Fundamental Understanding of Tea Growth and Modeling of Precise Tea Shoot Picking Based on 3-D Coordinate Instrument
Open Access
- 17 June 2021
- Vol. 9 (6), 1059
- https://doi.org/10.3390/pr9061059
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.Keywords
This publication has 31 references indexed in Scilit:
- Temperature-based prediction of harvest date in winter and spring cereals as a basis for assessing viability for growing cover cropsField Crops Research, 2021
- In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn BeltInternational Journal of Biometeorology, 2020
- Field experiments and model simulation based evaluation of rice yield response to projected climate change in Southeastern ChinaScience of The Total Environment, 2020
- A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growthGigaScience, 2019
- Differentiation of black tea infusions according to origin, processing and botanical varieties using multivariate statistical analysis of LC-MS dataFood Research International, 2018
- Modelling shoot growth and yield of Ceylon tea cultivar TRI-2025 (Camellia sinensis (L.) O. Kuntze)The Journal of Agricultural Science, 2018
- A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody CropsSensors, 2017
- Multitargeted therapy of cancer by green tea polyphenolsCancer Letters, 2008
- Beneficial effects of tea catechins on diet-induced obesity: stimulation of lipid catabolism in the liverInternational Journal of Obesity, 2002
- Climate and weather variability at the Tea Research Foundation of KenyaAgricultural and Forest Meteorology, 1992