Effect of mesoscale phytobiome communication on plant electrophysiological signals

Determine how inter-kingdom (mesoscale) phytobiome communication alters stress-induced plant electrophysiological signals recorded for diagnostic purposes, and characterize these alterations to inform signal processing and machine learning methods that improve stress classification accuracy.

Background

The paper proposes monitoring plant stress via electrophysiological signals that originate from molecular communication within the plant. These signals can be classified by machine learning to diagnose stress types (e.g., drought, pest infestation, nutrient deficiency).

The authors note that inter-kingdom interactions in the phytobiome (mesoscale communication) may influence these electrophysiological signals, but the manner and extent of this influence are currently unclear, posing a barrier to improving ML-based classification accuracy.

References

However, how mesoscale phytobiome communication alters these signals is still unclear and requires further research to improve classification accuracy using signal processing and ML methods.

— Decoding and Engineering the Phytobiome Communication for Smart Agriculture  (2508.03584 - Gulec et al., 5 Aug 2025) in Section 3.1 (Phytobiome Monitoring)