Articles | Volume 10, issue 2
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

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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.