Special Issue "Precision Phenotyping in Plant Breeding"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Crop Breeding and Genetics".

Deadline for manuscript submissions: closed (20 September 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Fiona L. Goggin

Department of Entomology, University of Arkansas, Fayetteville, AR 72701, USA
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Special Issue Information

Dear Colleagues,

As genome sequencing and molecular breeding techniques have dramatically increased the speed at which large populations can be genotyped, phenotyping has become the rate-limiting step in many crop improvement efforts. As a result, there is presently a major emphasis to develop better methods for rapid, high-throughput analyses of numerous plant traits, such as growth, morphology, stress tolerance, pest resistance, and biochemical profiles. Advances in the fields of high-throughput phenotyping, remote sensing, and computer vision are enabling mechanization of data collection, non-destructive measurement methods, and automation of data analysis. In addition, many approaches to high-throughput phenotyping are increasing the “dimensionality” of the data, or the number of different plant characteristics that can be measured at one time, and this increased dimensionality is enabling the emerging field of plant phenomics.

The objective of this Special Issue is to provide a forum for new research and review articles on recent advances in plant phenotyping. The scope of this Special Issue will include field phenotyping as well as phenotyping systems based in controlled environments, and can also encompass challenges in data analytics associated with plant phenotyping data.

Prof. Dr. Fiona Goggin
Guest Editor

Manuscript Submission Information

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Keywords

  • High throughput plant phenotyping
  • Field phenotyping
  • Remote sensing
  • Plant image analysis
  • Plant phenomics

Published Papers (6 papers)

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Editorial

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Open AccessFeature PaperEditorial Transdisciplinary Graduate Training in Predictive Plant Phenomics
Received: 29 April 2018 / Revised: 29 April 2018 / Accepted: 4 May 2018 / Published: 16 May 2018
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Abstract
Novel methods to increase crop productivity are required to meet anticipated demands for food, feed, fiber, and fuel. It is becoming feasible to use modern sensors and data analysis techniques for predicting plant growth and productivity based on genomic, phenotypic, and environmental data.
[...] Read more.
Novel methods to increase crop productivity are required to meet anticipated demands for food, feed, fiber, and fuel. It is becoming feasible to use modern sensors and data analysis techniques for predicting plant growth and productivity based on genomic, phenotypic, and environmental data. To design and construct crops that deliver desired traits requires trained personnel with scientific and engineering expertise as well as a variety of “soft” skills. To address these needs at Iowa State University, we developed a graduate specialization called “Predictive Plant Phenomics” (P3). Although some of our experiences may be unique, many of the specialization’s principles are likely to be broadly applicable to others interested in developing graduate training programs in plant phenomics. P3 involves transdisciplinary training and activities designed to develop communication, teambuilding, and management skills. To support students in this demanding and unique intellectual environment, we established a two-week boot camp before their first semester and founded a community of practice to support students throughout their graduate careers. Assessments show that P3 students understand the transdisciplinary training concepts, have formed a beneficial and supportive community, and interact with diverse faculty outside of their home departments. To learn more about the P3 program, visit www.predictivephenomicsinplants.iastate.edu. Full article
(This article belongs to the Special Issue Precision Phenotyping in Plant Breeding)
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Research

