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Agronomy
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Application and Promotion of Unmanned Aerial System (UAS) Technology in...
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Application and Promotion of Unmanned Aerial System (UAS) Technology in Agriculture and Forestry

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agricultural Biosystem and Biological Engineering".

Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 13197

Special Issue Editor

Dr. Craig Morley

E-Mail Website
Guest Editor
Toi Ohomai Institute of Technology, Department of Environmental Science, Rotorua 3046, New Zealand
Working in partnership with X-Craft (https://www.x-craft.co.nz/) and Aerospread (https://www.aerospread.co.nz/)
Interests: using UAS systems to manage invasive organisms; biosecurity; environmental management; conservation biology; island ecosystems

Special Issue Information

Dear Colleagues,

Agricultural/Forestry production faces numerous challenges because of an ever-burgeoning population and global climate change. One solution to help farmers and foresters reduce pathogens, disease and crops/trees loss to pest organisms is to use robotic unmanned aerial systems to accurately target pest organisms precisely, thus, reducing costs and harm to non-target species within the environment. To date, innovation has been incremental over several decades because of limited flight-times, payloads, beyond visual line of sight (BVLOS)/unmanned traffic management (UTM) platforms and, how to manage/analyse the data collected. Nevertheless, technology is improving rapidly and the purpose of this special issue is to publish high-quality research articles on how users have been able to overcome these issues.

We would encourage papers on cloud-based data-driven systems using machine learning/artificial intelligence (AI) on UASs that have been specifically designed to manage pest organisms. Thus, the challenge is fusing the data collected from the onboard remote sensors to predict, track and then target pest organisms. By improving BVLOS/UTM capability and developing better data management systems (using AI) we will increase the use of UASs significantly across whole landscapes for better food and forest security. 

Dr. Craig Morley
Guest Editor

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. Agronomy is an international peer-reviewed open access monthly 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 2600 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

  • Precision agriculture (PA)
  • Remote sensing (RS), including global positioning systems (GPS) and geographic information systems (GIS)
  • Robotics
  • Artificial Intelligence
  • Data management
  • Agrochemicals
  • Beyond the visual line of sight (BVLOS)
  • Global climate change

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Published Papers (2 papers)

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19 pages, 3241 KiB  
Article
Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery
by Jorge Torres-Sánchez, Francisco Javier Mesas-Carrascosa, Francisco M. Jiménez-Brenes, Ana I. de Castro and Francisca López-Granados
Agronomy 2021, 11(4), 749; https://doi.org/10.3390/agronomy11040749 - 12 Apr 2021
Cited by 20 | Viewed by 4181
Abstract
Significant advances in weed mapping from unmanned aerial platforms have been achieved in recent years. The detection of weed location has made possible the generation of site specific weed treatments to reduce the use of herbicides according to weed cover maps. However, the [...] Read more.
Significant advances in weed mapping from unmanned aerial platforms have been achieved in recent years. The detection of weed location has made possible the generation of site specific weed treatments to reduce the use of herbicides according to weed cover maps. However, the characterization of weed infestations should not be limited to the location of weed stands, but should also be able to distinguish the types of weeds to allow the best possible choice of herbicide treatment to be applied. A first step in this direction should be the discrimination between broad-leaved (dicotyledonous) and grass (monocotyledonous) weeds. Considering the advances in weed detection based on images acquired by unmanned aerial vehicles, and the ability of neural networks to solve hard classification problems in remote sensing, these technologies have been merged in this study with the aim of exploring their potential for broadleaf and grass weed detection in wide-row herbaceous crops such as sunflower and cotton. Overall accuracies of around 80% were obtained in both crops, with user accuracy for broad-leaved and grass weeds around 75% and 65%, respectively. These results confirm the potential of the presented combination of technologies for improving the characterization of different weed infestations, which would allow the generation of timely and adequate herbicide treatment maps according to groups of weeds. Full article
(This article belongs to the Special Issue Application and Promotion of Unmanned Aerial System (UAS) Technology in Agriculture and Forestry)
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Review

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35 pages, 5685 KiB  
Review
A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario
by Shibbir Ahmed, Baijing Qiu, Fiaz Ahmad, Chun-Wei Kong and Huang Xin
Agronomy 2021, 11(6), 1069; https://doi.org/10.3390/agronomy11061069 - 26 May 2021
Cited by 26 | Viewed by 8410
Abstract
Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of [...] Read more.
Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions. Full article
(This article belongs to the Special Issue Application and Promotion of Unmanned Aerial System (UAS) Technology in Agriculture and Forestry)
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