Special Issue "Machine Learning Applications in Smart Agriculture"

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

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 2655

Special Issue Editors

Dr. Panagiotis Sarigiannidis
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: IoT; 5G mobile communication; UAV; quality of service; radio access networks; computer network security; radio networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Alexandros-Apostolos Boulogeorgos
E-Mail Website
Guest Editor
Digital Systems, University of Piraeus, Piraeus, Greece & Electrical and Computer Engineering, University of Western Macedonia, 5010 Kozani, Greece
Interests: wireless communications; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To deal with the rising challenges and barriers of the agricultural domain, the complex agricultural landscape needs to be better investigated and understood. In order for this to happen, every aspect of the agricultural ecosystem, including both pre-production (sowing, treatment, inputs) and post-production activities (harvesting, labeling, shipping) as well consumer behavior, produces some data that must be further analyzed.

Data gathering throughout the agricultural ecosystem has already been facilitated through the use of recent technological developments in Information and Communication Technology (ICT), such as the Internet of Things (IoT), radio-frequency identification (RFID) systems, wireless sensor networks (WSNs), and unmanned aerial vehicles (UAVs). However, the processing of all this information constitutes a major obstacle. The multi-collectiveness, extreme volume, and high velocity of this information, in combination with the complexity of the agricultural domain, forms a complicated system, which hinders modern agriculture reach its true potential.

In the coming years, big data are expected to pave the way for a productive and sustainable rural development as they offer unprecedented capabilities and can drive innovative concepts, such as smart farming, precision agriculture, and smart livestock. Toward this direction, machine learning technologies envision a wide range of innovative applications that offer considerable benefits to agronomists and farmers, such as enhanced production of high quality, efficient resource allocation, germinal disease recognition, climate change mitigation, income increase, cost and laborious task decrease, animal welfare, and more.

However, several of the aforementioned expectations have not been delivered yet, and additional research efforts are needed to utilize machine learning applications in the new era of smart farming. The purpose of this Special Issue is to publish high-quality research papers, as well as review articles in the emerging research field of machine learning applications in smart agriculture, which are addressing the following topics:

  • Machine learning algorithms for predicting crop yield;
  • Machine learning algorithms for predicting livestock production;
  • Machine learning algorithms for demand and supply predictions in primary production;
  • Machine learning and computer vision algorithms for early detection and diagnosis of a crop disease/anomaly;
  • Machine learning algorithms for early detection and diagnosis of livestock diseases;
  • Tools and approaches for crop health monitoring;
  • Tools and approaches for livestock welfare monitoring;
  • Tools and methods for providing advice and guidance to farmers based on their crops' responsiveness to input;
  • Tools and approaches for field clustering based on crop conditions (production, damage, inputs);
  • Machine learning and computer vision schemes for weed detection and fruit grading;
  • Tools and approaches for combining multi-source structured and/or unstructured data;
  • Machine learning and computer vision algorithms for species recognition;
  • Concepts and approaches for data privacy and security in the agricultural domain;
  • Tools and methodologies for conducting scientific models and simulations for environmental phenomena;
  • Tools and methods for quantitative analysis of the interaction between crops and their environment.

Prof. Dr. Panagiotis Sarigiannidis
Dr. Thomas Lagkas
Dr. Alexandros-Apostolos A. Boulogeorgos
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.

Published Papers (1 paper)

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Research

Article
Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems
Telecom 2021, 2(1), 52-74; https://doi.org/10.3390/telecom2010005 - 09 Feb 2021
Cited by 7 | Viewed by 2003
Abstract
Climate change is emerging as a major threat to farming, food security and the livelihoods of millions of people across the world. Agriculture is strongly affected by climate change due to increasing temperatures, water shortage, heavy rainfall and variations in the frequency and [...] Read more.
Climate change is emerging as a major threat to farming, food security and the livelihoods of millions of people across the world. Agriculture is strongly affected by climate change due to increasing temperatures, water shortage, heavy rainfall and variations in the frequency and intensity of excessive climatic events such as floods and droughts. Farmers need to adapt to climate change by developing advanced and sophisticated farming systems instead of simply farming at lower intensity and occupying more land. Integrated agricultural systems constitute a promising solution, as they can lower reliance on external inputs, enhance nutrient cycling and increase natural resource use efficiency. In this context, the concept of Climate-Smart Agriculture (CSA) emerged as a promising solution to secure the resources for the growing world population under climate change conditions. This work proposes a CSA architecture for fostering and supporting integrated agricultural systems, such as Mixed Farming Systems (MFS), by facilitating the design, the deployment and the management of crop–livestock-=forestry combinations towards sustainable, efficient and climate resilient agricultural systems. Propelled by cutting-edge technology solutions in data collection and processing, along with fully autonomous monitoring systems, e.g., smart sensors and unmanned aerial vehicles (UAVs), the proposed architecture called MiFarm-CSA, aims to foster core interactions among animals, forests and crops, while mitigating the high complexity of these interactions, through a novel conceptual framework. Full article
(This article belongs to the Special Issue Machine Learning Applications in Smart Agriculture)
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