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A Data Descriptor for Black Tea Fermentation Dataset

Author

Listed:
  • Gibson Kimutai

    (African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, P.O. Box, 3900 Kigali, Rwanda
    Department of Mathematics, Physics and Computing, Moi University, P.O. Box, 3900-30100 Eldoret, Kenya)

  • Alexander Ngenzi

    (African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, P.O. Box, 3900 Kigali, Rwanda)

  • Rutabayiro Ngoga Said

    (African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, P.O. Box, 3900 Kigali, Rwanda)

  • Rose C. Ramkat

    (Department of Biological Sciences, Moi University, P.O. Box, 3900-30100 Eldoret, Kenya)

  • Anna Förster

    (Sustainable Communication Networks, University of Bremen, 8359 Bremen, Germany)

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.

Suggested Citation

  • Gibson Kimutai & Alexander Ngenzi & Rutabayiro Ngoga Said & Rose C. Ramkat & Anna Förster, 2021. "A Data Descriptor for Black Tea Fermentation Dataset," Data, MDPI, vol. 6(3), pages 1-8, March.
  • Handle: RePEc:gam:jdataj:v:6:y:2021:i:3:p:34-:d:520497
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    References listed on IDEAS

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    1. Gibson Kimutai & Alexander Ngenzi & Rutabayiro Ngoga Said & Ambrose Kiprop & Anna Förster, 2020. "An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks," Data, MDPI, vol. 5(2), pages 1-26, April.
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