Automated quality control of Hypnum moss in closed bio-production is hindered by continuous phytosanitary degradation like progressive desiccation. Previous deep learning methods treat these biological states as isolated categories and rely on localized convolutions, missing dispersed morphological symptoms. This study represents the first work to apply the 2D visual state space model combined with ordinal regression to moss physiological grading. We propose the visual state space ordinal regression network (VSS-ORNet) to assess moss across four ordinal physiological grades using an expanded dataset of 1 551 images. The framework synergizes a multi-branch inception stem for fine-grained textures with a 2D selective scan mechanism to capture macroscopic canopy shrinkage. A weighted ordinal loss explicitly penalizes large-margin errors to reflect the actual biophysical degradation curve. Under 5-fold cross-validation, VSS-ORNet achieved 93.49% accuracy and a 0.9662 quadratic weighted Kappa, outperforming the best baseline (Inception-ResNet-v2, 91.24%). Crucially, minimizing mean absolute error to 0.0830 ensures that misclassifications are safely confined to visually ambiguous, adjacent growth stages. Operating at 29.36 frames per second, the model satisfies latency requirements for standard batch processing. Ultimately, this non-invasive system enables real-time phytosanitary inspection, preventing cascading economic losses in large-scale bryophyte cultivation and dynamic storage facilities.