人工智能和工业4.0视域下高光谱成像技术融合深度学习方法在中药领域中的应用与展望

Application and prospects of hyperspectral imaging and deep learning in traditional Chinese medicine in context of AI and industry 4.0

21世纪,人工智能的崛起标志着世界进入工业4.0时代。随着计算机、电子信息科学等应用领域迅猛发展,机器学习作为人工智能的核心智慧,为中药现代化提供了方法学上的新思路。将支持向量机(support vector machines, SVM)、极限学习机(extreme learning machine, ELM)、卷积神经网络(convolutional neural networks, CNN)、循环神经网络(recurrent neural network, RNN)等机器学习方法与高光谱成像技术融合,用于中药真伪鉴定、硫熏鉴别、产地区分、含量测定、霉菌检测等,可以很大程度上解决中药质量难以严格把控的问题。该文总结了近年高光谱成像技术(hyperspectral imaging, HSI)结合机器学习方法在中药领域中的应用,重点介绍了高光谱成像技术的原理、图像预处理方法和深度学习算法,并对未来高光谱成像技术在中药领域的发展前景进行展望。

In the 21 st century, the rise of artificial intelligence(AI) marks the arrival of the intelligence era or the era of Industry 4.0. In addition to the rapid development of computer and electronic information science, machine learning, as the core intelligence of AI, provides a new methodology for the modernization of traditional Chinese medicine. The algorithms of machine learning include support vector machine(SVM), extreme learning machine(ELM), convolutional neural network(CNN), and recurrent neural network(RNN). The combination of machine learning algorithms and hyperspectral imaging analysis could be used for the identification of fake and inferior herbs, the origin of herbs and the content determination of bioactive ingredients in herbs, which has largely solved the difficulty in strictly controlling the quality of traditional Chinese medicine. The integration of high spectral imaging(HSI) and deep lear-ning will make the predicted results more reliable and suitable for analysis of great amounts of samples. This paper summarizes the application of hyperspectral imaging technology(HSI) and machine learning algorithms in the field of traditional Chinese medicine in recent years, focuses on the principles of hyperspectral imaging technology, preprocessing methods and deep learning algorithms, and gives the prospects of evolution of hyperspectral imaging technology in the field.