Study on classification and condition monitoring method of medicinal plants based on hyperspectral technology

With the rapid development of the global Chinese medicine industry, consumers' demand for high-quality Chinese herbal medicines and their preparations continues to rise, which in turn puts forward higher requirements for the quality control and management of medicinal plants. Facing the problems of subjectivity, low efficiency and high cost in traditional quality control and management methods, this paper will deeply explore the practicability and feasibility of hyperspectral technology based on practical application requirements, and put forward a set of new methods for classification and condition monitoring of medicinal plants, aiming at making up for the shortcomings of traditional methods and improving classification accuracy and monitoring efficiency. Practice has proved that hyperspectral imaging equipment is used to collect comprehensive spectral data of various medicinal plants, and the feature vectors of species and state differences of medicinal plants are extracted with corresponding feature engineering, and then efficient and accurate automatic classification is realized with the help of machine learning algorithm. At the same time, by collecting hyperspectral data of medicinal plants regularly, a nondestructive and real-time monitoring method can be formed to obtain the growth status, diseases and insect pests and nutritional status of medicinal plants, which provides strong technical support and scientific basis for promoting the improvement of quality control and management system of medicinal plants.