Special Issue "Algorithms for Plant Phenotyping Imaging: Turning Today’s Limitations into Tomorrow’s Strengths"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editor

Dr. Nicolas Virlet
E-Mail Website
Guest Editor
Rothamsted Research, Plant Sciences Department, West Common, AL55NX, Harpenden, UK
Interests: Field phenotyping; plant physiology; quantitative genetics; spectral imaging; nutrition

Special Issue Information

Dear Colleagues,

The last decade has seen an exponential increase in methodological articles in the area of high throughput phenotyping imaging. Most of them aim to extract quantitative and qualitative information about plants development in response to their environments. This diversity in algorithms is reflecting:

  • the broad range of species and their growing conditions (control environment to field, single plant to plot canopy or tree),
  • the growth dynamic across the lifecycle (young seedling to mature plants - reproductive stages),
  • the organs considered,
  • the wide range of traits,
  • the type of technology: sensors/camera (RGB, multi- hyperspectral, fluorescence, thermal infrared, lidar technologies and so on) and the vectors (from hand-pole to aircraft or nanosatellite).

The computer science community either aims to develop algorithms enabling a direct quantification of the desired trait or to build proxies replacing the more traditional data collection methods.While some traits measurements (ex: height) rely on well-established methodologies and algorithms, others (ex: proxy for visual scoring of stage of development in field condition) remain complicated to estimate. Besides, the difficulty often resides in having a robust algorithm to extract the information in a dynamic environment (ambient illumination, crop growth…) rather than in the trait itself.

The strength and potentials of the proposed methods are generally well-highlighted in the articles whereas, their weaknesses and limitations are too infrequently discussed in detail. Today’s limitations should be shaping the next generation of studies. As most of the algorithms rely on multiple steps, a deeper understanding of their underlying processes would provide meaningful information on both strengths and weaknesses, thus defining the next challenges for the community.

In this special issue, authors are invited to submit research paper providing new methods or using current ones, emphasizing their limitations in the context of their studies. Review or opinion papers are also welcome.

All species, phenotyping platforms and sensor technologies are welcome. The aims here is to highlight the challenges in algorithm development for tomorrow.

Dr. Nicolas Virlet
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 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 2400 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

  • Quantitative information extraction
  • Spatial/spectral information extraction
  • Photogrammetry Image processing
  • Image classification
  • Machine learning
  • Computer vision
  • Phenotyping Time series

Published Papers (1 paper)

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Research

Article
An Efficient Method for Estimating Wheat Heading Dates Using UAV Images
Remote Sens. 2021, 13(16), 3067; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163067 - 04 Aug 2021
Viewed by 365
Abstract
Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to [...] Read more.
Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading dates. Phenology models usually require complicated parameters calibrations, making it difficult to model other varieties and different locations, while in situ field-image recognition usually requires the deployment of a large amount of observational equipment, which is expensive. Therefore, in this study, we proposed a growth curve-based method for estimating wheat heading dates. The method first generates a height-based continuous growth curve based on five time-series unmanned aerial vehicle (UAV) images captured over the entire wheat growth cycle (>200 d). Then estimate the heading date by generated growth curve. As a result, the proposed method had a mean absolute error of 2.81 d and a root mean square error of 3.49 d for 72 wheat plots composed of different varieties and densities sown on different dates. Thus, the proposed method is straightforward, efficient, and affordable and meets the high-throughput estimation requirements of large-scale fields and underdeveloped areas. Full article
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