Advances in hyperspectral imaging (HSI) and 3D reconstruction have enabled accurate, high-throughput characterization of agricultural produce quality and plant phenotypes, both essential for advancing agricultural sustainability and breeding programs. HSI captures detailed biochemical features of produce, while 3D geometric data substantially improves morphological analysis. However, integrating these two modalities at scale remains challenging, as conventional approaches involve complex hardware setups incompatible with automated phenotyping systems. Recent advances in neural radiance fields (NeRF) offer computationally efficient 3D reconstruction but typically require moving-camera setups, limiting throughput and reproducibility in standard indoor agricultural environments. To address these challenges, we introduce HSI-SC-NeRF, a stationary-camera multi-channel NeRF framework for high-throughput hyperspectral 3D reconstruction targeting postharvest inspection of agricultural produce. Multi-view hyperspectral data is captured using a stationary camera while the object rotates within a custom-built Teflon imaging chamber providing diffuse, uniform illumination. Object poses are estimated via ArUco calibration markers and transformed to the camera frame of reference through simulated pose transformations, enabling standard NeRF training on stationary-camera data. A multi-channel NeRF formulation optimizes reconstruction across all hyperspectral bands jointly using a composite spectral loss, supported by a two-stage training protocol that decouples geometric initialization from radiometric refinement. Experiments on three agricultural produce samples demonstrate high spatial reconstruction accuracy and strong spectral fidelity across the visible and near-infrared spectrum, confirming the suitability of HSI-SC-NeRF for integration into automated agricultural workflows.

翻译：高光谱成像与三维重建技术的进步，使得对农产品质量和植物表型进行精确、高通量的表征成为可能，这对于推进农业可持续性和育种计划至关重要。高光谱成像能够捕捉农产品的详细生化特征，而三维几何数据则显著改善了形态学分析。然而，大规模整合这两种模态仍然具有挑战性，因为传统方法涉及复杂的硬件设置，与自动化表型分析系统不兼容。神经辐射场的最新进展提供了计算高效的三维重建方案，但通常需要移动相机设置，这在标准的室内农业环境中限制了通量和可重复性。为应对这些挑战，我们提出了HSI-SC-NeRF，一种面向农产品采后检测的高通量高光谱三维重建静态相机多通道NeRF框架。多视角高光谱数据通过静态相机采集，同时物体在定制化的特氟龙成像腔内旋转，该腔体提供漫射、均匀的照明。物体姿态通过ArUco校准标记进行估计，并通过模拟姿态变换转换到相机参考坐标系，从而实现对静态相机数据进行标准的NeRF训练。多通道NeRF公式通过复合光谱损失联合优化所有高光谱波段的重建，并辅以一个两阶段训练协议，该协议将几何初始化与辐射度细化解耦。在三个农产品样本上的实验表明，该方法在可见光和近红外光谱范围内具有较高的空间重建精度和强大的光谱保真度，证实了HSI-SC-NeRF适合集成到自动化农业工作流程中。