Digital twins in smart farming

Verdouw, C.N.; Tekinerdoğan, Bedir; Beulens, A.J.M.; Wolfert, Sjaak

doi:10.1016/j.agsy.2020.103046

Cited by 347 publications

(195 citation statements)

References 36 publications

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“…In cases where the collection of reference data is laborious or subjective, synthetic data may provide comprehensive results. This ties in with the introduced digital twin [117] concepts. Another and more traditional development direction would be to formalise the future research topic from the reference data perspective, especially acknowledging that data quality could make a difference.…”

Section: Discussionmentioning

confidence: 70%

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

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The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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Section: Discussionmentioning

confidence: 70%

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

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“…Digital twins have the potential to radically shift that model, so that future farming is based instead on real-time data, manipulated by AI analyses, which can then fuel better business decisions, improve animal health and well-being, and maximize the return from agricultural resources. An additional benefit is that action can be taken remotely and does not necessitate being at the farm [38].…”

Section: Digital Twins In Livestock Farmingmentioning

confidence: 99%

Digital Twins in Livestock Farming

Neethirajan

Kemp

2021

Animals

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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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“…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.…”

Section: Uses Beyond Humanshow Gans Can Contribute To Increase Welfare In Livestockmentioning

confidence: 99%

Beyond Deepfake Technology Fear: On its Positive Uses for Livestock Farming

Neethirajan¹

2021

Preprint

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Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.

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Digital twins in smart farming

Cited by 347 publications

References 36 publications

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Digital Twins in Livestock Farming

Beyond Deepfake Technology Fear: On its Positive Uses for Livestock Farming

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