Abstract
Nowadays, tea farmers often rely on their subjective experience and judgment to predict the harvest time of tea. However, the growth of tea is affected by a lot of uncertain factors such as temperature, amount of rainfall, pruning activity, and sea-level altitude. All the factors result in uncertainty on tea harvesting period and the tea farmers can hardly predict and control the harvest time. In addition to spending time to observe the growing condition of tea leaves, tea farmers have to spend much time to wait tea leaves becoming mature so as to prepare for harvesting jobs. This farming cycle is quite time-consuming and is also a heavy burden to tea farmers. Thus, based on the factors of tea harvest period, this research develops a tea harvest prediction model, which can be applied to any tea gardens in different sea-level altitudes; tea farmers may use this model to accurately and effectively predict the harvest time. This research firstly clarified various factors that have effects on the harvest time. Secondly, this research established a quantitative model according to the factors. Afterward, on the basis of the quantitative model, this research developed several multivariate multiple regression models to predict the harvest time. One of the predictable multivariate multiple regression models was selected as it is the most suitable for predicting the harvest time. At the final stage, this research took some historical records of tea harvesting period collected from tea gardens as cases to verify feasibility of the model. The verification result indicates that the harvest prediction model proposed by this research can efficiently determine the number of day for tea harvesting. That is, the model can be a reference tool for tea farmers to predict the harvest time.