Abstract
Tea is a popular beverage worldwide and also has great medical value. A fundamental understanding of tea shoot growth and a precision picking model should be established to realize mechanized picking of tea shoots with a small product loss. Accordingly, the terminal bud length (Lbud), tea stem length (Lstem), terminal bud angle (αbud), tea stem angle (αstem), and growth time (t) were considered as the key growth parameters; the sum of the vertical lengths of the terminal bud and stem (ξ), the picking radius (r), and the vertical length of the stem (Zstem) were considered as the picking indexes of the tea shoots. The variations in growth parameters with time were investigated using a 3-D coordinate instrument, and the relationships between the growth parameters and the picking indexes were established using an artificial neural network (ANN). The results indicated that the tea growth cycles for periods P1, P2, P3, P4, P5, and P6 were 14, 7, 6, 4, 4, and 6 d, respectively. A growth cycle diagram of the tea growth was established. Moreover, a 5-2-12-3 ANN model was developed. The best prediction of ξ, r, and Zstem was found with 16 training epochs. The MSE value was 0.0923 × 10−4, and the R values for the training, test, and validation data were 0.99976, 0.99871, and 0.99857, respectively, indicating that the established ANN model demonstrates excellent performance in predicting the picking indexes of tea shoots.
Highlights
The predictive performance of the established artificial neural network (ANN) model was estimated using statistical indexes including the coefficient of determination (R2), the mean squared error (MSE), and the mean absolute error (MAE), which can be calculated using Equations (24)–(26), respectively [39]
To ascertain the variation rules of the picking indexes with growth time, the interval time between two adjacent terminal bud branches was considered as the growth cycle of the tea shoots
The variations in Lbud, Lstem, and ξ with t for different periods are shown in Figure 7; the growth cycles of the tea shoots are different for different periods
Summary
Koichi (1988) and Takezawa (1994) improved the prediction accuracy of there are many factors affecting the development of tea shoots, and the same batch of tea tea shoot growth at different temperatures using water as a variable [11,12]. Jayasinghe established models for predicting theintroduced leaf area growth time as a variable to investigate the variation in tea shoot growth over in a and fresh weight of tea shoots at different development stages and locations. Th focuses on the growth rule of tea shoots, applying a 3-D coordinate instrument to key indices including terminal bud length (Lbud), stem length (Lstem), terminal bu accurate and fast artificial algorithm for crop morphology and yield prediction (αbud), and stem angleintelligence (αstem) in different growth periods. Tea shoots with one shoot and three intact leaves were used in the experiment
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