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

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Keywords

smart agriculture
IoT
machine learning
big data

Published Papers (1 paper)

Research

Open AccessArticle

Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming

Saeid Pourroostaei Ardakani

and

Ali Cheshmehzangi

Telecom 2021, 2(3), 255-270; https://doi.org/10.3390/telecom2030017 - 01 Jul 2021

Viewed by 573

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 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

(This article belongs to the Special Issue Applications of Communication Technologies in Agriculture: IoT, Machine Learning, and Big Data)

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