Abstract Deep learning is an important research achievement of artificial intelligence in recent years and has received special attention from scientists around the world. This study applies deep learning to spectral analysis techniques and proposes a rapid analysis method for cereals. First, the advanced features of the near infrared spectroscopy (NIR) were extracted by the deep learning-stacked sparse autoencoder (SSAE) method, and then the prediction model is built using the affine transformation (AT) and the extreme learning machine (ELM). Experiments were conducted on corn and rice data sets to verify the effectiveness of the method. The results show that the proposed method achieves good prediction results and is superior to other typical NIR analysis methods.

中文翻译：

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深度学习和近红外光谱在谷物分析中的应用

摘要 深度学习是近年来人工智能的一项重要研究成果，受到世界各国科学家的特别关注。本研究将深度学习应用于光谱分析技术，并提出了一种谷物的快速分析方法。首先，通过深度学习堆叠稀疏自编码器（SSAE）方法提取近红外光谱（NIR）的高级特征，然后使用仿射变换（AT）和极限学习机（ELM）建立预测模型. 在玉米和水稻数据集上进行了实验，以验证该方法的有效性。结果表明，所提出的方法取得了良好的预测结果，优于其他典型的近红外分析方法。