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

Viewed

Total article views: 1,416 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
979 381 56 1,416 47 42
  • HTML: 979
  • PDF: 381
  • XML: 56
  • Total: 1,416
  • BibTeX: 47
  • EndNote: 42
Views and downloads (calculated since 02 Jul 2021)
Cumulative views and downloads (calculated since 02 Jul 2021)

Viewed (geographical distribution)

Total article views: 1,355 (including HTML, PDF, and XML) Thereof 1,355 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Apr 2024
Download
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.