An ANN Model to Estimate the Impact of Tea Process Parameters on Tea Quality
Abstract
Present study deals with the development of an artificial neural network (ANN)-based technique for tea quality quantification by monitoring fermentation and drying condition of the tea processing stages. An RS485 network-based instrumentation system has been developed and implemented for data collection for these two stages. Three calibrated sensor nodes are installed in the fermentation room due to its larger floor area to collect temperature and relative humidity (RH). Dryer inlet temperature is recorded using a calibrated thermocouple-based sensor node. From seven input parameters and target quality data obtained from tea taster, the ANN model has been developed to find the correlation between the process condition and the tea quality. From the correlation study, more than 90% classification rate is obtained from the model. The model is also validated with some independent data showing more than 60% correlation. Error in terms of root mean square error (RMSE) is about 0.17. This model will be helpful for improvement of tea quality.
This paper was recommended by Regional Editor Tongquan Wei.
