CN113670917B - Analysis method and system for green tea quality - Google Patents
Analysis method and system for green tea quality Download PDFInfo
- Publication number
- CN113670917B CN113670917B CN202110856425.2A CN202110856425A CN113670917B CN 113670917 B CN113670917 B CN 113670917B CN 202110856425 A CN202110856425 A CN 202110856425A CN 113670917 B CN113670917 B CN 113670917B
- Authority
- CN
- China
- Prior art keywords
- information
- quality
- green tea
- obtaining
- tea
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 244000269722 Thea sinensis Species 0.000 title claims abstract description 355
- 235000009569 green tea Nutrition 0.000 title claims abstract description 212
- 238000004458 analytical method Methods 0.000 title claims description 18
- 235000013616 tea Nutrition 0.000 claims abstract description 143
- 235000014347 soups Nutrition 0.000 claims abstract description 83
- 230000008859 change Effects 0.000 claims abstract description 50
- 238000000034 method Methods 0.000 claims abstract description 34
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 26
- 238000013210 evaluation model Methods 0.000 claims abstract description 22
- 238000007477 logistic regression Methods 0.000 claims description 25
- 238000012549 training Methods 0.000 claims description 24
- 238000003860 storage Methods 0.000 claims description 15
- 238000012937 correction Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 10
- 238000009313 farming Methods 0.000 claims description 6
- 238000013441 quality evaluation Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000001303 quality assessment method Methods 0.000 claims description 5
- 238000011534 incubation Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 2
- 238000012795 verification Methods 0.000 description 18
- 230000000694 effects Effects 0.000 description 17
- 238000003062 neural network model Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 9
- 238000004519 manufacturing process Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 238000011156 evaluation Methods 0.000 description 6
- 238000013528 artificial neural network Methods 0.000 description 5
- 230000006399 behavior Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 206010028980 Neoplasm Diseases 0.000 description 4
- 201000011510 cancer Diseases 0.000 description 4
- 235000019640 taste Nutrition 0.000 description 4
- 235000009024 Ceanothus sanguineus Nutrition 0.000 description 3
- 240000003553 Leptospermum scoparium Species 0.000 description 3
- 235000015459 Lycium barbarum Nutrition 0.000 description 3
- 238000003915 air pollution Methods 0.000 description 3
- 210000000988 bone and bone Anatomy 0.000 description 3
- 238000000855 fermentation Methods 0.000 description 3
- 230000004151 fermentation Effects 0.000 description 3
- 239000003205 fragrance Substances 0.000 description 3
- 206010061218 Inflammation Diseases 0.000 description 2
- 230000032683 aging Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 235000019606 astringent taste Nutrition 0.000 description 2
- 230000003467 diminishing effect Effects 0.000 description 2
- 210000004709 eyebrow Anatomy 0.000 description 2
- 235000019225 fermented tea Nutrition 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 239000012535 impurity Substances 0.000 description 2
- 230000004054 inflammatory process Effects 0.000 description 2
- 238000004898 kneading Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 239000005445 natural material Substances 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000012552 review Methods 0.000 description 2
- 239000013049 sediment Substances 0.000 description 2
- 238000007493 shaping process Methods 0.000 description 2
- 230000001954 sterilising effect Effects 0.000 description 2
- 238000004659 sterilization and disinfection Methods 0.000 description 2
- FFRBMBIXVSCUFS-UHFFFAOYSA-N 2,4-dinitro-1-naphthol Chemical compound C1=CC=C2C(O)=C([N+]([O-])=O)C=C([N+]([O-])=O)C2=C1 FFRBMBIXVSCUFS-UHFFFAOYSA-N 0.000 description 1
- 101100134058 Caenorhabditis elegans nth-1 gene Proteins 0.000 description 1
- 241000209507 Camellia Species 0.000 description 1
- 241001070941 Castanea Species 0.000 description 1
- 235000014036 Castanea Nutrition 0.000 description 1
- 244000067456 Chrysanthemum coronarium Species 0.000 description 1
- 235000007871 Chrysanthemum coronarium Nutrition 0.000 description 1
- 241000414067 Inonotus obliquus Species 0.000 description 1
- 241000282320 Panthera leo Species 0.000 description 1
- 244000184734 Pyrus japonica Species 0.000 description 1
- 239000011324 bead Substances 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000007664 blowing Methods 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000003925 brain function Effects 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 230000003749 cleanliness Effects 0.000 description 1
- 235000018597 common camellia Nutrition 0.000 description 1
- 235000009508 confectionery Nutrition 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 239000002932 luster Substances 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 150000008442 polyphenolic compounds Chemical class 0.000 description 1
- 235000013824 polyphenols Nutrition 0.000 description 1
- 238000009331 sowing Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Tea And Coffee (AREA)
Abstract
The invention discloses a method and a system for analyzing the quality of green tea, wherein the method comprises the following steps: obtaining first image information, wherein the first image information comprises image information of a first green tea appearance; obtaining second image information, wherein the second image information comprises image information of tea soup of the first green tea; inputting the first image information and the second image information into a green tea quality initial evaluation model to obtain tea soup color change curve trend information of the first green tea at a standard water temperature; obtaining first fitness information according to the trend information of the tea soup color change curve of the first green tea and the trend information of the standard tea soup color change curve at the standard water temperature; and obtaining third quality grading information according to the first quality grading information and the second quality grading information. Solves the technical problem that the quality of the green tea is identified to lack of professionality, which leads to the next best commercial behavior in the prior art.
