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Current methods and future needs for visible and non-visible detection of plant stress responses

Research output: Contribution to journalReview articlepeer-review

Abstract

As climate change alters the frequency, intensity, and co-occurrences of abiotic and biotic stresses, the effective and efficient detection of plant stress responses and resistance mechanisms is critical for safeguarding global food security. Stressful environments elicit both visible and non-visible changes in plants. Cellular and subcellular changes, often invisible to the naked eye, can serve as indicators of stress and can be quantified using molecular, ionomic, metabolomic, genomic, and transcriptomic methods. In contrast, visible responses such as discoloration, morphological changes, and disease symptoms can be monitored efficiently through atmospheric, aerial, and terrestrial remote sensing platforms. Phenotyping at the whole-plant and organ levels offers valuable insights for diagnosing stress in situ, providing opportunities to study plant resistance and acclimation strategies under realistic conditions. However, the complexity of plant stress responses, spanning microscopic to macroscopic scales and diverse biological processes, make it challenging for any single technology to comprehensively capture the full spectrum of reactions. Furthermore, the rising prevalence of multifactorial stress conditions highlights the need for research on synergistic and antagonistic interactions between stress factors. To effectively mitigate the impacts of stress on agriculture, future research must prioritize integrative multi-omic approaches that connect cellular and subcellular processes with morphological and phenological stress responses.

Original languageEnglish (US)
Article number1585413
JournalFrontiers in Plant Science
Volume16
DOIs
StatePublished - 2025

Bibliographical note

Publisher Copyright:
Copyright © 2025 Cooper, Propst and Hirsch.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • machine learning
  • multi-omics
  • phenotyping
  • remote sensing
  • stress detection

PubMed: MeSH publication types

  • Journal Article
  • Review

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