IoT Devices vs. Drones for Data Collection in Agriculture

Petkovics, Imre; Petkovic, Djerdji; Petkovics, Ármin

doi:10.2507/daaam.scibook.2017.06

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Surveillance scenarios (eg: traffic, power line, agriculture and environment) are one of the key aspects in IoT. UAVs can be used to facilitate the requirements of these applications [138], [139]. In [140], Motlagh et al presented an integrative IoT platform operational in the sky, using UAVs equipped with diverse IoT devices.…”

Section: H Uavs and Dronesmentioning

confidence: 99%

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Wijethilaka

Liyanage

2021

IEEE Commun. Surv. Tutorials

292

124

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Internet of Things (IoT) is an emerging technology that makes people's lives smart by conquering a plethora of diverse application and service areas. In near future, the fifthgeneration (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e. slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning(AI/ML) in network slicing and IoT integration.

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Section: H Uavs and Dronesmentioning

confidence: 99%

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Wijethilaka

Liyanage

2021

IEEE Commun. Surv. Tutorials

292

124

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“…On the other hand, Multirotor UAVs have slower flight speeds and smaller payloads, and their maximum flight time is approximately half that of fixed-wing ones. However, they are most suited for smaller spaces that are not easily accessible and are considerably cheaper than fixed-wing ones [62]. In general, flight time and range, initial cost (e.g., imaging sensors, hardware, software, and tools), national laws, connectivity, weather dependency, and the need for specialized knowledge and skills are specific blockages for UAV-based farming development [18], [63].…”

Section: Architecture Of Uav Systemsmentioning

confidence: 99%

A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles

et al. 2022

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With the ever-increasing importance of dairy and meat production, precision livestock farming (PLF) using advanced information technologies is emerging to improve farming production systems. The latest automation, connectivity, and artificial intelligence developments open new horizons to monitor livestock in the pasture, controlled environments, and open environments. Due to the significance of livestock detection and tracking, this systematic review extracts and summarizes the existing deep learning (DL) techniques in PLF using unmanned aerial vehicles (UAV). In the context of livestock recognition studies, UAVs are receiving growing attention due to their flexible data acquisition and operation in different conditions. This review examines the implemented DL architectures and scrutinizes the broadly exploited evaluation metrics, attributes, and databases. The classification of most UAV livestock monitoring systems using DL techniques is in three categories: detection, classification, and localization. Correspondingly, this paper discusses the future benefits and drawbacks of these DL-based PLF approaches using UAV imagery. Additionally, this paper describes alternative methods used to mitigate issues in PLF. The aim of this work is to provide insights into the most relevant studies on the development of UAV-based PLF systems focused on deep neural network-based techniques.

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“…Of course, in the modern world there are many suppliers of automated measuring devices: from small and simple Arduino-based microcontrollers to multi-functional and expensive weather stations or even drones [6]. The approach to collecting climate data also differs greatly: older models of meters store data locally on a flash card, and the most modern models use cloud storage and are created in the technological approach of "big data" [7,8].…”

Section: Introductionmentioning

confidence: 99%

IoT-based agriculture monitoring platform architecture

Zorkin

Иванова

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The research is aimed at solving one of the most urgent tasks of our time: the development of reliable and scalable software for monitoring the climate of agricultural fields. The goal is to create an architecture of simple and universal systems for collecting and storing data from various measuring devices located on agricultural land. The methodological basis for the monitoring system is the “Internet of Things” (IoT) approach. The agro-industrial complex of the Tomsk region (Russia) was chosen as a model object. About a hundred measuring devices have been installed in agricultural fields, which provide continuous delivery of data series in real time. Our developed platform includes both the server part of the application that processes and stores these weather data, and a convenient user interface for visualizing data to agronomists. The results of the research can be used in designing the architecture of similar IoT-based monitoring systems.

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IoT Devices vs. Drones for Data Collection in Agriculture

Cited by 10 publications

References 4 publications

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Survey on Network Slicing for Internet of Things Realization in 5G Networks

A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles

IoT-based agriculture monitoring platform architecture

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