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Special Issue "Advanced Imaging for Plant Phenotyping"

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

Deadline for manuscript submissions: 1 September 2019.

Special Issue Editors

Guest Editor
Dr. Jan Behmann

University of Bonn, Institute of Plant Sciences and Resource Conservation, Nussallee 9, 53115 Bonn, Germany
Website | E-Mail
Interests: hyperspectral imaging; computer vision; camera calibration; machine learning; plant phenotyping
Guest Editor
Dr. Lasse Klingbeil

University of Bonn, Institute of Geodesy and Geoinformation, Nussallee 17, 53115 Bonn, Germany
Website | E-Mail
Interests: mobile multi-sensor systems; 3D Mapping; sensor fusion; precision agriculture; image- and laser-based plant phenotyping
Guest Editor
Dr. Stefan Paulus

IFZ-Institute for Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
Website | E-Mail
Interests: 3D and hyperspectral plant imaging; computer vision; plant phenotyping; machine learning

Special Issue Information

Dear Colleagues,

Plant phenotyping is an emerging topic involving the application of digital methods to the highly relevant task of optimizing the genetic potential, cultivation methods, and resource deployment in plant production. In transdisciplinary research, state-of-the-art sensors and data analysis concepts are combined to derive reliable plant-physiological parameters at an increasing throughput.

Plant phenotyping comprises technologies that derive parameters of the plant phenotype as a consequence of the interaction of its genotype with the environmental conditions. To cope with the natural variability in phenotypic expression, a high number of samples will be evaluated in most cases. Phenotypic parameters are expressed at various scales, from single leaves at the plant level, up to crop stands also regarding the plant–plant interaction. In most cases, the reaction of the plant to a specific environmental stress (e.g. drought, plant diseases, or nutrient efficiency) is recorded and used to evaluate the performance of the genotype under these specific conditions.

We welcome papers from the global research community actively involved in research on imaging for plant phenotyping. As such, this Special Issue is open to anyone doing research in this field. The selection of papers for publication will depend on quality and rigor of research. Specific topics include, but are not limited to advanced methods for imaging technologies, sensor setups, and data processing in plant phenotyping:

  • Panchromatic, multispectral, and hyperspectral approaches;
  • 3D imaging techniques adapted to plants;
  • High-throughput sensor platforms;
  • Robotics for phenotyping;
  • Field phenotyping;
  • Stress detection;
  • Disease detection;
  • Data analysis in plant phenotyping;
  • Multi-scale phenotyping;
  • Multi-sensor phenotyping.

Dr. Jan Behmann
Dr. Lasse Klingbeil
Dr. Stefan Paulus
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. 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 1800 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

  • panchromatic, multispectral, and hyperspectral approaches 
  • 3D imaging techniques adapted to plants
  • high-throughput sensor platforms 
  • robotics for phenotyping 
  • field phenotyping 
  • stress detection 
  • disease detection 
  • data analysis in plant phenotyping
  • multi-scale phenotyping 
  • multi-sensor phenotyping

Published Papers (3 papers)

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Research

Open AccessArticle
Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range
Remote Sens. 2019, 11(12), 1401; https://doi.org/10.3390/rs11121401
Received: 29 April 2019 / Revised: 3 June 2019 / Accepted: 7 June 2019 / Published: 12 June 2019
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Abstract
Previous plant phenotyping studies have focused on the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) range. The ultraviolet range (UV, 200–380 nm) has not yet been used in plant phenotyping even though a number of plant [...] Read more.
Previous plant phenotyping studies have focused on the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) range. The ultraviolet range (UV, 200–380 nm) has not yet been used in plant phenotyping even though a number of plant molecules like flavones and phenol feature absorption maxima in this range. In this study an imaging UV line scanner in the range of 250–430 nm is introduced to investigate crop plants for plant phenotyping. Observing plants in the UV-range can provide information about important changes of plant substances. To record reliable and reproducible time series results, measurement conditions were defined that exclude phototoxic effects of UV-illumination in the plant tissue. The measurement quality of the UV-camera has been assessed by comparing it to a non-imaging UV-spectrometer by measuring six different plant-based substances. Given the findings of these preliminary studies, an experiment has been defined and performed monitoring the stress response of barley leaves to salt stress. The aim was to visualize the effects of abiotic stress within the UV-range to provide new insights into the stress response of plants. Our study demonstrated the first use of a hyperspectral sensor in the UV-range for stress detection in plant phenotyping. Full article
(This article belongs to the Special Issue Advanced Imaging for Plant Phenotyping)
Figures

Figure 1

Open AccessArticle
3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images
Remote Sens. 2019, 11(9), 1110; https://doi.org/10.3390/rs11091110
Received: 6 April 2019 / Revised: 28 April 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
PDF Full-text (12117 KB) | HTML Full-text | XML Full-text
Abstract
Wheat is the main food crop today world-wide. In order to improve its yields, researchers are committed to understand the relationships between wheat genotypes and phenotypes. Compared to progressive technology of wheat gene section identification, wheat trait measurement is mostly done manually in [...] Read more.
Wheat is the main food crop today world-wide. In order to improve its yields, researchers are committed to understand the relationships between wheat genotypes and phenotypes. Compared to progressive technology of wheat gene section identification, wheat trait measurement is mostly done manually in a destructive, labor-intensive and time-consuming way. Therefore, this study will be greatly accelerated and promoted if we can automatically discover wheat phenotype in a nondestructive and fast manner. In this paper, we propose a novel pipeline based on 3D morphological processing to detect wheat spike grains and stem nodes from 3D X-ray micro computed tomography (CT) images. We also introduce a set of newly defined 3D phenotypes, including grain aspect ratio, porosity, Grain-to-Grain distance, and grain angle, which are very difficult to be manually measured. The analysis of the associations among these traits would be very helpful for wheat breeding. Experimental results show that our method is able to count grains more accurately than normal human performance. By analyzing the relationships between traits and environment conditions, we find that the Grain-to-Grain distance, aspect ratio and porosity are more likely affected by the genome than environment (only tested temperature and water conditions). We also find that close grains will inhibit grain volume growth and that the aspect ratio 3.5 may be the best for higher yield in wheat breeding. Full article
(This article belongs to the Special Issue Advanced Imaging for Plant Phenotyping)
Figures

Graphical abstract

Open AccessArticle
UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence
Remote Sens. 2019, 11(4), 410; https://doi.org/10.3390/rs11040410
Received: 18 January 2019 / Revised: 12 February 2019 / Accepted: 15 February 2019 / Published: 17 February 2019
Cited by 7 | PDF Full-text (9879 KB) | HTML Full-text | XML Full-text
Abstract
Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned [...] Read more.
Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks. Full article
(This article belongs to the Special Issue Advanced Imaging for Plant Phenotyping)
Figures

Graphical abstract

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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