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Special Issue "High-Throughput Phenotyping of Crop Traits: Progresses, Opportunities, and Challenges"
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 21073
Special Issue Editors
E-Mail Website1 Website2
Interests: remote sensing; plant physiology; urban climate; soil science; machine learning; digital agriculture; ecology
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Interests: photosynthesis; in-field crop phenotyping; food security
Interests: remote sensing of vegetation structure, function, and traits; biogeochemical cycling of carbon, water, and energy; physiological ecology; global change and its ecological impacts; model–data fusion
2. Global Change and Photosynthesis Unit, United States Department of Agriculture-Agricultural Research Service, Urbana, IL 61801, USA
Interests: plant biology; biofuels; photosynthesis; global change; agriculture
Special Issue Information
Improved crop productivity is of paramount importance to meet increasing agricultural demands associated with exponential population growth. These increasing demands will be challenged further with the pressures from climate change and with the world’s shrinking farmlands. Solutions to overcome this daunting challenge include breeding better crops through both traditional techniques and genetic modifications. Currently, a wealth of genomic information associated with crop productivity is available; yet linking these resources to crop phenotypes under field conditions is severely limiting. This phenotypic bottleneck restricts advances in crop improvement.
To bridge this knowledge gap, advances in high-throughput phenotyping (HTP) are highly sought to estimate crop phenotypic traits in a nondestructive, rapid, and cost-effective way. The rapid advancements in sensor technologies and low-cost sensing platforms are projected to ease the crop phenotyping bottleneck and offer scientists with rich data to help to seek the ways to improve crop productivity.
This Special Issue aims at showcasing the latest developments in HTP platforms (HTPPs), sensing technologies, and methodological advances to measure crop phenotypic traits from a proximal and remote sensing perspective. We also welcome review papers to synthesize the recent progresses of high-throughput phenotyping and to discuss those grand challenges remaining unresolved. In this Special Issue, potential topics include but are not limited to:
- High-throughput phenotyping platforms (HTPPs), such as unmanned aerial vehicles, robots, and gantries that have an important component in close-range/remote sensing;
- Innovative use of new sensors to collect phenotypic data (e.g., LiDAR, solar-induced florescence, thermal sensor);
- State-of-the-art techniques to process phenotypic measurements (e.g., deep learning);
- Data fusion (e.g., fusion of multisource data, such as structural, optical, physiological, and thermal data) for understanding plant growth;
- Advances in hyperspectral remote sensing for phenotyping;
- Phenotyping of plant stress (e.g., disease and drought stress).
Topics should be related to improving agricultural output, which includes all facets of agriculture, such as yield quantity, yield quality, crop health, agroecosystem services, etc.
Dr. Peng Fu
Dr. Katherine Meacham-Hensold
Dr. Jin Wu
Prof.Dr. Carl Bernacchi
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 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.
- high-throughput phenotyping
- close-range/remote sensing
- crop productivity
- data fusion
- plant stress
- hyperspectral image processing
- crop traits
- agroecosystem services
- plant physiology