Rice blast, caused by Magnaporthe oryzae, is a devastating fungal disease threatening global rice production, with annual yield losses ranging from 10% to 30% in epidemic regions. Conventional detection methods rely on visual inspection and laboratory diagnosis, which are limited by subjectivity, time-consuming procedures, and the inability to detect early-stage infections. Hyperspectral imaging technology offers a highly promising method for detecting rice blast disease. It can capture the physiological and biochemical changes that occur in plant tissues before the appearance of visible symptoms. In this study, we propose a Convolutional-State Space Hybrid Network (CS-HybridNet) featuring a dual-branch deep learning architecture that synergistically combines a ConvNeXt-based spatial branch with a LiteMamba-based global spatial branch (which models long-range spatial dependencies with spectral embeddings). Principal component analysis was employed to reduce the dimensionality from 360 hyperspectral bands to 16 principal components, retaining 99.09% of the original information while significantly improving computational efficiency. An adaptive feature fusion module effectively integrates spatial texture features with spectral features, enabling complementary information utilization. Experimental results on a dataset comprising 166 hyperspectral images demonstrate that CS-HybridNet achieves 96.30% ± 1.38% accuracy, 98.33% precision, 95.16% recall, and an AUC–ROC value of 0.971 on the independent test set, outperforming traditional machine learning methods and existing deep learning models by 3.5–12.8% in accuracy. Ablation studies validate the effectiveness of each component. This research demonstrates the efficacy of spatial-spectral fusion architecture for automated plant disease detection and establishes a technical foundation for the development of intelligent crop disease monitoring systems.