Special Issue "Applications of Unmanned Aerial Vehicle (UAV) and Ground-Based Platforms for High Throughput Crop Phenotyping"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Dr. Erik Alexandersson
Website
Guest Editor
Swedish University of Agricultural Sciences, Department of Plant Protection Biology, POB 102, Alnarp SE-23053, Sweden
Interests: Functional genomics and ‘-omics’ data in field trials; induced resistance in potato varieties; potato late blight; transcriptomics and proteomics; plant defense; biofortification of cassava; botanicals and plant resistance
Special Issues and Collections in MDPI journals
Dr. Gerrit Polder
Website
Guest Editor
Computer Vision and Plant Phenotyping, Wageningen University and Research - Business Unit Greenhouse Horticulture, Building 107, Droevendaalsesteeg 1, 6708 PB WageningenP.O. Box 644, Wageningen 6700 AP, the Netherlands.
Interests: Image processing and analysis; hyperspectral and multispectral imaging; spectroscopy, computer vision for plant phenotyping; 3D imaging; agricultural robotics; machine vision; machine learning
Dr. Junfeng Gao
Website
Guest Editor
Lincoln Institute for Agri-food Technology, University of Lincoln, Brayford Way, Brayford Pool, Lincoln LN6 7TS, UK
Interests: Agricultural robotics; computer vision; machine learning; plant phenotyping; crop disease and weed detection

Special Issue Information

Dear Colleagues,

High-throughput plant phenotyping is a rapidly emerging research area concerned with quantitative measurements of structural and functional plant properties as well as plant performance and health. The use of unmanned aerial vehicles (UAVs) and ground-based platforms, such as robots, that can be equipped with a variety of sensors enables a faster and more efficient method to collect a large amount of data. Crop phenotyping analysis is the foundation for crop breeding. Traditionally, crop phenotypes are measured manually, which is time-consuming and labor-intensive and sometimes requires destructive sampling for certain readouts. Currently, advances in artificial intelligence, such as machine learning and deep learning algorithms, big data analysis, and computer vision techniques, will further assist the crop phenotyping community to address limitations of using conventional approaches to phenotype analysis.

In this context, we welcome papers that present primary research, novel analytical methods, and applications including standard plant trait measurements, experimental technologies, hardware development for data collection, and software development, as well as reviews and opinion pieces in the field of high-throughput crop phenotype analysis both in field and controlled environments.

Dr. Erik Alexandersson
Dr. Gerrit Polder
Dr. Junfeng Gao
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 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. 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 1600 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

  • Remote sensing
  • Machine learning
  • Image analysis
  • Data analytics
  • Plant traits
  • Automation and robotics

Published Papers

This special issue is now open for submission.
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