PSUMamba: Dual-Path Bidirectional Mamba for Plant Stress Monitoring via Temporal Hyperspectral Imaging

Abstract

Abstract Temporal hyperspectral imaging enables non-destructive monitoring of agricultural stress through spectral signatures evolving across extended observation periods, yet processing high-dimensional spatial-spectral-temporal sequences remains computationally prohibitive for real-time deployment. Traditional machine learning methods sacrifice temporal information through dimensionality reduction, while hybrid deep learning architectures combining convolutional and recurrent networks suffer from optimization pathologies at component boundaries. We introduce PSUMamba, a dual-path bidirectional Mamba architecture that processes 204-band hyperspectral sequences across eight timepoints through linear-complexity state space models, achieving 95.05% accuracy with 99.00% AUC-ROC using 153,268 parameters. The architecture maintains perfect specificity (100%) with 93.67% sensitivity while out-performing Vision Transformer with 39-fold fewer parameters and 37.5% reduced training time. Separate spectral and temporal pathways with adaptive fusion enable specialized biochemical and physiological feature extraction without quadratic attention overhead. Ablation studies confirm temporal features dominate classification under experimental conditions, with dual-path fusion providing superior probabilistic calibration (97.35% AUC) over single-path variants. Statistical comparisons demonstrate significant improvements over PLS-DA (∆=12.07%, p=0.0001), 3D CNN (∆=15.48%, p=0.0042) and 1D CNN-LSTM (∆=33.77%, p

Plant phenotyping relevance

植物ストレス状態を時間分解ハイパースペクトル画像から推定する計算・センシング手法を開発し、既存手法との比較およびアブレーションで検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's hyperspectral imaging dataset and trained model weights are not publicly deposited; the authors state they are available from the corresponding author upon reasonable request. The preprocessing/model code is only promised to be public upon acceptance, so it does not qualify as an actionable public asset. No

No evidence-backed public reproduction asset is currently recorded.