Articles | Volume 10, issue 2
https://doi.org/10.5194/jsss-10-153-2021
https://doi.org/10.5194/jsss-10-153-2021
Regular research article
 | 
02 Jul 2021
Regular research article |  | 02 Jul 2021

An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

Gibson Kimutai, Alexander Ngenzi, Said Rutabayiro Ngoga, Rose C. Ramkat, and Anna Förster

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Cited articles

Akuli, A., Pal, A., Bej, G., Dey, T., Ghosh, A., Tudu, B., Bhattacharyya, N., and Bandyopadhyay, R.: A Machine Vision System for Estimation of Theaflavins and Thearubigins in orthodox black tea, International Journal on Smart Sensing and Intelligent Systems, 9, 709–731, https://doi.org/10.21307/ijssis-2017-891, 2016. a, b
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Short summary
This paper discusses the deployment of an internet of things (IoT)-based model to monitor the fermentation of tea in a tea factory in Kenya. The model uses deep learning to detect the optimum fermentation of tea as fermentation progresses. To further improve on the results, a majority voting technique based on regions is used. The model shows promising results, and its predictions correlated well with those of the experts.