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One aspect that, on the one hand, impacts climate change but on the other hand is also highly influenced by it, is the production of food. Roughly 11% of the Earth’s population was unable to meet their dietary energy requirements between 2014 and 2016, representing approximately 795 million people. [1] On the contrary, food production for the population of industrial nations in particular highly contributes to climate change due to a meat-focused diet, with the expectation of seasonal fruits throughout the entire year, as well as a high waste of food. [2] Both situations will become more complex in the next decades as the global population is predicted to grow to 10 billion by 2050 according to the United Nations. [1] This might increase the number of people with insufficiently satisfied dietary energy requirements. The increasing welfare in emerging countries will likely lead to more people adopting the resource-demanding nutrition of the industrial nations. | One aspect that, on the one hand, impacts climate change but on the other hand is also highly influenced by it, is the production of food. Roughly 11% of the Earth’s population was unable to meet their dietary energy requirements between 2014 and 2016, representing approximately 795 million people. [1] On the contrary, food production for the population of industrial nations in particular highly contributes to climate change due to a meat-focused diet, with the expectation of seasonal fruits throughout the entire year, as well as a high waste of food. [2] Both situations will become more complex in the next decades as the global population is predicted to grow to 10 billion by 2050 according to the United Nations. [1] This might increase the number of people with insufficiently satisfied dietary energy requirements. The increasing welfare in emerging countries will likely lead to more people adopting the resource-demanding nutrition of the industrial nations. | ||
Traditional food production approaches will not be able to deal with those issues sufficiently; hence, novel approaches are required. The integration of current research advances in the [[internet of things]] (IoT) seems especially promising in supporting various aspects of food production, including farming, supply chain management, monitoring, processing, or demand estimation. Whereas a commonly accepted definition of IoT is not present in the literature, it is agreed that IoT refers to connected computational resources and sensors which often supplement everyday objects. The sensors support the collection of data which can be [[Data analysis|analyzed]] for identifying changes in the environment, and the IoT system can react to accommodating those changes. Procedures from [[artificial intelligence]] (AI)—the idea that machines should be able to carry out tasks in a smart way—and [[machine learning]] (ML)—techniques for machines to learn from data—can complement the analysis and system controlling process in IoT systems. The actions of analyzing and controlling IoT systems are also named as a reason for adaptation. [3] The purposeful application of those methods can complement and optimize many existing processes. The research in this field is distributed across several domains, such as precision agriculture, smart farming, internet of food, food supply chain management, food authentication, industrial IoT (IIoT, or Industry 4.0) for food production, food safety and authentication, "food computing," or smart/pervasive health. Often, those concepts overlap and are not completely distinguished. | |||
Another research stream can be recognized under the notion of food informatics, which is often understood as data-centric research for supporting food production and consumption. [4,5,6,7] However, research alone does not provide a clear concept of food informatics. In this review paper, we want to distinguish the various research streams related to the topics of food production and consumption. Further, we motivate our perspective on food informatics as a supportive research stream that can contribute to the wide field of applying IoT and AI/ML to optimize food production and, hence, can be seen as an underlying technological basement for the other information and communication technology (ICT)-related research streams that target aspects of the food supply chain. Additionally, we present several case studies related to the production of food, discuss how food informatics contributes to those applications, and highlight the relation to the other presented research streams. However, we do not aim at providing a full-fledged survey as this would be not possible for a broad coverage of topics. Accordingly, we target providing a systematic mapping [8] approach to offer a cross section of the research landscape. | |||
In summary, our contributions are threefold: | |||
* Delineation of concepts: We provide a delineation of various concepts related to the digitalization of food science production. | |||
