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Plants
Special Issues
From Phenotyping to Phenomics—Techniques for Exploring Plant Traits and Diversity
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From Phenotyping to Phenomics—Techniques for Exploring Plant Traits and Diversity

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 15001

Special Issue Editor

Dr. Kioumars Ghamkhar

E-Mail Website
Guest Editor
Plant Functional Biology, AgResearch, Mosgiel, New Zealand
Interests: plant phenotypic diversity; crops and forages; genetic resources; plant phenomics; plant phenotypic evolution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the recent surge in food consumption and the need for global nourishment of the fast-growing world population, breeding better adapted crops for the future is imperative. The complex interactions of plants with several factors—including their genes as well as soil, climate, pests, and humans—determines their final overall performance. In the last decade, a revolution in phenotyping approaches as well as digital technologies have enabled researchers to start collecting high-quality and repeatable phenotypic data to almost the same extent as genomic data. Employing machine vision for discriminating useful data from sensors and cameras is now a norm for monitoring plant phenotype over time. These techniques also enable researchers to integrate plant analyses into a monitoring system which also includes soil and environment data.

This Special Issue aims to bring together a range of research papers that cover plant phenotyping at large scale, from hundreds of genotypes using simple and repeatable technology to small scale using high tech and accurate methods and anything in between.

Dr. Kioumars Ghamkhar
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 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. Plants 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 2700 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

  • digital technologies
  • plant phenotyping
  • plant phenomics
  • machine vision
  • computer vision
  • plant visualization

Published Papers (4 papers)

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Research

11 pages, 3163 KiB  
Article
“Live-Autoradiography” Technique Reveals Genetic Variation in the Rate of Fe Uptake by Barley Cultivars
by Kyoko Higuchi, Keisuke Kurita, Takuro Sakai, Nobuo Suzui, Minori Sasaki, Maya Katori, Yuna Wakabayashi, Yuta Majima, Akihiro Saito, Takuji Ohyama and Naoki Kawachi
Plants 2022, 11(6), 817; https://doi.org/10.3390/plants11060817 - 18 Mar 2022
Cited by 2 | Viewed by 2995
Abstract
Iron (Fe) is an essential trace element in plants; however, the available Fe in soil solution does not always satisfy the demand of plants. Genetic diversity in the rate of Fe uptake by plants has not been broadly surveyed among plant species or [...] Read more.
Iron (Fe) is an essential trace element in plants; however, the available Fe in soil solution does not always satisfy the demand of plants. Genetic diversity in the rate of Fe uptake by plants has not been broadly surveyed among plant species or genotypes, although plants have developed various Fe acquisition mechanisms. The “live-autoradiography” technique with radioactive 59Fe was adopted to directly evaluate the uptake rate of Fe by barley cultivars from a nutrient solution containing a very low concentration of Fe. The uptake rate of Fe measured by live autoradiography was consistent with the accumulation of Fe-containing proteins on the thylakoid membrane. The results revealed that the ability to acquire Fe from the low-Fe solution was not always the sole determinant of tolerance to Fe deficiency among barley genotypes. The live-autoradiography system visualizes the distribution of β-ray-emitting nuclides and has flexibility in the shape of the field of view. This technique will strongly support phenotyping with regard to the long-distance transport of nutrient elements in the plant body. Full article
(This article belongs to the Special Issue From Phenotyping to Phenomics—Techniques for Exploring Plant Traits and Diversity)
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15 pages, 3102 KiB  
Article
Multi-Species Prediction of Physiological Traits with Hyperspectral Modeling
by Meng-Yang Lin, Valerie Lynch, Dongdong Ma, Hideki Maki, Jian Jin and Mitchell Tuinstra
Plants 2022, 11(5), 676; https://doi.org/10.3390/plants11050676 - 01 Mar 2022
Cited by 4 | Viewed by 2649
Abstract
Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the [...] Read more.
Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (Sorghum bicolor (L.) Moench) and six genotypes of corn (Zea mays L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R2 = 0.809), as well as for the nitrogen content of sorghum and corn (R2 = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722, and 964 nm were responsive to changes in the relative water content, while the reflectances at 486, 521, 625, 680, 699, and 754 nm were responsive to changes in the nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy. Full article
(This article belongs to the Special Issue From Phenotyping to Phenomics—Techniques for Exploring Plant Traits and Diversity)
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24 pages, 11164 KiB  
Article
Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR
by Harold F. Murcia, Sebastian Tilaguy and Sofiane Ouazaa
Plants 2021, 10(12), 2804; https://doi.org/10.3390/plants10122804 - 17 Dec 2021
Cited by 6 | Viewed by 3307
Abstract
Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. [...] Read more.
Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values. Full article
(This article belongs to the Special Issue From Phenotyping to Phenomics—Techniques for Exploring Plant Traits and Diversity)
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19 pages, 4102 KiB  
Article
Phenocave: An Automated, Standalone, and Affordable Phenotyping System for Controlled Growth Conditions
by Fernanda Leiva, Pernilla Vallenback, Tobias Ekblad, Eva Johansson and Aakash Chawade
Plants 2021, 10(9), 1817; https://doi.org/10.3390/plants10091817 - 31 Aug 2021
Cited by 8 | Viewed by 3965
Abstract
Controlled plant growth facilities provide the possibility to alter climate conditions affecting plant growth, such as humidity, temperature, and light, allowing a better understanding of plant responses to abiotic and biotic stresses. A bottleneck, however, is measuring various aspects of plant growth regularly [...] Read more.
Controlled plant growth facilities provide the possibility to alter climate conditions affecting plant growth, such as humidity, temperature, and light, allowing a better understanding of plant responses to abiotic and biotic stresses. A bottleneck, however, is measuring various aspects of plant growth regularly and non-destructively. Although several high-throughput phenotyping facilities have been built worldwide, further development is required for smaller custom-made affordable systems for specific needs. Hence, the main objective of this study was to develop an affordable, standalone and automated phenotyping system called “Phenocave” for controlled growth facilities. The system can be equipped with consumer-grade digital cameras and multispectral cameras for imaging from the top view. The cameras are mounted on a gantry with two linear actuators enabling XY motion, thereby enabling imaging of the entire area of Phenocave. A blueprint for constructing such a system is presented and is evaluated with two case studies using wheat and sugar beet as model plants. The wheat plants were treated with different irrigation regimes or high nitrogen application at different developmental stages affecting their biomass accumulation and growth rate. A significant correlation was observed between conventional measurements and digital biomass at different time points. Post-harvest analysis of grain protein content and composition corresponded well with those of previous studies. The results from the sugar beet study revealed that seed treatment(s) before germination influences germination rates. Phenocave enables automated phenotyping of plants under controlled conditions, and the protocols and results from this study will allow others to build similar systems with dimensions suitable for their custom needs. Full article
(This article belongs to the Special Issue From Phenotyping to Phenomics—Techniques for Exploring Plant Traits and Diversity)
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