Hyperspectral image (HSI) crop analysis is essential for precision agriculture because it captures rich spectral and spatial information for accurate crop monitoring and assessment. However, HSI classification remains challenging due to high spectral dimensionality, spatial complexity, class imbalance, and limited labeled samples. To address these challenges, this paper proposes a BiSpectral Mamba-based framework that combines multi-scale convolutional feature extraction, spectral attention, bidirectional state-space modeling, and quantum-inspired learning. A multi-scale CNN backbone first extracts hierarchical spatial-spectral representations through feature fusion across multiple resolutions. A spectral attention mechanism then emphasizes informative bands while suppressing redundant and noisy channels. The refined features are processed by a BiSpectral Mamba module that captures long-range dependencies in both forward and backward directions by modeling hyperspectral feature maps as sequential tokens. In addition, class-weighted optimization and feature fusion strategies are incorporated to improve training stability and mitigate class imbalance. Experimental evaluation on the UAVHSI-Crop dataset demonstrates the effectiveness of the proposed framework, achieving an overall accuracy of 84.83%. The results show that integrating convolutional, attention-based, and state-space modeling components enables robust spatial-spectral feature learning for crop classification. The proposed framework also shows potential for broader agricultural and remote sensing applications, including crop disease detection, yield prediction, and soil moisture estimation, while highlighting the effectiveness of structured state-space and quantum-inspired architectures for hyperspectral image analysis.

翻译：高光谱图像作物分析对精准农业至关重要，因其可捕获丰富的光谱与空间信息以实现精确的作物监测与评估。然而，高光谱图像分类仍面临光谱维度高、空间复杂度大、类别不均衡及标注样本有限等挑战。针对这些问题，本文提出一种基于BiSpectral Mamba的框架，融合多尺度卷积特征提取、光谱注意力机制、双向状态空间建模及量子启发学习。该框架首先通过多尺度CNN骨干网络，利用跨分辨率特征融合提取层次化的空间-光谱表征；继而采用光谱注意力机制增强有效波段、抑制冗余噪声通道；精炼特征送入BiSpectral Mamba模块，通过将高光谱特征图建模为序列令牌，在正反双向捕获长程依赖关系。此外，引入类别加权优化与特征融合策略提升训练稳定性并缓解类别不均衡。在UAVHSI-Crop数据集上的实验验证了该框架的有效性，总体分类精度达84.83%。结果表明，融合卷积、注意力与状态空间建模组件可实现对作物分类的鲁棒空间-光谱特征学习。该框架在作物病害检测、产量预测及土壤湿度估算等更广泛的农业与遥感应用中亦展现出潜力，同时凸显了结构化状态空间与量子启发架构在高光谱图像分析中的有效性。