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

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.

An instance-based deep transfer learning method for quality identification of Longjing tea from multiple geographical origins

“…The results show that LeafNet was superior in the recognition of tea leaf diseases compared to the MLP and SVM algorithms. Kimutai et al [28] proposed a deep learning model named TeaNet to detect the optimum fermentation of tea. The experimental results showed that TeaNet was superior in the classification tasks compared to the other machine learning techniques, including KNN, SVM, RF, and linear discriminant analysis (LDA).…”

Internet of Things (IoT) in the food fermentation process: A bibliometric review

“…Many researchers have applied IoT in the food fermentation process to enhance high‐nutritional content and process optimization. Kimutai et al (2021) investigated how IoT, deep convolutional neural networks (CNN), and image processing may be used with majority voting strategies to choose the best black tea fermentation process. To picture tea in real‐time while the fermentation process developed, the authors built a prototype utilizing Raspberry Pi 3 models and a Pi camera.…”

“…IoT innovation has found a wide variety of uses ranging from development to designing, medication, fabricating, sports, and administration to practically all sectors of the economy (Kimutai et al, 2021). In addition, IoT has recently found a significant application in the agricultural sector.…”

“…Kimutai et al (2021) investigated how IoT, deep convolutional neural networks (CNN), and image processing may be used with majority voting strategies to choose the best black tea fermentation process.…”