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Plants
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Modeling of Plants Phenotyping and Biomass
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Modeling of Plants Phenotyping and Biomass

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

Deadline for manuscript submissions: 30 August 2025 | Viewed by 2049

Special Issue Editor

Dr. Yaping Xu

E-Mail Website
Guest Editor
1. Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA
2. Center for Agricultural Synthetic Biology, University of Tennessee, Knoxville, TN 37996, USA
3. Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
4. Department of Environmental and Geoscience, Sam Houston State University, Huntsville, TX 77340, USA
Interests: mapping; satellite image analysis; satellite image processing; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several perennial and annual crops have been considered as leading candidates for bioenergy production. The favorable traits, such as high biomass production, wide adaptation, and low agronomic input requirements, are highly associated with a desirable bioenergy feedstock. Increased productivity and sustainability of plant feedstocks in bioenergy crops are key factors for biofuel production. Factors affecting plant quality and performance can be broadly attributed to plant genetics and the growing environment. However, phenotyping resources have created a bottleneck in biofuel crop improvement and breeding. Advances in developing high-throughput phenotyping tools and techniques are essential for characterizing aboveground and belowground phenes to achieve sustainable growth of biofuel crops. Moreover, novel high-throughput approaches are needed to better understand the association between genotypes and phenotypes and to accelerate plant breeding. Research on this topic is important to fight against climate/ecosystem changes, leading to climate-smart or eco-efficient agriculture. 

We encourage original research, methods, and review articles to address the broad range of topics from a data-driven approach, including, but not limited to:

  • Perspectives of biofuel plant phenomics;
  • Big data challenges for genomics and phenotyping data;
  • High-throughput phenotyping: tools and techniques for assessment;
  • Genomic selection in biofuel crops: Benefits of high throughput phenotyping;
  • Precision agriculture association with high throughput biofuel plant phenotyping;
  • Biomass quantity/quality assessment;
  • Biotic/abiotic stress assessment;
  • Sustainability trait assessment.

Dr. Yaping Xu
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

  • biofuel
  • bioenergy
  • biomass
  • phenotyping
  • phenomics
  • AI and machine learning
  • image analysis

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Published Papers (2 papers)

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Research

13 pages, 1439 KiB  
Article
Digitally Quantifying Growth and Verdancy of Lolium Plants In Vitro
by Mara B. Depetris, Adam M. Dimech and Kathryn M. Guthridge
Plants 2025, 14(10), 1499; https://doi.org/10.3390/plants14101499 - 16 May 2025
Viewed by 232
Abstract
The image analysis of plants provides an opportunity to measure changes in growth and physiology quantitatively, and non-destructively, over time providing significant advantages over traditional methods of assessment which often rely on qualitative and subjective measures to distinguish between different treatments or genotypes [...] Read more.
The image analysis of plants provides an opportunity to measure changes in growth and physiology quantitatively, and non-destructively, over time providing significant advantages over traditional methods of assessment which often rely on qualitative and subjective measures to distinguish between different treatments or genotypes in an experiment. Image analysis techniques are commonly deployed for the analysis of plants in the field or glasshouse, but few studies have demonstrated the use of image analysis to phenotype plants grown under aseptic conditions in culture media. Lolium × hybridum Hausskn ‘Shogun’ plants were germinated in vitro and cultured on media containing combinations of thidiazuron [1-phenyl-3-(1,2,3-thiadiazol-5-yl) urea] (TDZ), N6-benzylaminopurine (BA) and gibberellic acid (GA3) or on phytohormone-free control media. RGB images were taken of the plants throughout the experiment and morphological image analysis techniques were used to quantify changes in plant development. A novel approach to quantitatively measure ’greenness‘ in plants using the CIELAB colour model (L*a*b) colour space from RGB images was developed. This methodology could be utilised to develop improved in vitro growth protocols for Lolium and grass species with similar morphology. Full article
(This article belongs to the Special Issue Modeling of Plants Phenotyping and Biomass)
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20 pages, 10592 KiB  
Article
Use of Uncrewed Aerial System (UAS)-Based Crop Features to Perform Growth Analysis of Energy Cane Genotypes
by Ittipon Khuimphukhieo, Lei Zhao, Benjamin Ghansah, Jose L. Landivar Scott, Oscar Fernandez-Montero, Jorge A. da Silva, Jamie L. Foster, Hua Li and Mahendra Bhandari
Plants 2025, 14(5), 654; https://doi.org/10.3390/plants14050654 - 21 Feb 2025
Cited by 1 | Viewed by 833
Abstract
Plant growth analysis provides insight regarding the variation behind yield differences in tested genotypes for plant breeders, but adopting this application solely for traditional plant phenotyping remains challenging. Here, we propose a procedure of using uncrewed aerial systems (UAS) to obtain successive phenotype [...] Read more.
Plant growth analysis provides insight regarding the variation behind yield differences in tested genotypes for plant breeders, but adopting this application solely for traditional plant phenotyping remains challenging. Here, we propose a procedure of using uncrewed aerial systems (UAS) to obtain successive phenotype data for growth analysis. The objectives of this study were to obtain high-temporal UAS-based phenotype data for growth analysis and investigate the correlation between the UAS-based phenotype and biomass yield. Seven different energy cane genotypes were grown in a random complete block design with four replications. Twenty-six UAS flight missions were flown throughout the growing season, and canopy cover (CC) and canopy height (CH) measurements were extracted. A five-parameter logistic (5PL) function was fitted through these temporal measurements of CC and CH. The first- and second-order derivatives of this function were calculated to obtain several growth parameters, which were then used to assess the growth of different genotypes with respect to weed competitiveness and biomass yield traits. The results show that CC and CH growth rates significantly differed among genotypes. TH16-16 was outstanding for its ground cover growth; therefore, it was identified as a weed-competitive genotype. Furthermore, TH16-22 had a higher CH maximum growth rate per day, yielding a higher biomass compared to other genotypes. The CH-based multi-temporal data as well as the growth parameters had a better relationship with biomass yield. This study highlights the application of UAS-based high-throughput phenotyping (HTP), along with growth analysis, for assisting plant breeders in decision-making. Full article
(This article belongs to the Special Issue Modeling of Plants Phenotyping and Biomass)
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