Abstract:
Research indicates that the regression-based tea–weather prediction models for
the tea production in Sri Lanka, based on climate parameters namely, rainfall,
relative humidity, minimum, maximum temperature, average wind speed, and
sunshine hours. Agro climate geographical regions of UVA province, which
contributes more of the country’s tea production, are used for this research. The
significance of climate parameters on tea production was explored using the
random forest algorithm, determining each variable's importance. The results
indicate that the minimum relative humidity, rainfall and the maximum
temperature during the tea plantation period are the most influential climate
indices. Machine learning implementations of the Random Forest (RF), Linear
Regression (LR), Multiple Linear Regression (MLR), and Support Vector
Machine (SVM) were applied for the tea prediction model. According to the
results, RF is the most reliable and accurate model for the prediction of tea
production in Sri Lanka. UVA province prediction model accuracy is 88.79% of
the eight agro climate districts and region-wise prediction tea-production model
accuracy is low parentage of the results. Further MLR and SVM, Machine
Learning implementation trained and validated for the same dataset and although
the results were low percentage compared to the RF implementation model
accuracy. The research, regression analysis already applied for RF, SVM and LR
for the region-wise of the UVA province. Final outcome of the results indicates
that the same process can be applied to the Tea- weather prediction model for all
of the tea growing areas in the country.
