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Special Issue "IoT for Smart Food and Farming"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 15054

Special Issue Editors

Dr. Cor Verdouw
E-Mail Website
Guest Editor
Information Technology Group, Wageningen University, Wageningen, The Netherlands
Interests: supply chain management; smart farming; data-driven agri-food systems; enterprise information systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bedir Tekinerdogan
E-Mail Website
Co-Guest Editor
Information Technology Group, Wageningen University, 6708 PB Wageningen, The Netherlands
Interests: software engineering; software architecture; systems engineering; smart systems; critical infrastructures; software ecosystems; system of systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sjaak Wolfert
E-Mail Website
Co-Guest Editor
Wageningen Economic Research, Wageningen, The Netherlands
Interests: smart farming; digital transformation; information management; sustainable food systems; personalized nutrition

Special Issue Information

Dear Colleagues,

It is widely argued that agriculture requires a radical increase in productivity to feed the ever-growing world population and to deal with challenges such as climate change, resource efficiency, animal welfare, waste reduction, food safety, and healthier consumer lifestyles. The internet of things (IoT)—where every ‘thing’ can be uniquely identified, equipped with sensors and actuators, and accessed remotely via the internet—is a very promising paradigm to meet these challenges, potentially enabling:

  • Better sensing of farming and food processing operations, including usage of inputs, crop growth, animal behavior, food spoilage, and resource utilization;
  • Improving quality management and traceability by remotely monitoring the location and conditions of shipments and agricultural products;
  • Better understanding of specific production circumstances, such as climate conditions, animal welfare, microbiological quality, pest pressure, and better knowledge about optimal interventions;
  • More advanced and remote control of operations, enabled by actuators and robotics, e.g., precise application of pesticides and fertilizers, autonomous harvesting, or adjusting ambient conditions of food during transportation;
  • Increasing consumer awareness of sustainability and health issues through personalized nutrition advice, health wearables, and home automation.

In recent decades, IoT has received a lot of attention in the agricultural domain, and great advancements have been realized in both the academic and industrial worlds. Yet, we believe that IoT is some way from realizing its full potential in agriculture. Applications are still struggling with both technical and organizational issues like interoperability, reliability of IoT devices in harsh environments, stable wireless communication in fields, stables, greenhouses, etc., energy-efficient and circular IoT hardware, affordability for small farmers, trustworthy governance models, data ownership, collaborative business models for exploiting IoT data, and so on and so forth.

The Special Issue will capture the latest innovations from fundamental scientific concepts to commercially robust IoT-inspired solutions relevant to the development, implementation, and adoption of IoT-based smart farming systems. The Guest Editors are inviting submissions on topics ranging from new sensors through to cloud-based computing, data-driven applications/services, and new business models. Topics of interest include, but are not limited to, the following themes:

  • Intelligent sensing technologies;
  • IoT platform integration;
  • IoT interoperability;
  • Cloud, edge, and fog computing for smart agriculture;
  • Wireless sensor networks;
  • Security, privacy, and trust;
  • Systems of IoT systems;
  • Software ecosystems;
  • Integration IoT and ERP;
  • Artificial intelligence and machine learning;
  • IoT-based decision support;
  • IoT reference architectures for agriculture and food;
  • Data governance and ethics;
  • Business models;
  • IoT adoption;
  • Innovative case studies;

Literature reviews on the state of the art and challenges.

Dr. Cor Verdouw
Prof. Dr. Bedir Tekinerdogan
Dr. Sjaak Wolfert
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart farming
  • food supply chains
  • internet-of-things technologies
  • data-driven applications/services
  • distributed intelligent sensor networks and applications
  • low-power wireless connectivity
  • wireless sensor networks
  • software architecture
  • business models
  • adoption
  • machine learning
  • artificial intelligence

Published Papers (4 papers)

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Research

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Article
Systems Architecture Design Pattern Catalog for Developing Digital Twins
Sensors 2020, 20(18), 5103; https://doi.org/10.3390/s20185103 - 07 Sep 2020
Cited by 9 | Viewed by 2860
Abstract
A digital twin is a digital replica of a physical entity to which it is remotely connected. A digital twin can provide a rich representation of the corresponding physical entity and enables sophisticated control for various purposes. Although the concept of the digital [...] Read more.
A digital twin is a digital replica of a physical entity to which it is remotely connected. A digital twin can provide a rich representation of the corresponding physical entity and enables sophisticated control for various purposes. Although the concept of the digital twin is largely known, designing digital twins based systems has not yet been fully explored. In practice, digital twins can be applied in different ways leading to different architectural designs. To guide the architecture design process, we provide a pattern-oriented approach for architecting digital twin-based systems. To this end, we propose a catalog of digital twin architecture design patterns that can be reused in the broad context of systems engineering. The patterns support the various phases in the systems engineering life cycle process, and are described using a well-defined pattern documentation template. For illustrating the application of digital twin patterns, we adopt a multi-case study approach in the agriculture and food domain. Full article
(This article belongs to the Special Issue IoT for Smart Food and Farming)
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Article
Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management
Sensors 2020, 20(13), 3632; https://doi.org/10.3390/s20133632 - 28 Jun 2020
Cited by 21 | Viewed by 2631
Abstract
The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and [...] Read more.
The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and fertilizers’ usage and so huge improvements of both quality and yield of the crops. In this manuscript, the development of an IoT-based smart traceability and farm management system is described, which calibrates the irrigations and fertigation operations as a function of crop typology, growth phase, soil and environment parameters and weather information; a suitable software architecture was developed to support the system decision-making process, also based on data collected on-field by a properly designed solar-powered wireless sensor network (WSN). The WSN nodes were realized by using the ESP8266 NodeMCU module exploiting its microcontroller functionalities and Wi-Fi connectivity. Thanks to a properly sized solar power supply system and an optimized scheduling scheme, a long node autonomy was guaranteed, as experimentally verified by its power consumption measures, thus reducing WSN maintenance. In addition, a literature analysis on the most used wireless technologies for agri-food products’ traceability is reported, together with the design and testing of a Bluetooth low energy (BLE) low-cost sensor tag to be applied into the containers of agri-food products, just collected from the fields or already processed, to monitor the main parameters indicative of any failure or spoiling over time along the supply chain. A mobile application was developed for monitoring the tracking information and storing conditions of the agri-food products. Test results in real-operative scenarios demonstrate the proper operation of the BLE smart tag prototype and tracking system. Full article
(This article belongs to the Special Issue IoT for Smart Food and Farming)
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Article
Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model
Sensors 2020, 20(11), 3173; https://doi.org/10.3390/s20113173 - 03 Jun 2020
Cited by 11 | Viewed by 1449
Abstract
For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of [...] Read more.
For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general. Full article
(This article belongs to the Special Issue IoT for Smart Food and Farming)
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Review

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Review
A Systematic Review of IoT Solutions for Smart Farming
Sensors 2020, 20(15), 4231; https://doi.org/10.3390/s20154231 - 29 Jul 2020
Cited by 54 | Viewed by 7355
Abstract
The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet [...] Read more.
The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis. Full article
(This article belongs to the Special Issue IoT for Smart Food and Farming)
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