Classification of black tea leaf water content based on hyperspectral imaging

Abstract

Withering is an important process step in black tea production, and the degree of withering directly determines the quality and flavor of black tea. Presently, the judgment of the degree of the withering of black tea at home and abroad mainly depends on the experience of tea masters, which is highly subjective and quickly leads to the inconsistent quality of finished tea. Based on these reasons, this thesis takes Ying Hong No. 9 black tea as the research object, extracts the spectral information of 50 tea leaves in four time periods using hyperspectral imaging technology and an image processing algorithm, and classifies them using a support vector machine, K-value proximity, random forest algorithm, and BP neural network, and the experimental results show that the spectral information of tea leaves in different periods has some variability. The BP neural network in The classification accuracy on the test set reached 73.3%, which was significantly better than the other three algorithms. Therefore, it is feasible to use hyperspectral imaging technology and related classification algorithms to nondestructively identify and classify the water content of black tea leaves during the tail-withering process.