A Deep Learning Based Approach on Categorization of Tea Leaf

Kamrul, Mahadi Hasan; Rahman, Mokhlesur; Robin, Risul Islam; Hossain, Safayet; Hasan, Mohammad H.; Paul, Pritom

doi:10.1145/3377049.3377122

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…Root Mean Squad Error (RMSE) is in essence the same as MSE, which is an expression of the MSE open root sign for a better description of the data. The calculation formula is shown in Equation (10).…”

Section: The Loss Functionmentioning

confidence: 99%

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Deep Learning Model for Soil Environment Quality Classification of Pu-erh Tea

Cai

Yuan

Liu

et al. 2022

Forests

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Pu-erh tea, Camellia sinensis is a traditional Chinese tea, one of the black teas, originally produced in China’s Yunnan Province, named after its origin and distribution center in Pu-erh, Yunnan. Yunnan Pu-erh tea is protected by geographical Indication and has unique quality characteristics. It is made from Yunnan large-leaf sun-green tea with specific processing techniques. The quality formation of Pu-erh tea is closely related to the soil’s environmental conditions. In this paper, time-by-time data of the soil environment of tea plantations during the autumn tea harvesting period in Menghai County, Xishuangbanna, Yunnan Province, China, in 2021 were analyzed. Spearman’s correlation analysis was conducted between the inner components of Pu’er tea and the soil environmental factor. The analysis showed that three soil environmental indicators, soil temperature, soil moisture, and soil pH, were highly significantly correlated. The soil environmental quality evaluation method was proposed based on the selected soil environmental characteristics. Meanwhile, a deep learning model of Long Short Term Memory (LSTM) Network for the soil environmental quality of tea plantation was established according to the proposed method, and the soil environmental quality of tea was classified into four classes. In addition, the paper also compares the constructed models based on BP neural network and random forest to evaluate the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the indicators for comparative analysis. This paper innovatively proposes to introduce the main inclusions of Pu’er tea into the classification and discrimination model of the soil environment in tea plantations, while using machine learning-related algorithms to classify and predict the categories of soil environmental quality, instead of relying solely on statistical data for analysis. This research work makes it possible to quickly and accurately determines the physiological status of tea leaves based on the establishment of a soil environment quality prediction model, which provides effective data for the intelligent management of tea plantations and has the advantage of rapid and low-cost assessment compared with the need to measure the intrinsic quality of Pu-erh tea after harvesting is completed.

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Section: The Loss Functionmentioning

confidence: 99%

“…With the combination of big data and agriculture, deep learning is also widely used in the field of tea research [6][7][8][9][10]. At present, the most used scope is to identify the type of tea, identify pests and diseases, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for Soil Environment Quality Classification of Pu-erh Tea

Cai

Yuan

Liu

et al. 2022

Forests

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“…From the experts' judgement, fermentation degrees of tea with time depends on the following factors: time (Obanda et al, 2001), temperature and humidity level at which fermentation takes place (Owuor and Obanda, 2001), the clones of the tea, nutrition levels of the tea, age of tea, stage of growth of tea, plucking standards, and post-harvesting handling. Presently, more than 20 clones of tea are grown in Kenya (Kamunya et al, 2012). Figure 3 shows sample images of three classes of tea, i.e.…”

Section: Image Databasementioning

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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“…Therefore, the identification of fresh tea leaves in the complex environment of tea plantations is a difficult issue of research in this field. Kamrul et al [9] applied three classification algorithm network models, VGG16 (very deep convolutional networks with 16 layers), CNN, and R-CNN (region-CNN), to identify fresh tea leaves, and the recognition accuracy of the three models all exceeded 92%. Wei et al [10] identified and classified fresh tea leaves based on fluorescence imaging tea images, and used two CNN models VGG16 and ResNet-34 for model training, respectively.…”

Section: Introductionmentioning

confidence: 99%

Research on Tea Trees Germination Density Detection Based on Improved YOLOv5

Wang

Yang

et al. 2022

Forests

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Tea plants are one of the most widely planted agricultural crops in the world. The traditional method of surveying germination density is mainly manual checking, which is time-consuming and inefficient. In this research, the Improved YOLOv5 model was used to identify tea buds and detect germination density based on tea trees canopy visible images. Firstly, five original YOLOv5 models were trained for tea trees germination recognition, and performance and volume were compared. Secondly, backbone structure was redesigned based on the lightweight theory of Xception and ShuffleNetV2. Meanwhile, reverse attention mechanism (RA) and receptive field block (RFB) were added to enhance the network feature extraction ability, achieving the purpose of optimizing the YOLOv5 network from both lightweight and accuracy improvement. Finally, the recognition ability of the Improved YOLOv5 model was analyzed, and the germination density of tea trees was detected according to the tea bud count. The experimental results show that: (1) The parameter numbers of the five original YOLOv5 models were inversely proportional to the detection accuracy. The YOLOv5m model with the most balanced comprehensive performance contained 20,852,934 parameters, the precision rate of the YOLOv5m recognition model was 74.9%, the recall rate was 75.7%, and the mAP_0.5 was 0.758. (2) The Improved YOLOv5 model contained 4,326,815 parameters, the precision rate of the Improved YOLOv5 recognition model was 94.9%, the recall rate was 97.67%, and the mAP_0.5 was 0.758. (3) The YOLOv5m model and the Improved YOLOv5 model were used to test the validation set, and the true positive (TP) values identified were 86% and 94%, respectively. The Improved YOLOv5 network model was effectively improved in both volume and accuracy according to the result. This research is conducive to scientific planning of tea bud picking, improving the production efficiency of the tea plantation and the quality of tea production in the later stage.

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A Deep Learning Based Approach on Categorization of Tea Leaf

Cited by 8 publications

References 5 publications

Deep Learning Model for Soil Environment Quality Classification of Pu-erh Tea

Deep Learning Model for Soil Environment Quality Classification of Pu-erh Tea

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

Research on Tea Trees Germination Density Detection Based on Improved YOLOv5

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