A Sensor Network to Monitor Process Parameters of Fermentation and Drying in Black Tea Production

Saikia, Debashis; Boruah, P. K.; Sarma, Utpal

doi:10.1007/s12647-015-0142-4

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…The Python programming language (Kumar and Panda, 2019) was used for writing programmes to capture the images using the Pi camera. It has various libraries (Dubosson et al, 2016) and is open source (Samal et al, 2017). Some of the libraries adopted included the following: TensorFlow (Tohid et al, 2019), Keras (Stancin and Jovic, 2019), Seaborn (Fahad and Yahya, 2018), Matplotlib (Hung et al, 2020), pandas (Li et al, 2020), and NumPy (Momm et al, 2020).…”

Section: Resourcesmentioning

confidence: 99%

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

Kimutai

Ngenzi

Ngoga

et al. 2021

J. Sens. Sens. Syst.

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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.

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Section: Resourcesmentioning

confidence: 99%

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

Kimutai

Ngenzi

Ngoga

et al. 2021

J. Sens. Sens. Syst.

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“…Tea is currently among the most popularly consumed beverage across the world [1] and is responsible for the economic growth of many countries, including India, Sri-Lanka, Kenya, China among other countries [2]. These top tea-producing countries produce several varieties of tea, which include: yellow tea, illex tea, oolong tea, black tea, white tea, among others [3].…”

Section: Background and Rationalementioning

confidence: 99%

A Data Descriptor for Black Tea Fermentation Dataset

Kimutai

Ngenzi

Said

et al. 2021

Data

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Tea is currently the most popular beverage after water. Tea contributes to the livelihood of more than 10 million people globally. There are several categories of tea, but black tea is the most popular, accounting for about 78% of total tea consumption. Processing of black tea involves the following steps: plucking, withering, crushing, tearing and curling, fermentation, drying, sorting, and packaging. Fermentation is the most important step in determining the final quality of the processed tea. Fermentation is a time-bound process and it must take place under certain temperature and humidity conditions. During fermentation, tea color changes from green to coppery brown to signify the attainment of optimum fermentation levels. These parameters are currently manually monitored. At present, there is only one existing dataset on tea fermentation images. This study makes a tea fermentation dataset available, composed of tea fermentation conditions and tea fermentation images.

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“…It should be noted that this manual means of data collection may reduce the accuracy of the results due to direct human intervention. Saikia, Boruah, & Sarma, (2015) developed an instrumentation system sensor network comprising thermocouple and RH -to -voltage converter for temperature and RH sensing respectively in black tea fermentation and drying process. The thermocouple output was conditioned using dedicated signal conditioning, while the RH sensor output signal has been pre-conditioned, thus making both the parameter sensing and signal conditioning stages exist as one entity.…”

Section: *Corresponding Authormentioning

confidence: 99%

Development of an Instrumentation System for a Laboratory Model Food Product Dryer

2020

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To achieve optimal dryer performance, the process parameters required for both the optimization and control of the drying process must be made available via the instrumentation system. A few works have been reported on the development of instrumentation systems for handling drying system parameters. Out of which, some are deficient in the number of drying process parameters that can be handled, while others are unreliable and inaccurate. Therefore, there is the need to develop a microcontroller-based instrumentation system that can monitor, measure, control, display and store the main drying process parameters and sample weight with a high degree of reliability and accuracy. In this study, the sensors were selected based on system specifications and interfaced with the microcontroller. The codes for controlling, logging and displaying of drying parameters were developed and installed on the microcontroller. When tested at steady-state conditions, the system yielded satisfactory results with maximum control and detection errors being 2.0% and 1.8% for the temperature and sample weight, respectively. The developed system can be used for efficient computation of both the dry and wet basis sample moisture content values and also detect the set sample weight. Keywords— Dryer, Drying parameters, Instrumentation system, Moisture content, Sensor.

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A Sensor Network to Monitor Process Parameters of Fermentation and Drying in Black Tea Production

Cited by 12 publications

References 14 publications

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

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

A Data Descriptor for Black Tea Fermentation Dataset

Development of an Instrumentation System for a Laboratory Model Food Product Dryer

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