Abstract
Abstract. Tea (Camellia sinensis) is one of the most consumed drinks across the world. Based on processing techniques, there are more than 15 000 categories of tea, but the main categories include yellow tea, Oolong tea, Illex tea, black tea, matcha tea, green tea, and sencha tea, among others. Black tea is the most popular among the categories worldwide. During black tea processing, the following stages occur: plucking, withering, cutting, tearing, curling, fermentation, drying, and sorting. Although all these stages affect the quality of the processed tea, fermentation is the most vital as it directly defines the quality. Fermentation is a time-bound process, and its optimum is currently manually detected by tea tasters monitoring colour change, smelling the tea, and tasting the tea as fermentation progresses. This paper explores the use of the internet of things (IoT), deep convolutional neural networks, and image processing with majority voting techniques in detecting the optimum fermentation of black tea. The prototype was made up of Raspberry Pi 3 models with a Pi camera to take real-time images of tea as fermentation progresses. We deployed the prototype in the Sisibo Tea Factory for training, validation, and evaluation. When the deep learner was evaluated on offline images, it had a perfect precision and accuracy of 1.0 each. The deep learner recorded the highest precision and accuracy of 0.9589 and 0.8646, respectively, when evaluated on real-time images. Additionally, the deep learner recorded an average precision and accuracy of 0.9737 and 0.8953, respectively, when a majority voting technique was applied in decision-making. From the results, it is evident that the prototype can be used to monitor the fermentation of various categories of tea that undergo fermentation, including Oolong and black tea, among others. Additionally, the prototype can also be scaled up by retraining it for use in monitoring the fermentation of other crops, including coffee and cocoa.
Highlights
Tea (Camellia sinensis) is currently among the most prevalent and extensively consumed drinks across the world, with a daily consumption of more than 2 million cups
This research has proposed a tea fermentation detection system based on internet of things (IoT), deep learning, and majority voting techniques
The deep learner model was composed of three convolutional layers and three pooling layers
Summary
Tea (Camellia sinensis) is currently among the most prevalent and extensively consumed drinks across the world, with a daily consumption of more than 2 million cups. Historical evidence indicates that the tea plant was indigenous to China and Burma, among other countries (Akuli et al, 2016). Tea is a source of many types of tea, which includes Oolong tea, black tea, white tea, matcha tea, sencha tea, green tea, and yellow tea, among others. The processing techniques determine the category of tea produced. Kenya is the leading producer of black tea. Black tea is the most popular among the categories of tea, and it accounts for an estimate of 79 % (Mitei, 2011) of the entire global tea consumption
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