* Definition of "food informatics": We review the state-of-the-art in food informatics and motivate a new understanding of food informatics as a supportive discipline for food production and an underlying technical basement for digitalization. | |||
* Application: We discuss the potential of IoT and AI/ML to support the process of food production and supply—in our understanding, the central role of food informatics—with regard to the socio-technical perspective of the various stakeholders. | |||
The remainder of this paper is structured as follows: The next section compares research streams related to the production and consumption of food, and the subsequent section presents a new definition of food informatics. We then present several research perspectives, as well as research challenges, when applying ICT in the food production domain. The penultimate section discusses possible threats to the validity of our claims. Finally, we close with related work and our conclusions. | |||
==Delineation of concepts== | |||
The production of food is a highly complex process. On the one hand, there is a high diversity in the combination of ingredients and intermediaries with many dependencies, for example, in the order of processing. Further, byproducts, side-products, or co-products might arise, such as butter milk when producing butter. On the other hand, food has hygienic, olfactory, sensory, or preservation requirements. In general, the food production process can be divided into several phases: | |||
* Agriculture: Production of ingredients/food | |||
* Logistics: Transportation of food while obeying hygienic constraints | |||
* Processing: Processing of ingredients into food products in an industrial process, while obeying hygienic constraints | |||
* Retail: Selling of food | |||
* Consumer use: Consumption by humans or animals | |||
* Food waste handling: Handling and disposal of food waste in an intelligent and sustainable way (not in the scope of this paper) | |||
In this paper, we see this process as a sequential process. However, in practice, a circular economy might be favorable from a sustainability viewpoint. Further, the mentioned byproducts, side-products, or co-products create a value-added network rather than a traditional value chain. However, in this paper we focus on how to support the different steps through [[Informatics (academic field)|informatics]] or ICT. Consequently, a sequential view on food production will not limit the validity of our arguments. | |||
Take for example the different processing phases for the production of Spätzle, a German pasta (see Figure 1). Production starts with the planting and harvesting of wheat (crop cultivation) as well as the production of eggs (livestock production). Both ingredients are transported to the production facility, where Spätzle is produced by adding water and salt. Subsequently, the product is delivered to wholesale trades, food retail markets, or directly to the consumer/restaurants, where the product is eventually consumed. In all phases, IoT devices can be integrated to either support data collection or actively control the processes through adaptation. (Adaptation is essentially an adjustment of the production process to handle machine faults, use traffic forecasts to re-calculate routes, or adjust production plans to most any delay. Additionally, technology known from smart health research, such as wearables, can help observe the consumption behavior of consumers. Data collection and analysis is supported by edge and [[cloud computing]] technology. With cloud resources, we refer to flexible server resources that can be used to complement the often limited computational resources of production machines. Those can be in-house resources, shared by multiple factories, or external resources like [[Google Cloud]], [[Amazon Web Services]], or [[Microsoft Azure]]. Edge devices act as additional computational resources within a factory that extend the computational resources of production machines. | |||
Revision as of 16:53, 22 March 2022
| Full article title | Food informatics: Review of the current state-of-the-art, revised definition, and classification into the research landscape |
|---|---|
| Journal | Foods |
| Author(s) | Krupitzer, Christian; Stein, Anthony |
| Author affiliation(s) | University of Hohenheim |
| Primary contact | christian dot krupitzer at uni-hohenheim dot de |
| Editors | Jaiswal, Amit K. |
| Year published | 2021 |
| Volume and issue | 10(11) |
| Article # | 2889 |
| DOI | 10.3390/foods10112889 |
| ISSN | 2304-8158 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.mdpi.com/2304-8158/10/11/2889/htm |
| Download | https://www.mdpi.com/2304-8158/10/11/2889/pdf (PDF) |
 |
This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Background: The increasing population of humans and their changing food consumption behavior, as well as the recent developments in the awareness for food sustainability, lead to new challenges for the production of food. Advances in the internet of things (IoT) and artificial intelligence (AI) technology, including machine learning and data analytics, might help to account for these challenges.
