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Conceptual framework on how to include data provided by a digital food twin into a self-aware learning and reasoning loop. Adapted from [15].
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Context 1
... using the digital twin as a base for reasoning, processes can be adapted based on the information provided by the digital twin. For controlling the food process operation, the LRA-M loop known from SeAC systems' research of the field of artificial intelligence is used (see Figure 2). Those SeAC systems have two main properties that describe their functionality [15]. ...
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... Furthermore, the wide range of abilities for data sharing through mobile devices such as smartphones provides a basis for complex analysis using remote computational and data resources. Some new computer-based concepts such as "digital twins" are used for the development of a conceptual framework for modeling the process of food production in order to perform a retrospective or predictive analysis of some quality indicators [35]. Krupitzer and Noack present their ideas to design an intelligent (self-aware) system to produce food with highquality thinking about a variety of physico-chemical parameters of raw materials and their influence on the quality of produced foods. ...
... computer-based concepts such as "digital twins" are used for the development of a conceptual framework for modeling the process of food production in order to perform a retrospective or predictive analysis of some quality indicators [35]. Krupitzer and Noack present their ideas to design an intelligent (self-aware) system to produce food with highquality thinking about a variety of physico-chemical parameters of raw materials and their influence on the quality of produced foods. ...
A review of current trends in CVSs (computer vision systems) regarding hardware and software components, in the context of cheese quality evaluation, is presented in this paper, and some directions for future development in the field are discussed. The application of methods of computer vision for evaluating the quality indicators of different types of cheese is explored in detail regarding systems for data gathering and algorithms for their processing. We discuss opportunities for the usage of advancements in AI (artificial intelligence) for a fast and effective quality control of food production. Modern computer-based concepts are viewed in the context of food quality control such as CAFE (computer-aided food engineering) and digital twins. Methods for further enhancement of the quality of human life are highlighted in the context of sustainability related to contemporary computer-based technologies.
Although there is an increasing interest in using digital twin technology (DTT) to improve agricultural practices, a holistic understanding of its cross-sectoral applications is limited. This lack of a holistic perspective is a critical gap that must be addressed for effective implementation and knowledge transfer. To address the gap, a meta-analysis of 24 peer-reviewed articles (2017-2023) was conducted to explore the applications, impact, and potential of DTT in various agricultural sectors. A systematic search was conducted in Lens, ProQuest, and ScienceDirect, focusing on crop production, aquaculture, and animal husbandry. The findings show that research focuses mostly on crop production with notable applications in robotics, artificial intelligence, and advanced sensor integration. On the other hand, research on the application of DTT in the forestry, livestock, and dairy sectors is scarce; thus, there is a need for further research in these areas. This study highlights the revolutionary role of DTT in enhancing agricultural sustainability and managing risks and recommends increased interdisciplinary cooperation to extend its usage in all agricultural sectors.
