| Autor: |
LU Danyu, JIN Zijing, LU Lu, HE Weizhong, SHU Zaifa, SHAO Jingna, YE Jianhui, LIANG Yuerong |
| Předmět: |
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| Zdroj: |
Journal of Tea Science; 2023, Vol. 43 Issue 5, p691-702, 12p |
| Abstrakt: |
In this study, based on the picture collection of tea shoot growth in the field, we used deep learning target detection algorithm YOLOv5 to construct a model for identifying different growth stages of tea shoots, and the testing results indicate that the model had high accuracy. Furthermore, the Image-J software and the image processing methods of threshold cutting based on Gray, RGB and HSV values were applied to process tea leaf area, and the accuracy and efficiency of different methods were compared. The results show that the accuracy of HSV-based algorithm system of cutting tea leaves and automatically calculating tea leaf area was over 94%, which had better performance than RGB-based algorithm system. The research results provide technical support for the intelligent recognition model of tea growth state and information extraction algorithm of leaf traits, and also build a theoretical basis for the development of tea bud automatic recognition module of tea plucking machinery. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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