Description
Technical Field
The invention relates to the field of quality analysis, in particular to a method and a system for analyzing the quality of green tea.
Background
In daily life, most people can drink the tea at leisure time, so that the tea has the health care function and can be used for shaping and nourishing the body. Green tea is the most abundant tea variety and yield in China, retains the natural substances of fresh leaves, has the characteristics of green leaves in clear soup and strong taste astringency, has special effects on aging resistance, cancer prevention, cancer resistance, sterilization, inflammation diminishing and the like, and is different from fermented tea and the like.
However, in the process of implementing the technical scheme of the invention in the embodiment of the application, the inventor of the application finds that at least the following technical problems exist in the above technology:
the prior art has the technical problem of identifying green tea quality with poor specificity, resulting in suboptimal commercial behavior.
Disclosure of Invention
According to the method and the system for analyzing the quality of the green tea, the technical problem that the quality of the green tea is identified to be lack of professionals in the prior art, so that the next best commercial behavior is caused is solved, and the technical effects that the quality analysis of the green tea is more accurate and strict, and then the quality tea evaluation principle is matched are achieved.
In view of the above problems, embodiments of the present application provide a method and a system for analyzing green tea quality.
In a first aspect, embodiments of the present application provide a method for analyzing quality of green tea, the method including: obtaining first image information, wherein the first image information comprises image information of a first green tea appearance; obtaining second image information, wherein the second image information comprises image information of tea soup of the first green tea; inputting the first image information and the second image information into a green tea quality primary evaluation model to obtain first quality grading information of the first green tea; obtaining trend information of a tea soup color change curve of the first green tea at the standard water temperature; obtaining trend information of a standard tea soup color change curve; obtaining first fitness information according to the trend information of the tea soup color change curve of the first green tea and the trend information of the standard tea soup color change curve at the standard water temperature; obtaining second quality scoring information of the first green tea according to the first fitness information; and obtaining third quality grading information according to the first quality grading information and the second quality grading information.
In another aspect, the present application also provides an analysis system for green tea quality, the system comprising: a first obtaining unit configured to obtain first image information including image information of a first green tea outline; a second obtaining unit for obtaining second image information including image information of tea soup of the first green tea; a third obtaining unit configured to input the first image information and the second image information into a green tea quality initial evaluation model, and obtain first quality score information of the first green tea; a fourth obtaining unit for obtaining trend information of the tea color change curve of the first green tea at the standard water temperature; the fifth obtaining unit is used for obtaining the standard tea soup color change curve trend information; the sixth obtaining unit is used for obtaining first fitness information according to the trend information of the tea soup color change curve of the first green tea and the trend information of the standard tea soup color change curve under the standard water temperature; a seventh obtaining unit, configured to obtain second quality score information of the first green tea according to the first fitness information; and an eighth obtaining unit for obtaining third quality score information according to the first quality score information and the second quality score information.
In a third aspect, the present invention provides a green tea quality analysis system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the first image information comprises image information of a first green tea shape, second image information is obtained, the second image information comprises image information of tea soup of the first green tea, the first image information and the second image information are input into a green tea quality primary evaluation model, first quality grading information of the first green tea is obtained, tea soup color change curve trend information of the first green tea under standard water temperature is obtained, first fitness information is obtained according to the tea soup color change curve trend information of the first green tea and the standard tea soup color change curve trend information under standard water temperature, and third quality grading information is obtained according to the first quality grading information and the second quality grading information. Thereby achieving the technical effects of more accurate and strict green tea quality analysis and further conforming to the tea evaluation principle with good quality.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification, so that the technical means of the present application can be more clearly understood, and the following specific embodiments of the present application are specifically described below.
