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| Study of Sensory Quality Evaluation of Tea Using Computer Vision Technology and Forest Random Method |
| LIU Peng1, WU Rui-mei1, YANG Pu-xiang2, LI Wen-jin2, WEN Jian-ping1, TONG Yang3, HU Xiao3, AI Shi-rong3* |
1. College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
2. Sericulture and Tea Research Institute of Jiangxi Province, Nanchang 330203, China
3. College of Software, Jiangxi Agricultural University, Nanchang 330045, China |
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Abstract In order to make up some flaws of sensory evaluation of tea quality, computer vision technology as a fast and nondestructive method was used to evaluate tea quality in this paper. Biluochun green tea samples were studied, and the tea samples were divided into four grades based on the evaluation results by experts for tea samples. The median filter and Laplace operator were used to preprocess the images of tea samples, and tea appearance features such as color features and texture features were extracted from the preprocessed images. Random forest(RF)method was used to analyze the significance of tea appearance features. The most important features and the optimal amounts of tree pruning of decision tree were investigated to develop the sensory evaluation model of tea quality. And the performance of RF model was compared to that of SVM model. The results show that nine more important features such as hue mean, hue standard deviation, greed channel mean, mean grey, saturation mean, red channel mean, saturation standard deviation, vision mean and uniformity were selected, and the result was consistent with the sensory evaluation profile; the optimal model was obtained when 9 most importance feature variables were selected and tree pruning of decision tree were 500. The overall recognition rate of the model was 95.75%, Kappa coefficient was 0.933, and OOB error was 5%. Compared with SVM model, the average recognition rate and Kappa coefficient of RF algorithm were improved 3.5% and 0.066, respectively. The 9 significant image features selected were consistent with the feature description of tea sensory evaluation terms by NY/T 863—2004. The study indicated that the developed model with a few significant appearance feature variables selected by RF method has high accuracy, and the model is simplified without lowering accuracy. The precision and stability of RF model are superior to that of SVM.
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Received: 2017-12-01
Accepted: 2018-04-22
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Corresponding Authors:
AI Shi-rong
E-mail: aisrong@163.com
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