Your new experience awaits. Try the new design now and help us make it even better

REVIEW article

Front. Plant Sci.

Sec. Plant Abiotic Stress

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1585413

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

Provisionally accepted
  • University of Minnesota Twin Cities, St. Paul, United States

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

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.

Keywords: Stress detection, multi-omics, remote sensing, machine learning, phenotyping

Received: 28 Feb 2025; Accepted: 11 Sep 2025.

Copyright: © 2025 Cooper, Propst and Hirsch. 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: Cory D Hirsch, cdhirsch@umn.edu

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.