Advances in hyperspectral imaging (HSI) and 3D reconstruction methods have enabled accurate and high-throughput characterization of agricultural produce quality and plant phenotypes, both of which are essential for advancing agricultural sustainability and enhancing breeding programs. HSI effectively captures detailed biochemical features of produce, while 3D geometric data significantly improves morphological analysis. However, integrating these two modalities at scale poses significant challenges. Conventional 3D reconstruction techniques often involve complex hardware setups that are difficult to integrate into automated phenotyping systems. Recent advances in neural radiance fields (NeRF) offer computationally efficient 3D reconstruction but typically require moving-camera setups, which limit throughput and reproducibility in standard indoor agricultural phenotyping environments. To address these challenges, we introduce a stationary-camera-based multi-channel NeRF framework designed explicitly for high-throughput hyperspectral reconstruction for postharvest inspection of agricultural produce. Our method captures multi-view hyperspectral data using a stationary hyperspectral camera by rotating the object in front of a carefully calibrated dark screen. We then estimate the relative pose of the rotated object with the help of calibration markers. These poses are then transformed to the camera frame of reference using simulated pose transformations, which facilitate NeRF training using standard approaches. In addition, we convert traditional 3-channel NeRF to use the multiple channels of the hyperspectral data to calculate the loss. Our approach yield high-resolution 3D hyperspectral point clouds with high precision-recall scores. Our results demonstrate that stationary-camera NeRF enables robust 3D hyperspectral reconstruction into automated agricultural workflows.