Electrome alterations in a plant-pathogen system: Toward early diagnosis
F Z Simmi1, L J Dallagnol2, A S Ferreira3
1Federal University of Pelotas, Post-Graduation Programme on Plant Physiology, Institute of Biology, Department of Botany, Pelotas, RS, Brazil.
Bioelectrochemistry (Amsterdam, Netherlands)
|March 8, 2020
Summary
Detecting plant infections early is possible by analyzing electrical signals. This study shows distinct electrophysiological patterns emerge in tomato plants after fungal inoculation, enabling accurate early detection.
Area of Science:
- Plant electrophysiology
- Plant pathology
- Bioinformatics
Background:
- Plant-pathogen interactions involve complex signaling pathways.
- Early detection of plant diseases is crucial for agricultural productivity.
Purpose of the Study:
- To investigate electrophysiological systemic responses in tomato plants following pathogenic fungal inoculation.
- To identify distinct electrical signaling patterns indicative of infection.
Main Methods:
- Measuring electrical signals in tomato plants before and after inoculation under controlled conditions.
- Analyzing time series data using approximate multi-scale entropy (ApEn).
- Employing machine learning algorithms for data classification and infection detection.
Main Results:
- Distinguishable changes in plant electrome activity were observed post-inoculation.
- Scale invariance and increased internal organization (ApEn) were detected.
- Machine learning achieved high accuracy in early-stage infection detection.
Conclusions:
- Electrophysiological monitoring can differentiate between healthy and infected plants.
- Long-distance electrical signaling plays a role in plant-pathogen interactions.
- This approach offers potential for early detection of plant infections.
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