Near Infrared hyperspectral imaging (HSI) offers a fast and non-destructive method for seed quality assessment through combining spectroscopy and imaging. Recently, convolutional neural networks (CNN) have shown to be promising tools for red-green-blue (RGB) image or spectral cereal classification. This paper describes the design and implementation of deep CNN models capable of utilizing both the spatial and spectral dimension of HSI data simultaneously for analysis of bulk grain samples with densely packed kernels. Classification of eight grain samples, including six different wheat varieties, were used as a test case. The study shows that the CNN architecture ResNet, originally designed for RGB images, can be adapted to use the full spatio-spectral dimension of the HSI data through adding a linear down sample layer prior to the conventional ResNet architecture. Using traditional spectral pre-processing methods before passing the data to the CNN does not improve the classification accuracy of the networks, while a channel-wise image standardization improves the accuracy significantly. The modified ResNet applied to the full spatio-spectral dimension has a classification accuracy of up to 99.75 ± 0.02%, outperforming both purely spectral (86.5 ± 0.1%) and purely spatial (98.70 ± 0.01%) based methods in terms of accuracy, indicating that utilizing spatio-spectral correlation can improve sample classification, but also that grain classification is primarily solved using spatial information. The findings reported in this paper demonstrate how CNN networks can be designed to leverage spatio-spectral information in hyperspectral data. The combination of HSI and spatio-spectral CNN networks shows a possible method for fast prediction of bulk grain quality parameters where both spectral and spatial properties of the grains are important.

近红外高光谱成像 (HSI) 通过结合光谱学和成像技术，为种子质量评估提供了一种快速且无损的方法。最近，卷积神经网络 (CNN) 已被证明是用于红-绿-蓝 (RGB) 图像或光谱谷物分类的有前途的工具。本文描述了深度 CNN 模型的设计和实现，该模型能够同时利用 HSI 数据的空间和光谱维度来分析具有密集核的散装谷物样本。八种谷物样品的分类，包括六种不同的小麦品种，被用作测试用例。研究表明，最初为 RGB 图像设计的 CNN 架构 ResNet，通过在传统的 ResNet 架构之前添加线性下采样层，可以适应使用 HSI 数据的完整空间谱维度。在将数据传递到 CNN 之前使用传统的光谱预处理方法并不能提高网络的分类精度，而通道图像标准化显着提高了精度。应用于全空间光谱维度的修改后的 ResNet 的分类精度高达 99.75 ± 0.02%，在精度方面优于纯光谱 (86.5 ± 0.1%) 和纯空间 (98.70 ± 0.01%) 方法，表明利用空间光谱相关性可以改善样本分类，但谷物分类主要是使用空间信息来解决的。本文报告的研究结果展示了如何设计 CNN 网络以利用高光谱数据中的空间光谱信息。HSI 和空间光谱 CNN 网络的结合显示了一种快速预测散装谷物质量参数的可能方法，其中谷物的光谱和空间特性都很重要。