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METHODS article

Front. Plant Sci.
Sec. Functional and Applied Plant Genomics
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1462694
This article is part of the Research Topic Utilizing Machine Learning with Phenotypic and Genotypic Data to enhance Effective Breeding in Agricultural and Horticultural Crops View all 12 articles

Deep phenotyping platform for microscopic plant-pathogen interactions

Provisionally accepted
Stefanie Lück Stefanie Lück 1Salim Bourras Salim Bourras 2Dimitar K Douchkov Dimitar K Douchkov 1*
  • 1 Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
  • 2 Swedish University of Agricultural Sciences, Uppsala, Uppsala, Sweden

The final, formatted version of the article will be published soon.

    The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro, a modular, machine learning-aided system designed for highthroughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens.BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision's open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes.

    Keywords: BluVision, Automated microscopy, barley, deep learning, Microphenomics, neuronal networks, pathogens, powdery mildew

    Received: 10 Jul 2024; Accepted: 14 Jan 2025.

    Copyright: © 2025 Lück, Bourras and Douchkov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dimitar K Douchkov, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.