Please use this identifier to cite or link to this item: http://ir.mu.ac.ke:8080/jspui/handle/123456789/7093
Title: A data descriptor for black tea fermentation dataset
Authors: Kimutai, Gibson
Ngenzi, Alexander
Ramkat, Rose
Said, Rutabayiro Ngoga
Förster, Anna
Keywords: Tea fermentation
Internet of things
Issue Date: 2021
Publisher: MDPI
Abstract: Tea is currently the most popular beverage after water. Tea contributes to the livelihood of more than 10 million people globally. There are several categories of tea, but black tea is the most popular, accounting for about 78% of total tea consumption. Processing of black tea involves the following steps: plucking, withering, crushing, tearing and curling, fermentation, drying, sorting, and packaging. Fermentation is the most important step in determining the final quality of the processed tea. Fermentation is a time-bound process and it must take place under certain temperature and humidity conditions. During fermentation, tea color changes from green to coppery brown to signify the attainment of optimum fermentation levels. These parameters are currently manually monitored. At present, there is only one existing dataset on tea fermentation images. This study makes a tea fermentation dataset available, composed of tea fermentation conditions and tea fermentation images.
URI: https://doi.org/10.3390/data6030034
http://ir.mu.ac.ke:8080/jspui/handle/123456789/7093
Appears in Collections:School of Biological and Physical Sciences

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.