Spatiotemporal analysis of herbicide-induced stress in chicory leaves using chlorophyll-a fluorescence imaging.

Authors

Ivana López-Valiño, Gabriela Beatriz Cordon, María Gabriela Lagorio

Published in

Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology. Oct 09, 2025. Epub Oct 09, 2025.

Abstract

The widespread use of herbicides such as atrazine and paraquat, although essential for weed control, can unintentionally impact non-target plant species, compromising agricultural productivity and environmental health. Understanding the physiological responses induced by these compounds is critical for developing more sustainable agricultural practices. Despite the extensive use of chlorophyll fluorescence techniques, most studies typically average signals across tissues, overlooking localized and heterogeneous stress patterns. To address this gap, the present study evaluated the use of time-resolved chlorophyll-a fluorescence imaging as a non-invasive, spatially resolved technique for the assessment of herbicide-induced stress in chicory (Cichorium intybus) leaves. The results revealed that chlorophyll fluorescence parameters, particularly F_v/F_m, F₀, Fₘ, and ϕNPQ, detected heterogeneous stress patterns depending on the herbicide across the leaf. Atrazine exposure increased F₀, indicating a blockage in the electron transport chain, while paraquat decreased F₀, suggesting chlorophyll degradation. Both herbicides reduced F_m and altered ETR, yet through distinct physiological mechanisms. These findings demonstrate that time-resolved fluorescence imaging can distinguish specific modes of herbicide action with high sensitivity. Notably, the technique enabled detection of physiological alterations before the appearance of visible symptoms, highlighting its potential for early stress diagnosis. This work establishes time-resolved fluorescence imaging as a powerful diagnostic tool, capable of discriminating specific herbicide actions and for monitoring plant stress at a fine spatial scale. Furthermore, its compatibility with automated and AI-based analysis platforms highlights its potential for advancing precision agriculture, optimizing herbicide application, and promoting sustainable farming practices.

PMID:
41065984
Bibliographic data and abstract were imported from PubMed on 09 Oct 2025.

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