Drawings
Fig. 1 is a flow chart of a method for analyzing green tea quality according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an analysis system for green tea quality according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a seventh obtaining unit 17, an eighth obtaining unit 18, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
According to the method and the system for analyzing the quality of the green tea, the technical problem that the quality of the green tea is identified to be lack of professionals in the prior art, so that the next best commercial behavior is caused is solved, and the technical effects that the quality analysis of the green tea is more accurate and strict, and then the quality tea evaluation principle is matched are achieved. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Summary of the application
In daily life, most people can drink the tea at leisure time, so that the tea has the health care function and can be used for shaping and nourishing the body. Green tea is the most abundant tea variety and yield in China, retains the natural substances of fresh leaves, has the characteristics of green leaves in clear soup and strong taste astringency, has special effects on aging resistance, cancer prevention, cancer resistance, sterilization, inflammation diminishing and the like, and is different from fermented tea and the like. However, the prior art has the technical problem of poor specificity in identifying the quality of green tea, resulting in a suboptimal commercial behavior.
Aiming at the technical problems, the technical scheme provided by the application has the following overall thought:
the embodiment of the application provides a green tea quality analysis method, which comprises the following steps: obtaining first image information, wherein the first image information comprises image information of a first green tea appearance; obtaining second image information, wherein the second image information comprises image information of tea soup of the first green tea; inputting the first image information and the second image information into a green tea quality primary evaluation model to obtain first quality grading information of the first green tea; obtaining trend information of a tea soup color change curve of the first green tea at a standard water temperature; obtaining trend information of a standard tea soup color change curve; obtaining first fitness information according to the trend information of the tea soup color change curve of the first green tea and the trend information of the standard tea soup color change curve at the standard water temperature; obtaining second quality scoring information of the first green tea according to the first fitness information; and obtaining third quality grading information according to the first quality grading information and the second quality grading information.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides a method for analyzing quality of green tea, where the method includes:
step S100: obtaining first image information, wherein the first image information comprises image information of a first green tea appearance;
specifically, the first image information is the image information of the first green tea shape, including the thickness, length, weight, size, tightness of the strips, whole crushing, uniformity, cleanliness, presence or absence of impurities and adulteration She Yingpian, and items such as a lower plate, dry and wet degree, body bone weight, and the like of the green tea leaves. The shape of green tea varies with tea, for example, the eyebrow tea is a tight and thin shape with the string, and is suitable for fishing, the bending part is one third of the upper tip, the lower tip is blunt, the body bone is heavy, the lower disc is less, the string is even, and the eyebrow tea is clean without impurities and is suitable for the old She Yingpian. The dragon well is flat and straight, the tip is not bent, and the body bone is tender. The Mao Feng is on the slender and slightly bent tip, the Japanese Yulu is on the slender needle and not bent, the Zhu cha is on the round bead, and the very tiny one is on the top, the shape can identify the way of the tea leaves, the way is on the original place, such as Huangshan Mao Feng, lion peak Longjing, liu an Guapian, and all the very noble.
Step S200: obtaining second image information, wherein the second image information comprises image information of tea soup of the first green tea;
specifically, the second image information is image information of the tea soup of the first green tea, and in the taste analysis of the green tea, the image information of the tea soup of the first green tea, that is, the tea color is an auxiliary means in the green tea identification process. The tea soup looks like a landscape, and is brighter and more pleasing to tea users, and the color of the soup generally means that the tea soup is observed in shade, clear and turbid, fresh and dark, whether free matters, sediment and the like exist. Generally, the tea soup is dark green and clear, the water color is dark yellow and turbid, or the free sediment is on the other hand, the inferior green tea soup color is yellow soup, the dark stir-frying time in the fixation is too long, the fixation leaves are not spread for cooling, the heavy press time in the kneading is too long, the stir-frying temperature is too low or the storage time is too long, the turbidity is not bright, the fresh leaves are provided with earth inclusions, the kneading is too heavy, the stir-frying time is too long, and certain varieties are deliberately pursued to have yellow and unclear soup color, and the other matters are taken into consideration.
Step S300: inputting the first image information and the second image information into a green tea quality primary evaluation model to obtain first quality grading information of the first green tea;
Further, the step S300 of the embodiment of the present application further includes:
step S310: inputting the first image information and the second image information into a green tea quality primary evaluation model, wherein the green tea quality primary evaluation model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets of training data comprises: the first image information, the second image information, and identification information for identifying a first result;
step S320: and obtaining output information in the green tea quality initial assessment model, wherein the output information comprises the first result, and the first result is first quality scoring information of the first green tea.
Specifically, the first training model is a Neural network model, that is, a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by a large number of simple processing units (called neurons) widely connected to each other, which reflects many basic features of human brain functions, and is a highly complex nonlinear power learning system. The neural network model is described based on a mathematical model of neurons. An artificial neural network (Artificial Neural Networks, ANN) is a description of the first order nature of the human brain system. In brief, it is a mathematical model. And inputting the first image information and the second image information into a neural network model through training of a large amount of training data, and outputting first quality grading information of the first green tea.
