Grading Keemun black tea based on shape feature parameters of machine vision

Abstract: Objective and accurate identification of tea grades is indispensable in tea processing and sales. Traditional grade identification often depends on human sensory judgments. This method is subjective, difficult to quantify, and has a certain error probability. The objective of this paper was to establish an objective and accurate method to identify the appearance grade of tea. In this paper, Keemun congou black tea was taken as the research object, and a SVM recognition method based on shape feature histogram multi-feature fusion was proposed. Firstly, the tea image acquisition system was built and the camera parameters were calibrated. Rectangular groups and irregular polygon groups of fix dimensions were used to test the measurement accuracy of the image acquisition system. The RGB image of tea leaves was greyed and its binary image was obtained. In order to obtain uniform shape feature parameters, the rotation of tea image was carried out with the minimum area of the leaf's outer rectangle as the constraint. Secondly, 6 absolute shape features - leaf length, leaf width, leaf area, leaf perimeter, the length and width of minimum area bounding rectangle, were extracted. On this basis, 2 relative shape features of length-width ratio and rectangularity were calculated. The histograms of different tea samples in different interval were further obtained, and the histogram distribution of the above characteristics was used as classifier inputs. Finally, the BP neural network, extreme learning machine (ELM), support vector machine (SVM) and least squares support vector machine (LS-SVM) were used as classifiers to classify tea samples. This result showed that the measurement accuracy of the image acquisition system constructed in this paper was less than 0.3 mm, and the shape feature parameters could be accurately extracted. When identifying all seven grades of tea samples, the recognition accuracy of BP neural network was 53.6%, the recognition accuracy of ELM was 87.86%, the recognition accuracy of SVM was 94.29% and the recognition accuracy of LS-SVM was 95.71%. The details of BP neural network classifier were as below: When 2 grades were classified, the recognition accuracy was 100% and the determination coefficient of the test set was 100.00%. When four grades were classified, the recognition accuracy was 97.5% and the determination coefficient of the test set was 93.19%. When all 7 classes were classified, the determination coefficient of test set was 53.6%. The details of ELM classifier were as below: When three grades were classified, the recognition accuracy was 90.00% When five grades were classified, the recognition accuracy was 88.00%.When SVM classifier with linear kernel function was used to identify 7 grades, the determination coefficient of test set was 86.10%. When LS-SVM classifier with linear kernel function was used to identify 7 grades, the determination coefficient of test set was 96.20%. It could be seen that the classifier based on LS-SVM had higher recognition accuracy and the best effect. There were two types of problems in the classification process: One was as the misidentification rate increased with samples amount increasing in the classification model, the second was the misidentification largely happened in adjacent classes. These problems were discussed in this paper. Through the above research, it was verified that the shape feature could be used to identify the appearance grade of Keemun congou black tea. This paper provided detailed experimental data and reference methods for the objective and digital grade identification of Keemun congou black tea.