A Data Descriptor for Black Tea Fermentation Dataset

Author:

Kimutai GibsonORCID,Ngenzi AlexanderORCID,Ngoga Said RutabayiroORCID,Ramkat Rose C.ORCID,Förster AnnaORCID

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.

Publisher

MDPI AG

Subject

Information Systems and Management,Computer Science Applications,Information Systems

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An instance-based deep transfer learning approach for resource-constrained environments;Proceedings of the ACM SIGCOMM Workshop on Networked Sensing Systems for a Sustainable Society;2022-08-22

2. An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques;Journal of Sensors and Sensor Systems;2021-07-02

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