Special Issue "Using Ground Robots and Unmanned Aerial Vehicles towards Smart Farming"
Deadline for manuscript submissions: closed (31 December 2021).
2. Department of Computer Science, University of Twente, Enschede 7514, The Netherlands
Interests: Internet of Things; deep learning; remote sensing
Interests: microbiology; environment; soil; molecular biology; energy; sustainability agricultural
Special Issues, Collections and Topics in MDPI journals
Although agriculture has evolved enormously in the past several decades, increasing production and productivity to unprecedented levels, it still faces challenges due to increasing demands for food, the degradation of physical environments (i.e., pollution, contamination of soils and waters), climate change, pests and diseases, etc.
Agriculture has been evolving mainly due to technological advancements in the field. Modern agricultural management embraces intensive and extensive information from Earth observations, sophisticated machinery, and high-precision field sensors. The quantity and quality of the available information have improved agricultural productivity and quality substantially in the past decade, enabling precision agriculture.
Until recently, Earth observations originated from satellite systems. However, information was generally available in low resolution, at not very frequent intervals (and definitely not on demand), and was error-prone (e.g., from the presence of clouds or storms).
Unmanned aerial vehicles (UAVs) can help to solve these problems, providing high-resolution aerial observations of the field on demand, allowing farmers to take more informed decisions in real-time. In combination with advanced computer vison (CV) techniques such as machine learning and deep learning (DL), UAVs and aerial photography enable a wide variety of applications related to classification and/or prediction in agricultural fields.
To better understand the challenges and opportunities in integrating UAVs with existing smart farming systems, this Special Issue invites contributions on:
- Innovative applications of UAVs in agriculture;
- Advanced CV-based models and applications for agriculture originating from UAVs;
- Integration of UAVs with existing farming systems, such as modern machinery and Internet of Things sensors;
- Use of multispectral/hyperspectral imaging from UAVs for tackling agricultural challenges;
- Use of UAVs/DL for solving agriculture-related problems such as monitoring of crop growth and health, identifying pests and diseases, mapping and predicting yields, monitoring livestock in real-time etc.;
- Swarms of UAVs for addressing agricultural challenges more efficiently (i.e., in space, time, and energy);
- Use of UAVs/DL to understand the environmental impact of agriculture on sustainability.
Dr. Andreas Kamilaris
Dr. Francesc Xavier Prenafeta-Boldú
Prof. Peter Stütz
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 papers will be 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 2500 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.
- deep learning
- unmanned aerial vehicles
- remote sensing
- computer vision
- precision agriculture
- smart farming
- multispectral imagery
- hyperspectral imagery
- crop growth and health
- pest and diseases
- yield mapping and prediction
- livestock monitoring
- environmental impact