Research on fresh tea leaf grading method based on geometric features

Huifang Yang; Weihua Wang; Shuang Zheng

doi:10.1117/12.3045793

9 October 2024 Research on fresh tea leaf grading method based on geometric features

Huifang Yang, Weihua Wang, Shuang Zheng

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 1328803 (2024) https://doi.org/10.1117/12.3045793
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huifang Yang, Weihua Wang, and Shuang Zheng "Research on fresh tea leaf grading method based on geometric features", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 1328803 (9 October 2024); https://doi.org/10.1117/12.3045793

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KEYWORDS

Shadows

Scene classification

Image classification

Image processing

RGB color model

Contour extraction

Matrices

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