Scope and approach: Several research perspectives—among them precision agriculture, industrial IoT, internet of food, and smart health—already provide new opportunities through digitalization. In this paper, we review the current state-of-the-art of the mentioned concepts. An additional concept to address is food informatics, which so far is mostly recognized as a mainly data-driven approach to support the production of food. In this review paper, we propose and discuss a new perspective for the concept of food informatics as a supportive discipline that subsumes the incorporation of information technology, mainly IoT and AI, in order to support the variety of aspects tangential to the food production process and delineate it from other existing research streams in the domain.
Key Findings and conclusions: Many different concepts related to digitalization in food science overlap. Further, food informatics is vaguely defined. In this paper, we provide a clear definition of food informatics and delineate it from related concepts. We corroborate our new perspective on food informatics by presenting several case studies about how it can support food production (as well as the intermediate steps until its consumption), and further describe its integration with related concepts.
Keywords: food informatics, internet of things, precision agriculture, smart agriculture, internet of food, food computing, food supply chain management
Introduction
Scientists have been alerting the world about climate change for a very long time; examples include the World Scientists’ Warning to Humanity from 1992[1] and the more recent World Scientists’ Warning to Humanity: A Second Notice in 2017.[2] However, it required Greta Thunberg and Fridays for Future to raise the awareness about climate change and the necessity to protect the environment and society.
One aspect that, on the one hand, impacts climate change but on the other hand is also highly influenced by it, is the production of food. Roughly 11% of the Earth’s population was unable to meet their dietary energy requirements between 2014 and 2016, representing approximately 795 million people. [1] On the contrary, food production for the population of industrial nations in particular highly contributes to climate change due to a meat-focused diet, with the expectation of seasonal fruits throughout the entire year, as well as a high waste of food. [2] Both situations will become more complex in the next decades as the global population is predicted to grow to 10 billion by 2050 according to the United Nations. [1] This might increase the number of people with insufficiently satisfied dietary energy requirements. The increasing welfare in emerging countries will likely lead to more people adopting the resource-demanding nutrition of the industrial nations.
Traditional food production approaches will not be able to deal with those issues sufficiently; hence, novel approaches are required. The integration of current research advances in the internet of things (IoT) seems especially promising in supporting various aspects of food production, including farming, supply chain management, monitoring, processing, or demand estimation. Whereas a commonly accepted definition of IoT is not present in the literature, it is agreed that IoT refers to connected computational resources and sensors which often supplement everyday objects. The sensors support the collection of data which can be analyzed for identifying changes in the environment, and the IoT system can react to accommodating those changes. Procedures from artificial intelligence (AI)—the idea that machines should be able to carry out tasks in a smart way—and machine learning (ML)—techniques for machines to learn from data—can complement the analysis and system controlling process in IoT systems. The actions of analyzing and controlling IoT systems are also named as a reason for adaptation. [3] The purposeful application of those methods can complement and optimize many existing processes. The research in this field is distributed across several domains, such as precision agriculture, smart farming, internet of food, food supply chain management, food authentication, industrial IoT (IIoT, or Industry 4.0) for food production, food safety and authentication, "food computing," or smart/pervasive health. Often, those concepts overlap and are not completely distinguished.
Another research stream can be recognized under the notion of food informatics, which is often understood as data-centric research for supporting food production and consumption. [4,5,6,7] However, research alone does not provide a clear concept of food informatics. In this review paper, we want to distinguish the various research streams related to the topics of food production and consumption. Further, we motivate our perspective on food informatics as a supportive research stream that can contribute to the wide field of applying IoT and AI/ML to optimize food production and, hence, can be seen as an underlying technological basement for the other information and communication technology (ICT)-related research streams that target aspects of the food supply chain. Additionally, we present several case studies related to the production of food, discuss how food informatics contributes to those applications, and highlight the relation to the other presented research streams. However, we do not aim at providing a full-fledged survey as this would be not possible for a broad coverage of topics. Accordingly, we target providing a systematic mapping [8] approach to offer a cross section of the research landscape.
