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RGB image-based drought stress classification of garden plants using SVM model
Seong-Ju Lee1, Seung-Won Han2, Tae-Wan Kim3,4
1Department of Plant Resources and Landscape Architecture, Hankyong National University, Anseong-si, 17579, Republic of Korea.
Scientific Reports
|May 7, 2026
Summary
This study developed a machine learning framework using RGB images to classify plant drought responses in urban gardens. The approach effectively groups plants by water needs, aiding irrigation management.
Area of Science:
- Horticultural Science
- Plant Physiology
- Remote Sensing
Background:
- Climate change-induced drought poses challenges for water management in urban gardens.
- Scalable, non-destructive methods are needed to assess plant water stress.
Purpose of the Study:
- To develop and validate an integrated framework for classifying plant drought response patterns.
- To assess the efficacy of RGB image indices and machine learning in identifying drought stress.
Main Methods:
- Combined chlorophyll fluorescence, RGB image indices, and machine learning (SVM, PLS-DA).
- Evaluated ten garden plant species under varying soil moisture.
- Utilized data augmentation (SMOTE) and hierarchical cluster analysis.
Main Results:
- Identified three distinct physiological drought response clusters using fluorescence and RGB indices.
- A Support Vector Machine model achieved 0.91 accuracy using GLI, NGRDI, BGI, and soil moisture.
- RGB indices with nonlinear models successfully reproduced drought response patterns.
Conclusions:
- The proposed framework shows potential for cost-effective classification of plant drought responses.
- This method can aid in grouping plants with similar water requirements for improved irrigation.
- Generalizability requires further validation in diverse field conditions.
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