Online Evaluation System of Tea Quality Based on Computer Vision

In order to realize the online evaluation and automatic grading of tea quality, and eliminate the defects of artificial sensory evaluation of tea quality, this paper focused on development ofa set of tea quality online evaluation and automatic grading system basing on computer vision technology. The software was designed by Open CV and Visual C++ realizingthe online evaluation for tea quality, combining supervised orthogonal locality preserving projections (SOLPP) to reduce the dimensionality for image features variables. The sensory evaluation models of tea quality were respectively developed based on RF (random forest), BP-ANN (back-propagation artificial neural network) and SVR (relevance vector machine), by contrast, the performance of based RF model was the optimal. The system automatically completed the image acquisition of tea samples, original image preprocessing, feature extraction, and the grade evaluation to the tea samples based on the developed models. According to the evaluation results, the tea samples were transfered to the corresponding grading tank by the grading and collecting device droved by the control system. The prototype testing results showed that the classification accuracy rate of the Wuyuan Xianzhi green tea and Biluochun green tea reached more than 93%. This developed system had simple structure with stable operation, and the samples could be accurately sent to the corresponding grade tanks, which could meet the requirements of online evaluation of tea grade.