Digital Twins in Livestock Farming

Abstract

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.

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Intelligent host engineering for metabolic flux optimisation in biotechnology

“…This allows the performance in silico of ‘what if?’ kinds of experiments in a manner far less costly than doing them all, allowing one to choose a subset of the most promising. This is also sometimes referred to as e-science [ 319–321 ], or having a ‘digital twin’ [ 322 , 323 ] of the process of interest. It is, of course, very well established in fields such as chemical or electronic engineering, where it would be inconceivable to design a process plant or a new chip without modelling it in parallel.…”

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“…A digital twin of a farm animal will allow continuous monitoring of the mental, physical, and emotional state of the animals. In addition, modeling, simulating and augmenting the data allows the digital twin to be used to plan, monitor, control and optimize cost-, labor-and energy-efficient animal husbandry processes based on real-life data (26,27). Using GANs to develop a digital twin will allow different situations to be explored and will help predicting its effects on the animals.…”