Digital twins in smart farming

Digital Twins can be considered as a new phase in smart and data-driven greenhouse horticulture. A Digital Twin is a digital equivalent to a real-life object of which it mirrors its behaviour and states over its lifetime in a virtual space. Research indicates that they can substantially enhance productivity and sustainability, and are able to deal with the increasing scarcity of green labour in greenhouse horticulture. This paper presents the results of a systematic literature review on Digital Twin applications in greenhouse horticulture. The review identifies 8 articles that explicitly address Digital Twins in greenhouse horticulture and 115 studies that implicitly apply the Digital Twin concept in smart IoT-based systems. Findings indicate that the concept of the Digital Twin is in a seminal phase in greenhouse horticulture, but there are existing applications that are not yet framed as Digital Twins. In the reviewed papers, there is a dominant focus on the cultivation process at the greenhouse level, among others for climate control, energy management and lighting. About 9% of the articles are virtualizing plants themselves, which indicates that the granularity level addressed is still rather limited. Only 7 % of the articles look beyond plants or single greenhouses. None of the reviewed articles consider the company level. Furthermore, most applications address monitoring and control of the state and behaviour of real-life objects. More advanced applications, including predictive and prescriptive capabilities across the complete lifecycle, are still in an early stage of development, although predictive Digital Twins are gaining prominence.

The aim of the work was to determine the current status and prospects for the development of organic livestock products manufacturing in the context of philosophy or technology in Eastern European countries (review material) with an emphasis on the situation in Ukraine and Romania. Organic livestock farming combines care for the environment, human health, and livestock welfare, and is an ethical and strategic vector for the development of the agricultural sector in the 21st century. Organic livestock farming is not just a technology for raising livestock, but above all an ethical and ecological system that seeks to maintain a balance between productivity, nature, and livestock welfare. The philosophy of organic production cannot be fully “scaled” as an industrial technology, because it is not about mass production, but about awareness, locality, and sustainable development. However, the gradual spread of organic principles is entirely feasible thanks to environmental education, certain agricultural policies, and consumer demand. Organic production is a philosophy, a marketing strategy, and a way of thinking. Organic livestock farming in Eastern Europe is an unformed, emerging but strategically important sector. In Ukraine, it remains a niche market but has growth potential due to export demand, political integration with the EU, and the growth of conscious domestic consumers. Global climate threats have somewhat changed the role of organic products, with organic products evolving from an “elite” product to a tool for environmental responsibility. In times of climate turbulence, organic livestock farming is not only an alternative to intensive industrial production, but also an essential factor in the preservation of agroecosystems. At the same time, consumer demand is a key driver for the development of organic livestock farming, especially in regions with a high level of environmental awareness. Despite its many advantages, organic livestock farming is not a universal solution for all countries and types of farms, as it requires a specialized approach, a balance between ideals and reality (economics, livestock health), and has potential but also objective limitations in terms of productivity, accessibility, and sustainability. Global climate threats have somewhat changed the role of organic products, with the organic products evolved from an “elite” product to a tool for environmental responsibility. In times of climate turbulence, organic livestock farming is not only an alternative to intensive industrial production, but also an essential factor in the preservation of agroecosystems. At the same time, consumer demand is a key driver for the development of organic livestock farming, especially in regions with a high level of environmental awareness. Organic livestock farming is not a panacea, but it is less vulnerable to some climate change impacts in terms of global warming, is more environmentally sustainable in the long term, and requires adaptation to new climate realities in terms of breeding, agroecology, digital tools, etc.

The significance of digital twins in mechanical and plant engineering companies is becoming increasingly important in the course of Industry 4.0 as a cyber-physical system. Digital twins represent a real object in virtual space. The properties of the real object are simulated using structural and behavior models, so that the digital twin represents a spitting image of the real object in virtual space. The structural models and behavioral models are created by modeling the digital twin in different modeling and simulation domains. A common form of the digital twin in mechanical and plant engineering is a digital twin, which is based on a kinematic multibody simulation. The kinematic digital twin can be coupled with industrial controllers and is often used for the virtual commissioning of plants/machines. These digital twins usually represent the kinematic behavior of the plan/machine in an idealized way and neglect the physically determined system dynamics. By extending the digital twin by the generation of state variables of a multiphysical dynamic simulation and the use of these state variables by industrial programmable logic controller (PLCs), advantages arise in the use of the digital twin with respect to virtual commissioning, fault analysis, condition monitoring and simulative optimization of machines and plants. Thus, it is necessary to complement the kinematic digital twin by a multiphysical dynamic simulation and to connect this extended model of the digital twin to PLCs.

