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Classification of tobacco leaf diseases based on multi-source remote sensing data.

Ke Chen¹, Jian Guo², Linyi Liu¹

¹State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.

Frontiers in Plant Science

|February 20, 2026

Summary

This study introduces a robust tobacco leaf disease classification method using multi-source data, achieving 88.7% accuracy. The approach combines hyperspectral data, leaf area index, and chlorophyll content for improved disease assessment.

Keywords:

continuous wavelet transform hyperspectral reflectance data random forest remote sensing tobacco leaf

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Area of Science:

Agricultural remote sensing
Plant pathology
Data science in agriculture

Background:

Manual tobacco disease assessment is subjective and lacks robustness.
Single-feature extraction methods limit the accuracy of traditional classification approaches.
Objective and reliable disease classification is crucial for effective crop management.

Purpose of the Study:

To develop an accurate and robust tobacco leaf disease classification method.
To leverage multi-source data for improved disease identification.
To overcome the limitations of subjective manual observation and single-feature methods.

Main Methods:

Utilized hyperspectral reflectance data, leaf area index (LAI), and chlorophyll content as data sources.
Applied continuous wavelet transform for hyperspectral feature extraction.
Normalized LAI and chlorophyll content using Z-score method and employed a random forest algorithm for classification.

Main Results:

Achieved an overall classification accuracy of 88.7%.
Obtained a Kappa coefficient of 0.83, indicating strong classification performance.
Demonstrated the robustness of the multi-source data approach.

Conclusions:

The proposed multi-source data model offers a reliable and effective solution for tobacco leaf disease classification.
This method provides valuable insights for future research utilizing multi-source remote sensing data.
The integration of diverse data sources enhances classification accuracy and robustness in plant disease detection.