RGB image-based drought stress classification of garden plants using SVM model

Abstract

Abstract Water management in urban gardens is increasingly complex due to diverse plant species and growing drought stress under climate change. This study proposes a non-destructive method to classify drought responses of mixed garden plant species using RGB image indices and a support vector machine (SVM) model. Chlorophyll fluorescence responses were used to evaluate photosynthetic stress, while image-derived indices—green leaf index (GLI), normalized green-red difference index (NGRDI), and blue-green pigment index (BGI)—were analyzed to assess drought responses. Hierarchical clustering grouped species into three response clusters based on fluorescence and image patterns. Principal component analysis (PCA) identified NGRDI, GLI, and BGI as key variables, with NGRDI and GLI showing strong correlations with soil moisture content and BGI distinguishing cluster-specific responses. An SVM model was constructed using RGB indices and soil moisture content as input features, achieving a classification accuracy of 88.3% and an F1 score of 0.85 through five-fold cross-validation. This approach supports efficient water management and plant selection, and can be extended to precision irrigation strategies using machine learning.

Plant phenotyping relevance

RGB画像指標から植物の乾燥ストレス応答を推定・分類する非破壊的手法を開発し、交差検証で性能評価しているため、表現型取得・推定法が中心である。

Code and data availability

The paper describes RGB image collection (6,000 images), vegetation index computation, chlorophyll fluorescence measurements, and an SVM model, but provides no public repository, code deposit, or downloadable dataset URL. The data availability statement only says data are within the manuscript/supplementary files, and虽

No evidence-backed public reproduction asset is currently recorded.