Special Issue "Applications of Communication Technologies in Agriculture: IoT, Machine Learning, and Big Data"

A special issue of Telecom (ISSN 2673-4001).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 3930

Special Issue Editors

Dr. Sotirios K. Goudos
E-Mail Website
Guest Editor
[email protected], Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: antenna design; microwave components design; wireless communications; evolutionary algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Paolo Rocca
E-Mail Website
Guest Editor
[email protected] – DISI, University of Trento, Trento, Italy
Interests: antenna array synthesis and design; electromagnetic inverse scattering; optimization techniques for electromagnetics
Prof. Dr. Demosthenes Vouyioukas
E-Mail Website
Guest Editor
Department of Information and Communication Systems Engineering, School of Engineering, University of the Aegean, 83200 Samos, Greece
Interests: mobile and wireless communication systems; channel characterization and propagation models; performance modeling of wireless networks; opportunistic mobile networks; cooperative communications; satellite and aerial networks
Special Issues, Collections and Topics in MDPI journals
Dr. Achilles D. Boursianis
E-Mail Website
Guest Editor
[email protected], Department of Physics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
Interests: antenna design; microwave components design; wireless communications; evolutionary algorithms; machine learning

Special Issue Information

Dear Colleagues,

The world of agriculture is undergoing a profound transformation. Applications of emerging communications in the agricultural industry are continuously developing. Smart agriculture has the ability to deliver a new era of innovative cultivation systems that will be based on a combination of new information and communication technologies. In order to do so, several technological challenges have to be met. New types of sensors, the Internet of Things (IoT), machine learning (ML), and big data analysis (BDA) will help farmers to sustainably and profitably manage their holdings. We invite researchers to contribute original papers describing applications and systems on the emerging trends of communication technologies for addressing various issues in the agricultural domain.

Potential topics include but are not limited to the following:

  • IoT applications in agriculture;
  • Cloud-, fog-, and edge-based systems in smart farming;
  • Crop models and decision support systems in smart farming;
  • Utilization of big data in sustainable agriculture;
  • Big data innovation in sustainable agriculture;
  • Machine learning for agricultural applications;
  • Deep learning for agricultural applications;
  • Communication technologies in smart agriculture;
  • Smart irrigation systems for yield improvement;
  • Use of robotics and IoT for cultivation processes automation;
  • Use of IoT in forestry management;
  • RF energy harvesting applications in agriculture;
  • UAV/FANET for agricultural applications;
  • Gamification in smart agriculture.

Prof. Sotirios K. Goudos
Prof. Paolo Rocca
Prof. Demosthenes Vouyioukas
Prof. Shaohua Wan
Dr. Achilles D. Boursianis
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. Telecom is an international peer-reviewed open access quarterly 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 1000 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 agriculture
  • IoT
  • machine learning
  • big data

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
Telecom 2022, 3(1), 70-85; https://doi.org/10.3390/telecom3010004 - 06 Jan 2022
Viewed by 972
Abstract
Actual and upcoming climate changes will evidently have the largest impact on agriculture crop cultivation in terms of reduced harvest, increased costs, and necessary deviations from traditional farming. The aggravating factor for the successful applications of precision and predictive agriculture is the lack [...] Read more.
Actual and upcoming climate changes will evidently have the largest impact on agriculture crop cultivation in terms of reduced harvest, increased costs, and necessary deviations from traditional farming. The aggravating factor for the successful applications of precision and predictive agriculture is the lack of granulated historical data due to slow, year-round cycles of crops, as a prerequisite for further analysis and modeling. A methodology of plant growth observation with the rapid performance of experiments is presented in this paper. The proposed system enables the collection of data with respect to various climate conditions, which are artificially created and permuted in the encapsulated design, suitable for further correlation with plant development identifiers. The design is equipped with a large number of sensors and connected to the central database in a computer cloud, which enables the interconnection and coordination of multiple geographically distributed devices and related experiments in a remote, autonomous, and real-time manner. Over 40 sensors and up to 24 yearly harvests per device enable the yearly collection of approximately 750,000 correlated database entries, which it is possible to independently stack with higher numbers of devices. Such accumulated data is exploited to develop mathematical models of wheat in different growth stages by applying the concepts of artificial intelligence and utilizing them for the prediction of crop development and harvest. Full article
Show Figures

Figure 1

Article
Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming
Telecom 2021, 2(3), 255-270; https://doi.org/10.3390/telecom2030017 - 01 Jul 2021
Cited by 1 | Viewed by 1523
Abstract
UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route [...] Read more.
UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm. Full article
Show Figures

Figure 1

Back to TopTop