Next-Generation Plant Phenomics: Bridging Genomics, AI, and Genetic Resources

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 139

Special Issue Editors


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Guest Editor
Plant Genetics, AgResearch, Grasslands, Palmerston North, New Zealand
Interests: plant phenotypic diversity; crops and forages; genetic resources; plant phenomics; plant phenotypic evolution
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Res Ctr Fodder Crops & Dairy Prod, Council Agr Res & Econ CREA, Lodi, Italy.
Interests: white lupin; genetic variation; trait genetic architecture; genome-enabled prediction

Special Issue Information

Dear Colleagues,

Plant phenotyping continues to be a cornerstone of modern plant science, offering essential insights into how plant traits are shaped by genetics and environmental interactions. With recent breakthroughs in phenomic technologies—from hyperspectral imaging and X-ray analysis to machine learning and automated trait extraction—we are entering a new era where complex traits in crops, forages, and their wild relatives can be measured with unprecedented speed and precision. These advances offer transformative potential for plant breeding, conservation, and the strategic management of GenBank collections.

This Special Issue, “Next-Generation Plant Phenomics: Bridging Genomics, AI, and Genetic Resources”, aims to explore emerging trends and future directions in plant phenotyping. We particularly welcome submissions that address the integration of phenotypic and genomic data, the development of scalable and adaptable phenotyping tools, and innovative approaches to characterising plant diversity under real-world and controlled conditions. Special attention will be given to technologies that enhance our ability to assess large germplasm collections efficiently, facilitate climate-resilient breeding, and improve our understanding of plant adaptation and evolution.

We invite researchers, breeders, and technologists to contribute original research articles, reviews, or perspective pieces that reflect on the technological, methodological, and conceptual advances shaping the future of plant phenomics.

Dr. Kioumars Ghamkhar
Dr. Nelson Nazzicari
Guest Editors

Manuscript Submission Information

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Keywords

  • plant phenotyping
  • genomic integration
  • high-throughput phenotyping
  • machine learning
  • plant genetic resources

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Published Papers (1 paper)

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Research

25 pages, 1407 KB  
Article
Analysis of Phenotypic Diversity and Comprehensive Evaluation of 51 Helleborus L. Hybrid Individuals
by Liuqing Qu, Bingyu Yuan, Xiaohui Wen, Jia Guo, Jianrang Luo and Xiaohua Shi
Plants 2025, 14(20), 3226; https://doi.org/10.3390/plants14203226 - 20 Oct 2025
Abstract
Helleborus orientalis L. is a valuable winter-flowering and understory landscape plant, but its application and breeding are hindered by poor heat tolerance and the lack of a robust germplasm evaluation system. In this study, 51 Helleborus L. hybrid individuals obtained through manual open [...] Read more.
Helleborus orientalis L. is a valuable winter-flowering and understory landscape plant, but its application and breeding are hindered by poor heat tolerance and the lack of a robust germplasm evaluation system. In this study, 51 Helleborus L. hybrid individuals obtained through manual open pollination were evaluated using coefficient of variation (CV), Shannon–Weaver diversity index (H′), correlation analysis, principal component analysis (PCA), and cluster analysis to assess genetic diversity and ornamental value based on 17 phenotypic traits. The results showed rich phenotypic diversity among the hybrids. Quantitative traits showed CV ranging from 9.48% to 37.99% and H′ between 0.77 and 1.51, with flower count and leaf length being the most variable. Qualitative traits had H′ values from 0.52 to 1.55, with sepal color showing the highest diversity. Significant correlations were detected among heat tolerance, pest resistance, leaf and petiole length, as well as plant and flower form. PCA extracted six principal components accounting for 74.50% of cumulative variance. Cluster analysis classified the 51 germplasms into five groups. Using the AHP model, a comprehensive evaluation system was established, and 13 elite individuals were selected for variety rights application and characterization. This study provides a reference for establishing DUS test guidelines and advancing breeding and utilization of Helleborus L. Full article
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