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An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

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  • 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)

  • 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)

  • Ambrose Kiprop

    (Department of Chemistry and Biochemistry, Moi University, P.O. Box, 3900-30100 Eldoret, Kenya
    African Center of Excellence in Phytochemicals, Textile and Renewable Energy (ACE II-PTRE), P.O. Box, 3900-30100 Eldoret, Kenya)

  • Anna Förster

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

Abstract

Tea is one of the most popular beverages in the world, and its processing involves a number of steps which includes fermentation. Tea fermentation is the most important step in determining the quality of tea. Currently, optimum fermentation of tea is detected by tasters using any of the following methods: monitoring change in color of tea as fermentation progresses and tasting and smelling the tea as fermentation progresses. These manual methods are not accurate. Consequently, they lead to a compromise in the quality of tea. This study proposes a deep learning model dubbed TeaNet based on Convolution Neural Networks (CNN). The input data to TeaNet are images from the tea Fermentation and Labelme datasets. We compared the performance of TeaNet with other standard machine learning techniques: Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). TeaNet was more superior in the classification tasks compared to the other machine learning techniques. However, we will confirm the stability of TeaNet in the classification tasks in our future studies when we deploy it in a tea factory in Kenya. The research also released a tea fermentation dataset that is available for use by the community.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:2:p:44-:d:352352
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    References listed on IDEAS

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    Cited by:

    1. 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.

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