Drone Data Analytics in Aerial Computing / edited by P. Karthikeyan, Sathish Kumar, V. Anbarasu.

Format:

Book

Author/Creator:

Kārttikēyan̲, Pa.

Contributor:

Kumar, Sathish.

Anbarasu, V.

Series:

Transactions on Computer Systems and Networks, 2730-7492

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Quantitative research.

Computational Intelligence.

Artificial Intelligence.

Data Analysis and Big Data.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Data Analysis and Big Data.

Physical Description:

1 online resource (282 pages)

Edition:

1st ed. 2023.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2023.

Summary:

This book discusses the latest research, theoretical, and experimental research innovations in drone data analytics in aerial computing. Drone data analytics guarantees that the right people have the correct data at their fingertips whenever they need it. The contents also discuss the challenges faced with drone data analytics, such as due to the high mobility of drones, aerial computing is significantly different from terrestrial computing. It also includes case studies from leading drone vendors. The book also focuses on the comparison of data management and security mechanisms in drone data analytics. This book is useful to those working in agriculture, mining, waste management, and defenses department.

Contents:

Introduction to Drone Data Analytics in Aerial computing

A Study in Federated Learning Analytics for UAV

Analysis of Geospatial Data Collected by Drones as Part of Aerial Computing

Beach wrack identification on unmanned aerial vehicles dataset using Artificial Intelligence for Coastal Environmental Management

Environmental drones for autonomous air pollution investigation, detection, and remediation

Detection of Pathogens in Plant Leaves using Drone-based Deep Learning Approach

Artificial Intelligence Based Drones for Plant Disease Detection

Machine vision in UAV Data Analytics for Precision Agriculture

Smart IoT Drone-Rover for Sustainable Crop Prediction Based on Mutual Subset Feature Selection Using U-Net CNN For Sustainable Crop Recommendation

IoT Based Automatic Drip Irrigation Control Using Intelligent Agriculture

IOT-Based Innovative Agriculture Farming System Based on Rover-Drone Surveil-lance Sensing Unit Using Feature Selection and Classification Techniques

Village mapping for micro level planning using UAV technology

An in-sight analysis of Cyber-security Protocols and the Vulnerabilities in the Drone Communication

Introspecting the Impact of Selected Macro-Economic Variables and Policy Interventions in Unmanned Aerial Vehicle (UAV) Sector: The Case of India. .

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9950-56-2

OCLC:

1401059906