Research on fresh tea leaf grading method based on geometric features

Due to the high complexity and difficulty distinguishing different tea leaf grades in machine-picked fresh tea and avoiding subjective factors, this paper proposes a geometric feature-based grading method for fresh tea classification. It classifies three common types of fresh tea leaves in factories: single bud(A0), one bud with one leaf(A1), and one with two leaves(A2). Firstly, the collected samples of fresh tea leaves are preprocessed, including target cropping, shadow removal, and image denoising. Then, the image is grayscale and binarized to extract the contour of the fresh leaves. Next, the Douglas-Peucker algorithm is used to approximate the edge contour of the fresh tea leaves with a polygon, and the contour edges are extended along a specific vertex. The position relationship between an arbitrary point on the extension line and the polygon contour determines the convex-concave nature of the vertex. Then, the initial key points of the tea leaf contour are found among all convex vertices of the polygon contour based on geometric distance, and the remaining key points are found by combining the initial key points and the concave points of the contour. Finally, fresh tea leaves are classified based on the number of key points. The results show that the accuracy of fresh tea grading can reach 97.08%, effectively classifying fresh tea and providing a reference for objective grading of fresh tea.