Digital twins in the green life sciences

Knibbe, Willem Jan; Afman, Lydia A.; Boersma, Sjoerd; Bogaardt, M.J.; Evers, Jochem B.; Evert, F.K. van; Heide, Jene van der; Hoving, I.E.; Mourik, S. van; Ridder, Dick de; Wit, Allard de

doi:10.1080/27685241.2022.2150571

Cited by 8 publications

(4 citation statements)

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“…Wageningen University recently completed a DT project in metabolic health. The aim of the project called “Me, my diet and I” was to develop a personalized DT that can predict changes to an individual's blood values such as glucose and triglycerides to provide dietary advice with the goal to reduce cardiometabolic disease risk (Knibbe et al., 2022).…”

Section: Nams For Nutrition Researchmentioning

confidence: 99%

Human‐based new approach methodologies to accelerate advances in nutrition research

Cassotta,

Cianciosi,

Elexpuru‐Zabaleta

et al. 2024

Food Frontiers

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Much of nutrition research has been conventionally based on the use of simplistic in vitro systems or animal models, which have been extensively employed in an effort to better understand the relationships between diet and complex diseases as well as to evaluate food safety. Although these models have undeniably contributed to increase our mechanistic understanding of basic biological processes, they do not adequately model complex human physiopathological phenomena, creating concerns about the translatability to humans. During the last decade, extraordinary advancement in stem cell culturing, three‐dimensional cell cultures, sequencing technologies, and computer science has occurred, which has originated a wealth of novel human‐based and more physiologically relevant tools. These tools, also known as “new approach methodologies,” which comprise patient‐derived organoids, organs‐on‐chip, multi‐omics approach, along with computational models and analysis, represent innovative and exciting tools to forward nutrition research from a human‐biology‐oriented perspective. After considering some shortcomings of conventional in vitro and vivo approaches, here we describe the main novel available and emerging tools that are appropriate for designing a more human‐relevant nutrition research. Our aim is to encourage discussion on the opportunity to explore innovative paths in nutrition research and to promote a paradigm‐change toward a more human biology‐focused approach to better understand human nutritional pathophysiology, to evaluate novel food products, and to develop more effective targeted preventive or therapeutic strategies while helping in reducing the number and replacing animals employed in nutrition research.

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Section: Nams For Nutrition Researchmentioning

confidence: 99%

Human‐based new approach methodologies to accelerate advances in nutrition research

Cassotta,

Cianciosi,

Elexpuru‐Zabaleta

et al. 2024

Food Frontiers

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“…By analyzing historical data and real-time sensor data, digital twins can identify trends and patterns that may indicate issues such as pest infestations, crop diseases, or soil deficiencies. This early warning system can help farmers take proactive measures to prevent losses, which can help them save money and increase profitability (Knibbe et al, 2022).…”

Section: Predictive Analyticsmentioning

confidence: 99%

A Review on Digital Twins Technology: A New Frontier in Agriculture

Dawn,

Ghosh,

Saha

et al. 2023

AIA

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Farming is crucial for various aspects of daily life, including food, the economy, environment, culture, and community. It provides employment opportunities, generates income, and increases the export of agricultural products, particularly in rural areas. Sustainable farming practices promote soil health, biodiversity, and ecosystem services, and are essential in many parts of the world. Farming is deeply rooted in cultures and traditions and is a way of life for many communities, passed down from generation to generation. Without farming, we would not have the abundance and variety of food that we enjoy today. Advancements in technology, such as artificial intelligence, machine learning, and the Internet of Things, have greatly impacted agriculture by producing vast amounts of digital data on crops, soil, and weather conditions. However, managing and analyzing this data can be challenging for farmers, especially those in developing nations. To address this issue, affordable digital farming solutions, including open-source software platforms, sensor networks, and mobile apps, are being developed to help farmers optimize their resources, increase yields, and profits. Digital twin technology can play a crucial role in digital farming by providing farmers with a virtual replica of their physical farm. It is a digital depiction of a real-world asset, such a farm or a particular crop field, that gathers information from sensors, weather stations, and satellite pictures. This technology has arisen that has been hailed as revolutionary in a number of fields, including manufacturing machines, construction, agriculture, healthcare, and the automotive and aerospace industries. However, the technology is still in its early stages in agriculture, and it can be challenging to handle the interactions between different farming-related digital twin components. Additionally, digital twinning can require significant investment in technology and infrastructure, which may be a barrier for small-scale farmers.

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“…David et al (2023) discusses Cyber-Biophysical systems within the context of DTs. Other sources focus on greenhouses (Howard et al, 2021), agriculture (Tekinerdogan and Verdouw, 2020;Verdouw and Kruize, 2017), chemical reactions (Silber et al, 2023), or life sciences (El Saddik, 2018;Knibbe et al, 2022).…”

Section: Twinning System Componentsmentioning

confidence: 99%

Towards a Family of Digital Model/Shadow/Twin Workflows and Architectures

Paredis

Gomes

Vangheluwe

2021

Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics

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This document is a result of the COOCK project "Smart Port 2025: improving and accelerating the operational efficiency of a harbour eco-system through the application of intelligent technologies". It reports on the needs of companies for modelling and simulation and AI-based techniques, with twinning systems in particular. This document categorizes the purposes and Properties of Interest for the use of Digital Twins. It further illustrates some of the twinning usages, and touches on some of the potential architectural compositions for twins. This last topic will be further elaborated in a followup report.

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Digital twins in the green life sciences

Cited by 8 publications

References 50 publications

Human‐based new approach methodologies to accelerate advances in nutrition research

Human‐based new approach methodologies to accelerate advances in nutrition research

A Review on Digital Twins Technology: A New Frontier in Agriculture

Towards a Family of Digital Model/Shadow/Twin Workflows and Architectures

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