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SYSTEMATIC REVIEW article
Front. Plant Sci.
Sec. Plant Abiotic Stress
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1545025
A SYSTEMATIC REVIEW OF MULTI-MODE ANALYTICS FOR ENHANCED PLANT STRESS EVALUATION
Provisionally accepted
Abdolrahim Zandi 1
Seyedali Hosseinirad 2
Hossein Kashani Zadeh 1 Kouhyar Tavakolian 1
Byoung-Kwan Cho 3 Fartash Vasefi 2 Moon S Kim 3 Pantea Tavakolian 1*- 1 University of North Dakota, Grand Forks, United States
- 2 SafetySpect Inc., Grand Forks, North Dakota, United States
- 3 Agricultural Research Service, United States Department of Agriculture, Washington D.C., District of Columbia, United States
The main objective of this review is to investigate the evolving landscape of analytical methods for early recognition of plant stress-induced symptoms or patterns using multiple optical detection modes across various spectral bands. Detecting plant stress remains a critical challenge in agriculture, where early intervention is vital for improving crop resilience and maximizing yield. Traditional single-mode approaches fail to capture the intricate interplay of abiotic stressors. In contrast, Multi-Mode Analytics (MMA) integrates spectral imaging, imagebased phenotyping, and adaptive computational techniques to enhance accuracy, efficiency, and predictive capability. With the rapid advancement of machine learning, data fusion, and hyperspectral technologies, a comprehensive review of MMA methods is essential for driving precision agriculture, optimizing resource allocation, and safeguarding food security in terrestrial and space environments . . The review addresses several critical questions: What are the diverse abiotic stressors affecting plant health, and how do conventional methods fall short in capturing their complex interactive relationship? What advantages doesdoes multi-mode analyticsMMA, including hyperspectroscopy, offers over conventional single-mode techniques regarding accuracy and efficiency in detecting stressful events? How can advanced analytical methods, including machine learning and data fusion, enhance timely intervention strategies to improve agricultural productivity? The sophisticated multi-mode analyticsMMA improves reliability and accuracy of and enable prompt early interventions in challenging environments, such as food safety, and high-yield and terrestrial and space agriculture. The findings reveal that multi-mode analytical methods, including spectral, time, frequency analysis, and multi-dimensional data reduction methods, enable earlier and more precise mapping of plant stress indicators. -
Keywords: Plant stressor, hyperspectral, -Mapping indices, Stress pattern, Multi-modespectroscopy, -Stress pathways, -Hyperspectral Fluorescence imaging (HFI), Hyperspectral Reflectance Imaging (HRI)
Received: 18 Dec 2024; Accepted: 27 Feb 2025.
Copyright: © 2025 Zandi, Hosseinirad, Kashani Zadeh, Tavakolian, Cho, Vasefi, Kim and Tavakolian. 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:
Pantea Tavakolian, University of North Dakota, Grand Forks, United States
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.