Abstract. The urgency of climate change demands innovative solutions that transcend traditional mitigation and adaptation strategies. In this context, Digital Twins (DTs) have emerged as a transformative tool for integrating real-time data, advanced simulation, and predictive analytics across diverse domains. This paper presents a comprehensive review of DT applications that bolster climate resilience, emphasizing how the technology can drive decarbonization, enhance resource efficiency, and mitigate urban heat island effects. We categorize DT implementations into three spatial scales—small-scale assets, city-level systems, and global or Earth-level networks—illustrating the versatility and scalability of DT-driven interventions. Subsequently, we delve into key sectoral applications, including green building practices, climate-responsive construction, and precision agriculture, illustrating how DTs can optimize operational processes and reduce greenhouse gas emissions. By synthesizing findings from leading studies and real-world case examples, we highlight both the current achievements and the challenges—such as data standardization, computational demands, and stakeholder engagement—that hamper wider DT adoption. We argue that addressing these hurdles through cross-disciplinary collaboration, regulatory support, and technological innovation is critical for fully leveraging DTs’ potential in climate resilience. Overall, this paper underscores DTs as a promising catalyst for transformative change, offering data-driven insights that guide policy and practice toward a more sustainable, equitable, and climate-resilient future.

The agricultural domain is experiencing an increasing use of digital twin technologies in greenhouse horticulture for the betterment of monitoring production. Research in this domain has started leading towards more advanced ways of visually interacting with these digital twins, and this could be by means of immersive technologies (such as virtual reality) to better allow farmers to feel a sense of presence when exploring these digital copies. Yet there are many remaining challenges for the technology’s integration and more studies are needed into user interaction; specifically regarding simulation sickness to cater for comfort during prolonged and regular use. Thus, this article documents the survey of 30 participants by means of the Simulator Sickness Questionnaire (before and after interaction) when using an immersive digital greenhouse twin environment. Findings indicate that users who experience simulation sickness tend to provide a lower evaluation score of the digital twin and their prior experience of gaming (on varied devices) affects the overall evaluation of the digital environment. Further, the level of playability and realism (referred to as convenience), statistically affects the end users’ level of sickness. For tracking crop growth by means of digital greenhouse twins, where prolonged user interaction may take place in a virtual environment, these are notable considerations for digital twin developers.

Organic dairy farming in Europe

The silent pandemic of antimicrobial resistance (AR) has been on the rise for the past decades. It is essential to determine the burden of AR in animal farms that spreads leading to human exposure. A total of 100 samples including soil, litter, animal excreta, and wastewater were collected from seven conventional and one organic farm in Egypt. The prevalence of extended-spectrum β-lactamase-producing Escherichia coli (ESBL-producing E. coli), fluoroquinolone-resistant E. coli, fluoroquinolone-resistant Salmonella, and vancomycin-resistant enterococci (VRE) was determined in studied farms. Conventional farms had a higher prevalence of antimicrobial-resistant bacteria than the organic farm (73.81%vs. 18.75%, P<.001). In conventional farms 21.43% of samples yielded mixed isolates; however, in the organic farm, only single isolates of ESBL-producing E. coli were detected. The most prevalent ESBL-production gene was blaTEM (82.14%), followed by blaCTX-M (48.22%), and blaSHV (19.64%), either alone or in combination with another gene. The most prevalent fluoroquinolone-resistance genes were qnrS (82.69%) and qnrB (42.30%), either alone or in combination with another gene(s). A total of five VRE isolates harbored vanA gene (83.33%), none carried vanB gene, and one isolate was negative for both genes. The studied conventional livestock farms had significantly higher rates of serious AR threats than the organic farm.

A digital twin (DT) system is a virtual system that can provide a comprehensive description of a real physical system. The DT system continuously receives data from physical sensors and user input information and provides information feedback to the physical system. It is an emerging technology that utilizes an advanced Internet of Things (IoT) to connect different objects, which is in high demand in various industries and its research literature is growing exponentially. Traditional physical systems provide data support for the monitoring of physical objects such as buildings through digital modeling techniques, data acquisition tools, human computer interfaces, and building information models (BIM). However, DT can offer much more than data presentation. DT uses the received data to perform operations such as analysis, prediction, and simulation, and finally transmits the analysis results to the physical system as feedback. Compared with other physical systems, DT has the characteristics of bidirectional data exchange and real-time autonomous management. The plant factory control system based on digital twin technology continuously measures the power consumption of electrical equipment through the sensors of the physical system and makes the corresponding virtual color-coded gradient map based on the obtained data. The darker the virtual device is, the more power it currently requires, and just based on the shade of color gives the user a very intuitive idea of the current power usage of the electronic device. There has been extensive research on digital twin technology, but there are few studies on implementing plant factories based on digital twin technology. This paper proposes the idea of combining digital twin technology with plant factories to provide research directions for future smart agriculture. It proves that smart agricultural production with sustainability can also benefit from this idea.

