Special Issue "Precision Agriculture, Horticulture and Forestry: Extracting Canopy Information from Drone Imagery for Management and Decision-Making"
A special issue of Drones (ISSN 2504-446X).
Deadline for manuscript submissions: closed (31 May 2020).
G10 - UCEMM, Department of Geography and Environment, School of Geosciences, University of Aberdeen, St. Mary's, Elphinstone Road, Aberdeen AB24 3UF, UK
Interests: UAVs; geographical information systems (GIS); remote sensing and digital image processing (DIP); cartography and digital mapping; global positioning systems (GPS); WebGIS; mobile GIS; internet applications; precision viticulture; vineyard management and viticulture; marine and coastal zone management; coastal and marine spatial planning (CMSP); landscape ecology and biodiversity; human–computer interfaces (HCI); geo-visualization; colour in map design; journalistic cartography; GIS in school education
Special Issues and Collections in MDPI journals
Interests: Pre-symptomatic disease detection; pest and disease monitoring; pest and disease management, host–pathogen interaction; future agriculture scenarios
The rapid evolution of drones, UAVs, unmanned ground-based vehicles (UGV) and drone-related technologies including software has seen the development of many environmental applications in recent years. Precision agriculture and horticulture have been one such area that has seen a growing role for image acquisition and processing, and more recently 3D models. Drones have been applied to monitor crop area, to estimate yield, crop water and nutritional status, extract forest canopy information, as well as to oversee and monitor animals on farmland. Additionally, several concepts have been developed to both detect and spray diseased crop plants. Several low-cost commercial applications have also been developed to provide the horticulturalist or farmer with ‘plug and play’ solutions that can be easily deployed in the field. For example, they can be used to acquire imagery from different sensors and processed online in the Cloud to provide information for real-time decision-making.
Advances in computer vision and the parallel development of Unmanned Aerial Vehicles (UAVs) allows for extensive use of UAV in forest inventory and indirect measurements of tree features. Artificial intelligence (AI) is also permitting accurate autonomous flight and detailed information extraction for use in farm management decision support systems (DSS). Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. Progress in the generation of high-resolution 3D canopy models—using Structure from Motion (SfM) software—has led to the extraction of detailed canopy structure information from such imagery. These exploit high-resolution UAV imagery with variables extracted from such imagery contributing to the improved identification of the effects of canopy structure and leaf biochemistry on crops. In other applications, stereo UAV imagery has been used to extract tree canopy heights and multi-spectral imagery has been demonstrated as an accurate and efficient means to measure various tree structural attributes. The determination of tree height is important, mainly because of its biological and commercial importance. It is a significant indicator, which reflects the site productive capacity of the species concerned, when it is growing on a particular site. Image analysis has been used to assess crop development at the emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency Other research will focus on how to use the anisotropy signal as a source of information for the estimation of physical vegetation properties.
These technological attempts to try to solve numerous problems by analyzing airborne imaging and taking airborne actions (e.g., fungicide applications) require interdisciplinary skills and combine biological and technological knowledge as well as IT skills in terms of programming the drones and analyzing the obtained data. This Special Issue, therefore, welcomes scientific papers from authors working in the field of drone applications in precision agriculture, horticulture, and forestry. It will bring together studies presenting results on crop monitoring, applications working with drones as a practical tool such as the application of fungicides, and drone applications in forestry and livestock, focusing on the extraction of information from UAV imagery for crop management and decision support systems. It will cover technical developments of drones, their sensors, applications, and case studies. The Special Issue will not be limited to aerial drones but will also include ground-based unmanned vehicles as well.
Prof. Dr. David R. Green
Dr. Johannes Fahrentrapp
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. Drones is an international peer-reviewed open access quarterly 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 1000 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.
- crop monitoring
- 3D modelling
- artificial intelligence (AI)
- information extraction