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Remote Sensing
Special Issues
UAV Agricultural Management: Recent Advances and Future Prospects
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UAV Agricultural Management: Recent Advances and Future Prospects

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 25 December 2024 | Viewed by 8334

Special Issue Editors

Dr. Hamdi Zurqani

E-Mail Website
Guest Editor
University of Arkansas Agricultural Experiment Station, Arkansas Forest Resources Center, University of Arkansas, Monticello, AR, USA
Interests: environmental information science; GeoAI; GIS and remote sensing; LiDAR; big geospatial data analytics; land evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Matese

E-Mail Website
Guest Editor
Institute of BioEconomy, National Research Council (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy
Interests: environmental governance; institutional and ecological economics; climate change; biodiversity protection; land management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing involves collecting data about crops and soil conditions without physical contact. Recent advancements in UAV technology have made UAV remote sensing systems popular owing to their mobile, rapid, and economical characteristics. UAV remote sensing has gradually advanced from being employed solely in research to its utilization in practical applications, such as precision agriculture, vegetated area monitoring, crop area mapping, wildlife research, archaeological application, and environmental monitoring. This technology has the advantages of automation, intelligence, and specialization, enabling the rapid acquisition of remote sensing information about land, resources, environment, and events. Moreover, UAV remote sensing provides the real-time processing, modeling, and analysis of advanced emerging aerial remote sensing technology solutions. Due to its low cost and high level of detail, UAV remote sensing is revolutionizing many agricultural, environmental, archaeological, and surveillance applications by offering the repeated acquisition of high-resolution image data over large areas.

One of the critical advantages of UAV remote sensing in agriculture is its ability to provide accurate and detailed information without causing damage to crops. This is possible by combining UAV remote sensing with advanced spectral imaging techniques, which can monitor farm crop growth in real-time, generating farmland crop prescription maps based on high-resolution spectral images. This plays a vital role in monitoring crop growth, identifying issues, and creating targeted intervention plans. However, it is essential to note that low-altitude remote sensing operations are bound to affect ground crops due to the rotor wind field of UAVs.

This Special Issue aims to explore the recent advances and prospects of UAV (Unmanned Aerial Vehicle) agricultural management. It seeks to provide a platform for researchers, scientists, and practitioners to showcase their innovative research and discuss the potential of UAV technology in revolutionizing agricultural practices. The Special Issue will address a wide range of topics, including, but not limited to, remote sensing, image analysis, data processing, and the applications of UAVs in agriculture. By highlighting the latest advancements in using unmanned aerial vehicle (UAV) technology in agricultural management and discussing future directions, this Special Issue aims to contribute to the growing body of knowledge in the field of UAV agricultural management. Research articles, review articles and short communications are welcome.

Dr. Hamdi Zurqani
Dr. Alessandro Matese
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • enhanced crop monitoring using UAVs
  • real-time monitoring of plant health
  • optimal resource allocation for farming
  • detecting and preventing crop diseases
  • automated irrigation systems using UAVs
  • improved pest management strategies
  • high-resolution data for yield prediction
  • timely identification of nutrient deficiencies
  • enhanced crop scouting capabilities
  • minimizing chemical input with UAVs
  • optimizing harvest timing using drones
  • sustainable agriculture through UAVs

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Published Papers (1 paper)

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Review

42 pages, 20744 KiB  
Review
A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions
by Md Habibur Rahman, Mohammad Abrar Shakil Sejan, Md Abdul Aziz, Rana Tabassum, Jung-In Baik and Hyoung-Kyu Song
Remote Sens. 2024, 16(5), 879; https://doi.org/10.3390/rs16050879 - 1 Mar 2024
Cited by 5 | Viewed by 7158
Abstract
Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital [...] Read more.
Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches. Full article
(This article belongs to the Special Issue UAV Agricultural Management: Recent Advances and Future Prospects)
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