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- Title
Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.
- Authors
Li, Jinsong; Li, Qijun; Luo, Wei; Zeng, Liang; Luo, Liyong
- Abstract
Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.
- Subjects
MACHINE learning; SUPPORT vector machines; RANDOM forest algorithms; COMPUTER systems; SENSORY evaluation; GREEN tea; TEA extracts
- Publication
Foods, 2024, Vol 13, Issue 16, p2516
- ISSN
2304-8158
- Publication type
Article
- DOI
10.3390/foods13162516