Abstract:
Tea is one of the most popular beverages in the world, and its processing involves a number
of steps which includes fermentation. Tea fermentation is the most important step in determining
the quality of tea. Currently, optimum fermentation of tea is detected by tasters using any of the
following methods: monitoring change in color of tea as fermentation progresses and tasting and
smelling the tea as fermentation progresses. These manual methods are not accurate. Consequently,
they lead to a compromise in the quality of tea. This study proposes a deep learning model dubbed
TeaNet based on Convolution Neural Networks (CNN). The input data to TeaNet are images from
the tea Fermentation and Labelme datasets. We compared the performance of TeaNet with other
standard machine learning techniques: Random Forest (RF), K-Nearest Neighbor (KNN), Decision
Tree (DT), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Naive Bayes
(NB). TeaNet was more superior in the classification tasks compared to the other machine learning
techniques. However, we will confirm the stability of TeaNet in the classification tasks in our future
studies when we deploy it in a tea factory in Kenya. The research also released a tea fermentation
dataset that is available for use by the community.