Simulations are used in very different contexts and for very different purposes. An emerging development is the possibility of using simulations to obtain a more or less representative reproduction of...

This article sets out to investigate the development of total factor productivity in organic and conventional agriculture in the period from 1999/2000 to 2006/07. Productivity development is assessed with the use of Malmquist Indices based on Stochastic Frontier Ana-lysis. The analysis is based on a balanced panel of farm records of 151 organic and conventional farms, respectively. A matching procedure is applied to identify for each organic farm a conventional counterpart with similar structural features. The study reveals a similar development of productivity in organic grazing livestock and mixed farms, but the productivity level is still lower compared to their conventional counterparts. Only organic arable farms had started off with a higher level of productivity in 1999/2000 but they were not able to maintain their advantage over their conventional counterparts. The Malmquist analysis highlights that these changes cannot be attributed to low rates of technical progress. Lack of technical and scale efficiency are rather to be seen as the key drivers of the slowed productivity development in organic agriculture.

Digital Twins (DT) have massive scope for success in the field of sustainable agriculture. But the number of works done in this field is relatively less compared to other domains like Manufacturing, Healthcare, Autonomous Vehicles, and Aviation. Due to frequent natural calamities like floods and epidemics, the need for sustainable and self-sufficient agriculture from the primary level is essential. Soil-less agriculture is gaining more popularity over soil-born methods due to issues of soil-based agriculture like soil erosion, intensive labor, water reusability, and overall productivity. This work deals with one of the popular soil-less methods, Hydroponics. The various ways in which DTs can contribute to the various phases of hydroponics like designing, operation, monitoring, optimization, and maintenance are discussed. This paper also presents a review of research works done in the application of DT in smart farming.

With the development of digital twin, the research on the fusion of intelligent manufacturing system and digital twin technology attracts more attention. In this paper, the realization of digital twin is studied for geometric feature online inspection system of body-in-white (BIW). A three-level virtual modeling approach of Element-Behavior-Rule is proposed for the physical environment modeling in a digital environment. An element model, behavior model and rule model are defined, respectively, and the relationship among the three-layer model is given for real-time communication. A three-layer communication architecture of the digital twin system is designed for the real-time mapping between physical and virtual space, and the process of the online inspection digital twin system is analyzed. Finally, on the basis of the online inspection physical experimental platform and JHIM (Jiangheng Intelligent Manufacturing) software platform, the digital twin system for geometric feature online inspection of BIW is developed, which shows that the virtual model can be driven in real time by real-time physical inspection data. Digital twin technology provides a feasible way for real-time monitoring of the online inspection system.

Digital Twin is an emerging technology at the forefront of Industry 4.0, with the ultimate goal of combining the physical space and the virtual space. To date, the Digital Twin concept has been applied in many engineering fields, providing useful insights in the areas of engineering design, manufacturing, automation, and construction industry. While the nexus of various technologies opens up new opportunities with Digital Twin, the technology requires a framework to integrate the different technologies, such as the Building Information Model used in the Building and Construction industry. In this work, an Information Fusion framework is proposed to seamlessly fuse heterogeneous components in a Digital Twin framework from the variety of technologies involved. This study aims to augment Digital Twin in buildings with the use of AI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a drone-based Digital Twin augmentation framework with reusable and customisable components. A proof of concept is also developed, and extensive evaluation is conducted for 3D reconstruction and applications of AI for defect detection.KeywordsDigital Twin3D reconstructionArtificial Intelligence (AI)Information augmentationUnmanned aerial vehicle (UAV)

This article explores the concept of digital twins and reasons why manufacturers prefer digital replicas of products, machines, processes, or even entire factories. A digital twin models the robotic line with such high fidelity that the engineer can do all this in the virtual world. Digital twins are the foundation of tomorrow’s smarter workplace. A factory’s digital twin must be robust enough to capture those changes, plus all relevant data from each operation. Smart factories, such as GE’s Brilliant Factory and Siemens’ competing Industrie 4.0, need both types of digital twins—product and process—to work. Digital product models contain each component that goes into a product, from screws and welds to plastic shapes and machined metals. Digital twins also support greater automation. As artificial intelligence (AI) systems learn more about specific machines, they will use their digital twins to help engineers run plants more efficiently. AI can analyze it to see if a screw is loose or a bearing is starting to fail. The better the AI knows the machine, the more accurately it can predict when that failure is likely to happen.

Literature review of digital twin in healthcare