In summary, our contributions are threefold:
- Delineation of concepts: We provide a delineation of various concepts related to the digitalization of food science production.
- Definition of "food informatics": We review the state-of-the-art in food informatics and motivate a new understanding of food informatics as a supportive discipline for food production and an underlying technical basement for digitalization.
- Application: We discuss the potential of IoT and AI/ML to support the process of food production and supply—in our understanding, the central role of food informatics—with regard to the socio-technical perspective of the various stakeholders.
The remainder of this paper is structured as follows: The next section compares research streams related to the production and consumption of food, and the subsequent section presents a new definition of food informatics. We then present several research perspectives, as well as research challenges, when applying ICT in the food production domain. The penultimate section discusses possible threats to the validity of our claims. Finally, we close with related work and our conclusions.
Delineation of concepts
The production of food is a highly complex process. On the one hand, there is a high diversity in the combination of ingredients and intermediaries with many dependencies, for example, in the order of processing. Further, byproducts, side-products, or co-products might arise, such as butter milk when producing butter. On the other hand, food has hygienic, olfactory, sensory, or preservation requirements. In general, the food production process can be divided into several phases:
- Agriculture: Production of ingredients/food
- Logistics: Transportation of food while obeying hygienic constraints
- Processing: Processing of ingredients into food products in an industrial process, while obeying hygienic constraints
- Retail: Selling of food
- Consumer use: Consumption by humans or animals
- Food waste handling: Handling and disposal of food waste in an intelligent and sustainable way (not in the scope of this paper)
In this paper, we see this process as a sequential process. However, in practice, a circular economy might be favorable from a sustainability viewpoint. Further, the mentioned byproducts, side-products, or co-products create a value-added network rather than a traditional value chain. However, in this paper we focus on how to support the different steps through informatics or ICT. Consequently, a sequential view on food production will not limit the validity of our arguments.
Take for example the different processing phases for the production of Spätzle, a German pasta (see Figure 1). Production starts with the planting and harvesting of wheat (crop cultivation) as well as the production of eggs (livestock production). Both ingredients are transported to the production facility, where Spätzle is produced by adding water and salt. Subsequently, the product is delivered to wholesale trades, food retail markets, or directly to the consumer/restaurants, where the product is eventually consumed. In all phases, IoT devices can be integrated to either support data collection or actively control the processes through adaptation. (Adaptation is essentially an adjustment of the production process to handle machine faults, use traffic forecasts to re-calculate routes, or adjust production plans to most any delay. Additionally, technology known from smart health research, such as wearables, can help observe the consumption behavior of consumers. Data collection and analysis is supported by edge and cloud computing technology. With cloud resources, we refer to flexible server resources that can be used to complement the often limited computational resources of production machines. Those can be in-house resources, shared by multiple factories, or external resources like Google Cloud, Amazon Web Services, or Microsoft Azure. Edge devices act as additional computational resources within a factory that extend the computational resources of production machines.
References
- ↑ Union of Concerned Scientists (16 July 1992). "1992 World Scientists' Warning to Humanity". Union of Concerned Scientists. https://www.ucsusa.org/resources/1992-world-scientists-warning-humanity.
- ↑ Ripple, William J.; Wolf, Christopher; Newsome, Thomas M.; Galetti, Mauro; Alamgir, Mohammed; Crist, Eileen; Mahmoud, Mahmoud I.; Laurance, William F. et al. (1 December 2017). "World Scientists’ Warning to Humanity: A Second Notice" (in en). BioScience 67 (12): 1026–1028. doi:10.1093/biosci/bix125. ISSN 0006-3568. https://academic.oup.com/bioscience/article/67/12/1026/4605229.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation and updates to spelling and grammar. In some cases important information was missing from the references, and that information was added. The authors did not provide citations for the "World Scientists'" papers; citations were added for this version.
