Abstract:
Tea (Camellia sinensis) is one of the most consumed drinks across the world. Based on processingtechniques, 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 amongthe 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 processedtea, fermentation is the most vital as it directly defines the quality. Fermentation is a time-bound process, andits optimum is currently manually detected by tea tasters monitoring colour change, smelling the tea, and tastingthe tea as fermentation progresses. This paper explores the use of the internet of things (IoT), deep convolutionalneural networks, and image processing with majority voting techniques in detecting the optimum fermentationof black tea. The prototype was made up of Raspberry Pi 3 models with a Pi camera to take real-time imagesof 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 accuracyof 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 accuracyof 0.9737 and 0.8953, respectively, when a majority voting technique was applied in decision-making. From theresults, it is evident that the prototype can be used to monitor the fermentation of various categories of tea thatundergo fermentation, including Oolong and black tea, among others. Additionally, the prototype can also bescaled up by retraining it for use in monitoring the fermentation of other crops, including coffee and cocoa
(1) (PDF) An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques. Available from: https://www.researchgate.net/publication/352903819_An_internet_of_things_IoT-based_optimum_tea_fermentation_detection_model_using_convolutional_neural_networks_CNNs_and_majority_voting_techniques [accessed Jul 21 2023].
