Abstract:
"
Tea is the second most famous beverage of the world which is widely used. Compared
to other crops, tea industry is one of the prominent industries in Sri Lankan Economy.
Tea manufacturing has lots of inside processes such as rolling, withering, fermentation
and drying. Process of withering and drying mostly depend to the quality and taste of
tea. Determining time spent for the entire withering is a huge challenge since it is
usually detected by human experiences or sensory details. Considering the
technological enhancement in software development, tracking the withering process
and estimating the exact time of withering process is an important need for tea industry
to reduce cost and time to exceed the expected profits. So, the dissertation focused on
designing a solution to address this above problem.
The researcher’s intention is to propose a system that can easily track the process of
withering from start to end with a machine learning approach. The system was tested
with multiple algorithms both in machine learning and deep learning. The proposed
system, Twithering-tracker consists with a novel design and program to be worked in
industry level. The predictions are generated using very few amounts of input factors
which mostly impactable. So, the end users can easily use the system with a minimal
effort. Moreover, the proposed system is acted as decision-making system to achieve
maximum benefits.
The dissertation includes all the findings identified using different approaches. It
would be a great contribution since the domain is an untouched area. Furthermore, the
dataset used for the implementation can be also taken as a contribution since it was
freshly collected by the researcher only for this research purpose. After the final
marking phase, the source code, developed model, summary of findings and freshly
collected dataset will be published in an open-source medium."
