Evaluation of Data Mining Strategies for Classification of Black Tea Based on Image-Based Features

Bakhshipour, Adel; Sanaeifar, Alireza; Payman, Sayed Hossein; Guárdia, Miguel de la

doi:10.1007/s12161-017-1075-z

Cited by 32 publications

(16 citation statements)

References 56 publications

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“…At present, research on mechanized picking of famous and excellent tea is still in the exploration stage, and the research direction mainly focuses on the identification and location of tea buds. Early research methods are mainly based on the color, shape, texture, and other characteristics of tea leaves to identify the tea buds [7][8][9]. Such methods have poor robustness and low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

Zhu

Tong

et al. 2021

Agriculture

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Accurately obtaining the posture and spatial position of tea buds through machine vision and other technologies is difficult due to the small size, different shapes, and complex growth environment of tea buds. Therefore, end effectors are prone to problems, such as picking omission and picking error. This study designs a picking end effector based on negative pressure guidance for famous tea. This end effector uses negative pressure to guide tea buds in a top-down manner, thereby correcting their posture and spatial position. Therefore, the designed end effector has deviation tolerance performance that can improve the picking success rate. The pre-experiment is designed, the tip of apical bud is referred to as the descent position, and the negative pressure range is determined to be 0.6 to 0.9 kPa. A deviation tolerance orthogonal experiment is designed. Experimental results show that various experimental factors are ranked in terms of the significance level of the effect on the average success rate, and the significance ranking is as follows: negative pressure (P) > pipe diameter (D) > descent speed (V). An evaluation method of deviation tolerance performance is presented, and the optimal experiment factor-level combination is determined as: P = 0.9 kPa, D = 34 mm, V = 20 mm/s. Within the deviation range of a 10 mm radius, the average success rate of the negative pressure guidance of the end effector is 97.36%. The designed end effector can be applied to the intelligent picking of famous tea. This study can provide a reference for the design of similar picking end effectors for famous tea.

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Section: Introductionmentioning

confidence: 99%

Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

Zhu

Tong

et al. 2021

Agriculture

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“…Commonly, tea quality is determined by a tea tester expert based on the aroma, liquor color, texture, and morphological aspects (Bakhshipour, Sanaeifar, Payman, & de la Guardia, ; Zhu, Ye, He, & Dong, ). This sensory assessment method for tea quality is absolutely experiential without standardization of measurements.…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis

Yuan

Chen

Shao³

et al. 2019

Journal of Food Science

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Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate.Practical Application: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.

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“…The detection of tea grade and type based on machine vision is attractive to the food industry for its convenience in system installation and fast response. Yet, its high requirements on sample positioning, lighting uniformity and focal distance still prevent its practical application [8][9][10].Application of fluorescence spectroscopy in food analysis is becoming increasingly attractive and has been demonstrated to be capable of classifying wine vinegars [11], fermented dairy products [12], camellia oil [13], cereals flours [14] and meat freshness [15,16] etc. Laser-induced fluorescence (LIF) can excite the characteristic fluorescence of the internal materials of leaves [17], which makes the detection more accurate [18], but there is still room for reduction in laser price and maintenance cost.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

Lin

Lu³

et al. 2019

Sensors

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A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.

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Evaluation of Data Mining Strategies for Classification of Black Tea Based on Image-Based Features

Cited by 32 publications

References 56 publications

Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis

Robust Classification of Tea Based on Multi-Channel LED-Induced Fluorescence and a Convolutional Neural Network

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