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Open AccessArticle GainTKW: A Measurement System of Thousand Kernel Weight Based on the Android Platform
Agronomy 2018, 8(9), 178; https://doi.org/10.3390/agronomy8090178
Received: 11 July 2018 / Revised: 17 August 2018 / Accepted: 3 September 2018 / Published: 10 September 2018
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Abstract
Thousand kernel weight (TKW) is an important parameter for the evaluation of grain yield. The traditional measurement method relies on manual steps: weighing and counting. In this paper, we developed a system for the automated evaluation of thousand kernel weight that combines a
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Thousand kernel weight (TKW) is an important parameter for the evaluation of grain yield. The traditional measurement method relies on manual steps: weighing and counting. In this paper, we developed a system for the automated evaluation of thousand kernel weight that combines a weighing module and Android devices, called “gainTKW”. The system is able to collect the weight information from the weighing module through a serial port using the RS232-micro USB cable. In the imaging process, we adopt a k-means clustering segmentation algorithm to solve the problem of uneven lighting. We used the marker-controlled watershed algorithm and area threshold method to count the number of kernels that are touching one another. These algorithms were implemented based on the OpenCV (Open Source Computer Vision) libraries. The system tested kernel images of six species taken with the Android device under different lighting conditions. The algorithms in this study can solve the segmentation problems caused by shadows, as well. The appropriate numbers of kernels, of different species, are counted with an error ratio upper limit of 3%. The application is convenient and easy to operate. For the experiments, we can prove the efficiency and accuracy of the developed system by comparing the results between the manual method and the proposed application. Full article
(This article belongs to the Special Issue Precision Phenotyping in Plant Breeding)
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Open AccessArticle Image-Based On-Panicle Rice [Oryza sativa L.] Grain Counting with a Prior Edge Wavelet Correction Model
Received: 28 February 2018 / Revised: 22 April 2018 / Accepted: 16 May 2018 / Published: 7 June 2018
Cited by 1 | PDF Full-text (3019 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The number of rice grains on a panicle is an important index for variety screening during high-quality rice [Oryza Sativa L.] breeding. For an in-vivo image-based measurement, the occlusion and overlapping among grains are the major challenges in non-destructive precise phenotyping of
[...] Read more.
The number of rice grains on a panicle is an important index for variety screening during high-quality rice [Oryza Sativa L.] breeding. For an in-vivo image-based measurement, the occlusion and overlapping among grains are the major challenges in non-destructive precise phenotyping of the on-panicle grains. In order to tackle these challenges, this paper describes a correction-model-referred on-panicle grain counting method based on the area of the rice panicle and its edge contour wavelet analysis. First, we assume that a deterministic correlation exists between the number of grains of the panicle and the traits of its edge contour morphology, which reflects the extent to which the grains are occluded. Second, a method for coarsely estimating grain number per panicle is proposed based on the projective area of the panicle in the image and the average area of a rice grain. Finally, a correction model which is built with the average wavelet frequency of the edge contour of the panicle is employed to correct the estimated value of the grain number. Two randomly selected cases are investigated in detail, showing that computation accuracy with a correction model is increased by 26% and 23% respectively when compared to that of the naive area-based computation. In conclusion, we reveal and validate the relationship between the number of grains of the panicle and the fluctuation frequency of its edge contours. Further, experiments show that errors caused by overlapping and occlusion scenarios can be alleviated with the estimation and correction hybrid models, achieving an average accuracy of 94% compared to the results of manual counting. Full article
(This article belongs to the Special Issue Precision Phenotyping in Plant Breeding)
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Open AccessArticle Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans
Received: 26 February 2018 / Revised: 3 April 2018 / Accepted: 16 April 2018 / Published: 17 April 2018
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Abstract
Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that
[...] Read more.
Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that can be capitalized by improving the predictive ability of the common prediction models used for genomic prediction. Imagery (canopy coverage) data recorded between days 14–71 using two collection methods (ground information in 2013 and 2014; aerial information in 2014 and 2015) on a soybean nested association mapping population (SoyNAM) was used to calibrate the prediction models together with the inclusion of several types of interactions between canopy coverage data, environments, and genomic data. Three different scenarios were considered that breeders might face testing lines in fields: (i) incomplete field trials (CV2); (ii) newly developed lines (CV1); and (iii) predicting lines in unobserved environments (CV0). Two different traits were evaluated in this study: yield and days to maturity (DTM). Results showed improvements in the predictive ability for yield with respect to those models that solely included genomic data. These relative improvements ranged 27–123%, 27–148%, and 65–165% for CV2, CV1, and CV0, respectively. No major changes were observed for DTM. Similar improvements were observed for both traits when the reduced canopy information for days 14–33 was used to build the training-testing relationships, showing a clear advantage of using phenomics in very early stages of the growing season. Full article
(This article belongs to the Special Issue Precision Phenotyping in Plant Breeding)
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Review

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Open AccessReview Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots
Received: 11 April 2018 / Revised: 24 April 2018 / Accepted: 9 May 2018 / Published: 14 May 2018
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Abstract
Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT
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Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been or may be effective for the analysis of 3D CT images of plant roots. We describe and comment on different approaches to aspects of the analysis of plant roots based on images, namely, (1) root segmentation, i.e., the isolation of root from non-root matter; (2) root-system reconstruction; and (3) extraction of higher-level phenotypes. As many of these approaches are novel and have yet to be applied to this context, we limit ourselves to brief descriptions of the methodologies. With the rapid development and growing use of X-ray CT scanning technologies to generate large volumes of data relevant to root structure, it is timely to review existing and potential quantitative and computational approaches to the analysis of such data. Summaries of several computational tools are included in the Appendix. Full article
(This article belongs to the Special Issue Precision Phenotyping in Plant Breeding)
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Open AccessReview Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges
Received: 23 February 2018 / Revised: 16 April 2018 / Accepted: 19 April 2018 / Published: 22 April 2018
Cited by 2 | PDF Full-text (754 KB) | HTML Full-text | XML Full-text
Abstract
Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production,
[...] Read more.
Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production, quality, and resistance to various stresses. Beyond fully automatic phenotyping systems, several promising technologies can help accurately characterize a wide range of plant traits at affordable costs and with high-throughput. In this review, we revisit the principles of proximal and remote sensing, describing the application of non-invasive devices for precision phenotyping applied to the protected horticulture. Potentiality and constraints of big data management and integration with “omics” disciplines will also be discussed. Full article
(This article belongs to the Special Issue Precision Phenotyping in Plant Breeding)
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