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IoT-Based Systems for Smart and Sustainable Agriculture

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

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 8946

Special Issue Editors


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Guest Editor

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Guest Editor
Affiliation 1: Istituto per le Macchine Agricole e Movimento Terra (IMAMOTER), Consiglio Nazionale delle Ricerche (CNR), Via Canal Bianco, 28, 44124 Ferrara, ItalyAffiliation 2: Ministero delle Politiche Agricole Alimentari, Forestali e del Turismo (MIPAAFT), Via XX Settembre, 20, 00187 Roma, Italy
Interests: smart farming; Internet of Things; geographic information science; semantic web technologies

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Guest Editor
Senior Lecturer, Mälardalen University, Sweden, Mälardalens högskola, Box 883, 721 23 Västerås, Sweden
Interests: artificial intelligence; robot–robot interaction; multi-agent systems; adaptive autonomous agents

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Guest Editor
Department of Telematics and Electronic Engineering, ETSIS de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
Interests: autonomy and cooperation; ubiquitous computing and internet of things (IoT); cyber physical systems (CPS); underwater; ground and aerial cooperating robots; embedded systems; distributed systems and software architectures; next-generation telematics networks and services
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Dependable Systems Engineering, Austrian Institute of Technology, Austria, Giefinggasse, 4, 1210 Vienna, Austria
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last few decades have witnessed the advancement of the Internet of Things (IoT) and multiple wireless sensor networks (WSNs), in both the academic and industrial worlds. Among various scenarios in which these paradigms can find an application, a relevant area, subject to ever-increasing interest and to the participation of different actors, is represented by agriculture, which is becoming “smarter and smarter” in different ways. Indeed, farming is facing many economic challenges in terms of productivity, cost-effectiveness, and quality, as well as an increasing labor shortage partly due to depopulation of rural areas.

In this historical moment, in which the indiscriminate use of pesticides, climate change, reduction of water supplies, depletion of resources, as well as loss of soil quality, which are already limiting the amount of food produced by the world’s farmlands, it is necessary to identify the best approaches to tackle these challenges. Furthermore, reliable detection, accurate identification, and proper quantification of pathogens and other factors and diseases affecting both plant and animal health are critical and need to be kept under control in order to reduce economic expenditures, trade disruptions, and human health risks.

Agriculture needs to be enhanced by new technologies in order to make it sustainable in a smart way. In the last few years, several IoT-based products dedicated to farming have appeared, but there are still some remaining issues, ranging from data acquisition, interpretation, and reliable collection in some potentially harsh radio environments. Moreover, together with the growing involvement of various technologies in the agricultural field, another key point is represented by managing the harvested data not only in-site, at the farmer’s estate, but in a logically centralized and aggregated way, thus exploiting the characteristics of cloud/edge computing-based infrastructures and architectures. In this way, the information is available at different levels, with the aim to be processed applying different approaches (e.g., using machine learning algorithms, as well as geographically distributed technologies), in order to offer specific services. Different access and handling policies can be applied to various actors, ranging from the farmer (interested in improving its crop’s health and quality adopting some treatments based on a set of practices obtained by multiple data analyses) to the final consumer, aware of the whole traceability process for the food supply chain “from the field to the fork”.

One challenging issue of next generation agriculture is big data. A new data processing culture is required to handle and exchange the huge amount of data in a reliable, secure, and lawful way. Data reliability means that the own collected daily data and the aggregated data from a third-party data provider are reliable, verified, and accurate in time and value. Data security ensures and guaranties that data at any transfer path will never be manipulated by criminal activities to avoid wrong data supply in smart agriculture applications. This can be achieved with a good user authentication implementation and sufficient data encryption before the data leave the protected owner zone—and, finally, the lawful handling of private generated data, which must be treated as a private property of the data owner and on which it must have the opportunity to sell its own data as a product. A fair data exploitation is the most exchange-related aspect in future data-driven agriculture. This can be only achieved with the most reliable data sources possible and strong data security.

This Special Issue calls for reports on high quality, up-to-date innovative original current advances in solutions and research for smart and sustainable agriculture, ranging from IoT, WSN, efficient sensing, cloud/edge computing, smart actuators, etc.

Some topics of interest include but are not limited to:

  • Data-aware networking in smart agriculture;
  • Sensor network deployment for smart agriculture;
  • Cloud computing for smart agriculture;
  • Edge computing for smart agriculture;
  • Cyberphysical systems (CPS) for smart agriculture;
  • Internet of Things (IoT) for smart agriculture;
  • Farm services and oriented applications for agri-food systems;
  • Modeling and metrics for sensing in smart agriculture;
  • Implementation and prototypes of WSN agriculture showcases;
  • Data security by user authentication and data encryption;
  • Security by design philosophy in agriculture;
  • Cost-effective sensor development for smart agriculture.

Prof. Dr. Gianluigi Ferrari
Dr. Luca Davoli
Dr. Roberto Fresco
Dr. Baran Çürüklü
Prof. Dr. José-Fernán Martínez
DI(FH) Erwin Kristen
Guest Editors

Manuscript Submission Information

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Published Papers (2 papers)

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Research

14 pages, 2509 KiB  
Article
Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service
by Andrej Zgank
Sensors 2020, 20(1), 21; https://doi.org/10.3390/s20010021 - 19 Dec 2019
Cited by 50 | Viewed by 5082
Abstract
Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for [...] Read more.
Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models’ and Gaussian Mixture Models’ topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system. Full article
(This article belongs to the Special Issue IoT-Based Systems for Smart and Sustainable Agriculture)
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16 pages, 6797 KiB  
Article
Harvest Stage Recognition and Potential Fruit Damage Indicator for Berries Based on Hidden Markov Models and the Viterbi Algorithm
by Marcos Orchard, Carlos Muñoz-Poblete, Juan Ignacio Huircan, Patricio Galeas and Heraldo Rozas
Sensors 2019, 19(20), 4421; https://doi.org/10.3390/s19204421 - 12 Oct 2019
Cited by 3 | Viewed by 2867
Abstract
This article proposes a monitoring system that allows to track transitions between different stages in the berry harvesting process (berry picking, waiting for transport, transport and arrival at the packing site) solely using information from temperature and vibration sensors located in the basket. [...] Read more.
This article proposes a monitoring system that allows to track transitions between different stages in the berry harvesting process (berry picking, waiting for transport, transport and arrival at the packing site) solely using information from temperature and vibration sensors located in the basket. The monitoring system assumes a characterization of the process based on hidden Markov models and uses the Viterbi algorithm to perform inferences and estimate the most likely state trajectory. The obtained state trajectory estimate is then used to compute a potential damage indicator in real time. The proposed methodology does not require information about the weight of the basket to identify each of the different stages, which makes it effective and more efficient than other alternatives available in the industry. Full article
(This article belongs to the Special Issue IoT-Based Systems for Smart and Sustainable Agriculture)
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