slowly leaking into space. However, the mission team quickly modified several high-fidelity simulators to match the real-world conditions of the damaged spacecraft and used this to help the astronauts pick the right moves to land safely back on earth [10]. This was probably one of the first real-world applications of a digital twin.
Are Plants Intelligent?
However, it is important to note that digital twins were not a familiar concept back in 1970. Even so, this specific example met several key characteristics of a digital twin. For instance, the simulators sensed the real-world condition of the spacecraft and used that information to modify themselves. More importantly, it helped the team address what-if scenarios that were never considered in the design plan.
Space crafts are extremely costly, mission-critical and inaccessible by anyone not on it. Therefore, in a sense, they were the perfect real-world applications for digital twins—because the high costs were well worth the potential benefits it could offer. At least this was true until a few decades ago. However, as discussed earlier, the cost of sensing, sending, storing, and processing real-world changes in physical entities has become exponentially lower. This opened newer opportunities for several other industries, including the biomedical and agricultural livestock sectors, to also benefit from digital twins.
John Vickers of NASA first coined the term “digital twin” in 2002 [11]. Around the same time, the research professor Dr Michael Grieves worked with Vickers to adapt the concept of digital twins as a way to improve product lifecycle management (PLM) in the manufacturing sector [12]. Initially, he called it the “Conceptual Ideal for PLM”. However, even during this early stage, he touched upon several key properties of digital twins [13]. In his paper, Grieves spoke about the difference between real and virtual spaces and highlighted the
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