Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
4.5
Journals
Plants
Special Issues
Research Trends in Plant Phenotyping
share announcement

Research Trends in Plant Phenotyping

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2771

Special Issue Editors

Dr. Boris Lazarević

E-Mail Website
Guest Editor
Faculty of Agriculture, University of Zagreb, 10000 Zagreb, Croatia
Interests: crop nutrition; crop ecophysiology; using plant phenotyping techniques in the quantification of plant abiotic and biotic stresses
Special Issues, Collections and Topics in MDPI journals
Dr. Ankica Đ. Kondić-Špika

E-Mail Website
Guest Editor
Institute of Field and Vegetable Crops, Novi Sad, Serbia
Interests: plant phenotyping; field phenotyping; crops; vegetable crops; field of application of different biotechnological methods in the breeding of small grains

Special Issue Information

Dear Colleagues,

The history of plant phenotyping can be traced back to the early days of agriculture when farmers began to observe and select plants with desirable traits for cultivation. With the development of modern plant breeding and genetics in the 20th century, limitations in phenotyping accuracy, precision, and throughput limited the power of genetic analysis. At the beginning of the 21st century, advancements in automatization, sensor technology, computer storage capacity, etc., enabled the development of high-throughput phenotyping (HTP) and shifted the phenotyping bottleneck from data acquisition to data analysis. Today we are faced with even faster development in sensor technology, machine vision, automation technology, and cloud-based technologies, combined with machine learning techniques and artificial intelligence, increasing the power of plant phenotyping. This has enabled the separation of meaningful data from environmental and experimental noise and the integration of HTP techniques in ecophysiology research, crop breeding and precision agriculture research, opening new avenues for the improvement of crop productivity and crop production sustainability.

This Special Issue aims to attract all kinds of crop phenotyping research, from phenotypic data collection to the development of various sensors for plant phenotyping to the application of phenotyping techniques in plant ecophysiology, plant breeding, precision agriculture and advancements in phenomics analysis.

Dr. Boris Lazarević
Dr. Ankica Đ. Kondić-Špika
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 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

  • high-throughput phenotyping
  • proximal sensing
  • remote sensing
  • phenotypic data analysis
  • marker-assisted breeding
  • precision agriculture

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 54841 KiB  
Article
High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images
by Mohamed Farag Taha, Hanping Mao, Yafei Wang, Ahmed Islam ElManawy, Gamal Elmasry, Letian Wu, Muhammad Sohail Memon, Ziang Niu, Ting Huang and Zhengjun Qiu
Plants 2024, 13(3), 392; https://doi.org/10.3390/plants13030392 - 29 Jan 2024
Viewed by 1254
Abstract
Chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely [...] Read more.
Chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems. Full article
(This article belongs to the Special Issue Research Trends in Plant Phenotyping)
►▼ Show Figures

Figure 1

16 pages, 3609 KiB  
Article
Phenotypic and Genotypic Variation of Cultivated Panax quinquefolius
by Abdurraouf Abaya, Geovanna Cristina Zaro, Alvaro De la Mora Pena, Tom Hsiang and Paul H. Goodwin
Plants 2024, 13(2), 300; https://doi.org/10.3390/plants13020300 - 19 Jan 2024
Viewed by 1213
Abstract
American ginseng (Panax quinquefolius) is widely used due to its medicinal properties. Ontario is a major producer of cultivated American ginseng, where seeds were originally collected from the wild without any subsequent scientific selection, and thus the crop is potentially very diverse. [...] Read more.
American ginseng (Panax quinquefolius) is widely used due to its medicinal properties. Ontario is a major producer of cultivated American ginseng, where seeds were originally collected from the wild without any subsequent scientific selection, and thus the crop is potentially very diverse. A collection of 162 American ginseng plants was harvested from a small area in a commercial garden and phenotyped for morphological traits, such as root grade, stem length, and fresh and dry weights of roots, leaves, stems, and seeds. All of the traits showed a range of values, and correlations were observed between root and stem weights, root dry weight and leaf dry weight, as well as root and leaf fresh weights. The plants were also genotyped using single nucleotide polymorphisms (SNPs) at the PW16 locus. SNP analysis revealed 22 groups based on sequence relatedness with some groups showing no SNPs and others being more diverse. The SNP groups correlated with significant differences in some traits, such as stem length and leaf weight. This study provides insights into the genetic and phenotypic diversity of cultivated American ginseng grown under similar environmental conditions, and the relationship between different phenotypes, as well as genotype and phenotype, will aid in future selection programs to develop American ginseng cultivars with desirable agronomic traits. Full article
(This article belongs to the Special Issue Research Trends in Plant Phenotyping)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop