Abstract:
Tea cultivation is one of the main sources of foreign exchange earnings in Sri
Lanka and the Tea Research Institute (TRI) is the only national institution in
Sri Lanka for generating and disseminating related new technologies. TRI has
introduced tea clones under several series. These clones cannot be grown
everywhere in the country. Clones should be grown in cultivation zones
recommended by the TRI. When obtaining tea shoots for tea nurseries, it is
very important to know what kind of tea clones that the mother plant belongs
to. The selected clone types should be clones recommended for the cultivation
zone we are going to plant. Otherwise, diseases in the tea plantation may
increase and the cultivation may be destroyed. The subtle differences between
tea clones make it difficult to distinguish them. It is the main problem for tea
growers (especially novices). The major aim of this research is to minimize
the difficulty of identifying the three most widely grown tea clones in Sri
Lanka, namely TRI 2023, TRI 2025, and TRI 2026. This study found that tea
clones can be distinguished using the second and fourth normal leaves. A
Convolutional Neural Network (CNN) was trained using images of these
leaves to distinguish these three types of tea clones, achieving 97% accuracy
in 30 epochs. The implemented framework was tested using the test dataset
contained (30) images collected from small tea holdings. This study
significantly proposes a web application and a framework which are to provide
predictions for distinguishing above tea clones.
