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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Cross-Crop Transferability of Machine Learning Models for Early Stem Rust Detection in Wheat and Barley Using

Anton Terentev¹, Daria Kuznetsova², Alexander Fedotov^1,2

¹All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia.

Plants (Basel, Switzerland)

|November 13, 2025

Summary

Hyperspectral imaging and machine learning enable early detection of stem rust in wheat and barley before symptoms appear. Models show high accuracy and cross-crop transferability, crucial for sustainable agriculture.

Keywords:

barley (Hordeum vulgare L.)cross-crop transferability early plant disease detection hyperspectral data processing hyperspectral imaging machine learning remote sensing stem rust (Puccinia graminis f. sp. tritici)wheat (Triticum aestivum L.)zero-shot cross-domain validation

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Area of Science:

Agricultural Science
Plant Pathology
Remote Sensing

Background:

Stem rust, caused by *Puccinia graminis* f. sp. *tritici*, significantly threatens wheat and barley production.
Early detection is vital for effective disease management and ensuring food security.
Current detection methods often rely on visible symptoms, limiting early intervention.

Purpose of the Study:

To assess hyperspectral imaging and machine learning for early stem rust detection in cereals.
To evaluate the cross-crop transferability of diagnostic models between wheat and barley.
To identify robust spectral features for rust disease identification.

Main Methods:

Collected hyperspectral data from wheat and barley before visible disease symptoms.
Applied multi-stage preprocessing including spectral normalization and standardization.
Engineered features based on spectral curve morphology and employed Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine models.

Main Results:

Achieved high F1-scores (up to 0.962 for wheat, 0.94 for barley) for stem rust detection.
Demonstrated high cross-crop model transferability (F1 > 0.94) with minimal false negatives (<2%).
Confirmed the universality of spectral signatures for stem rust across different cereal crops.

Conclusions:

Hyperspectral imaging combined with advanced machine learning offers a viable solution for early stem rust diagnostics.
The developed models show potential for broad application across cereal crops, aiding sustainable agriculture.
Further research is needed to validate field transferability from controlled laboratory conditions.