Predicting green tea moisture content during roasting by shape feature

Abstract

This study investigated the changes in tea moisture content and shape features parameters during the roasting process of roasted green tea under different temperatures(70, 100 and 130 °C), leaf amounts (40, 70 and 100 kg), and cylinder speeds (25, 27.5 and 30 r/min), and developed a moisture content prediction model based on Random forest-Genetic algorithm-Back propagation(RF-GA-BP) neural network. Firstly, based on image processing technology, the shape parameters of tea leaves during the roasting process were automatically extracted, and the random forest algorithm was used to rank their importance to reduce the complexity of the model. Then, the activation function and the number of hidden layer nodes in the network structure were optimized. Finally, the initial weights and thresholds of the neural network were optimized using genetic algorithm to obtain the optimal parameters for constructing a moisture content prediction model during the roasting process. The results indicated that the importance of slightness was the highest, with a value of 0.865. The RF-GA-BP neural network model with mean absolute error, root mean square error, and coefficient of determination of 0.030, 0.036, and 0.915, respectively, was superior to the BP, GA-BP, and RF-BP neural network models, and could better predict the moisture content of tea during the roasting process.