Furthermore, the training process is essentially a supervised learning process, each set of supervision data comprises the first image information, the second image information and identification information for identifying a first result, the first image information and the second image information are input into a neural network model, and the neural network model carries out continuous self-correction and adjustment according to the identification information for identifying the first result until the obtained first output result is consistent with the identification information, and the data supervised learning of the set is ended and the next data supervised learning is carried out; and when the output information of the neural network model reaches the preset accuracy rate/reaches a convergence state, ending the supervised learning process. Through the supervised learning of the neural network model, the neural network model is enabled to process the input information more accurately, and then the output first quality scoring information is enabled to be more reasonable and accurate, and further the technical effects of analyzing the quality of green tea more accurately and strictly and conforming to the quality-good tea evaluation principle are achieved.
Step S400: obtaining trend information of a tea soup color change curve of the first green tea at a standard water temperature;
Step S500: obtaining trend information of a standard tea soup color change curve;
specifically, the trend information of the tea soup color change curve of the first green tea is trend information of the tea soup color of the first green tea changing in the same direction within a certain period, and the trend information of the standard tea soup color change curve is standard trend information of the tea soup color of the first green tea changing in the same direction within the same period. The tea polyphenol in the tea soup is oxidized quickly when being contacted with air, so that the tea soup is easy to change color, therefore, the color of the tea soup is to be appreciated in time, and the color of the tea soup is mainly distinguished from the aspects of chromaticity, brightness, turbidity and the like, whether the color of the tea soup is dark or normal, and the degree of darkness, clarity or turbidity of the tea soup is distinguished. The color of the tea soup can be determined by color phase, brightness and chroma. The hue refers to the color type, the color of tea soup mainly changes between green and red, which is related to the fermentation degree of tea, the less the fermentation, the greenish the soup color, the more the fermentation, the redness of Shang Seyu, and the non-step changes of yellow-green, golden-yellow, orange-red and the like. Lightness refers to the degree of darkness of a color, which is related to the degree of baking of tea, and tea with little baking appears bright in color, and after baking, the color of the soup becomes darker due to the aggravation of the degree of baking. The chroma refers to the saturation degree of the color, which is related to the amount of soluble matters in the tea soup, and the more the soluble matters are dissolved out, the greater the consistency of the tea soup is, and the higher the chroma is as the color of the soup. Conversely, the less soluble, the lower the color of the tea soup.
Step S600: obtaining first fitness information according to the trend information of the tea soup color change curve of the first green tea and the trend information of the standard tea soup color change curve at the standard water temperature;
step S700: obtaining second quality scoring information of the first green tea according to the first fitness information;
specifically, the first fitness information is information of a matching part after the trend information of the tea soup color change curve of the first green tea is compared with the trend information of the standard tea soup color change curve under the standard water temperature, and the second quality grading information of the first green tea is grading of the color quality of the first green tea according to the first fitness information, for example, the tea soup prepared from fresh green tea is dark green in color, has faint scent, blue flower fragrance, cooked chestnut fragrance and the like, tastes sweet and refreshing, and the leaf bottom is bright. The tea soup of the old green tea has the characteristics of yellow and dark appearance, no luster and low fragrance, such as hot air blowing to the tea leaves, yellow and dry leaves in wet places, cool feeling smelling, and deep yellow color of the tea soup.
Step S800: and obtaining third quality grading information according to the first quality grading information and the second quality grading information.
Specifically, the third quality grading information is quality grading information obtained by comprehensively evaluating the first quality grading information and the second quality grading information, the tea quality is initially evaluated through the appearance of the tea and the color of the tea, and then the primary evaluation result is comprehensively graded by combining the trend of a specific tea color change curve, so that the tea quality of the green tea is comprehensively and accurately analyzed.
Further, the embodiment of the application further includes:
step S910: obtaining a place of origin of the first green tea;
step S920: acquiring altitude information of the producing area;
step S930: obtaining a incubation period of the first green tea;
step S940: acquiring air quality information, temperature information and humidity information of the producing area in the cultivation period;
step S950: acquiring a first influence factor according to the altitude information, the air quality information, the temperature information and the humidity information;
step S960: obtaining variety information of the first green tea;
step S970: and inputting the variety information of the first green tea and the first influencing factors into a traceability quality assessment model to obtain fourth quality scoring information of the first green tea.
Further, the step S970 of the embodiment of the present application further includes:
step S971: taking the variety information of the first green tea as an abscissa;
step S972: constructing a two-dimensional rectangular coordinate system by taking the first influence factor as an ordinate;
step S973: and constructing a logistic regression line in the two-dimensional rectangular coordinate system according to a logistic regression model, and constructing a traceability quality evaluation model, wherein one side of the logistic regression line represents the first output result, the other side of the logistic regression line represents the second output result, the first output result is qualified fourth quality grading information, and the second output result is unqualified fourth quality grading information.
Specifically, the first green tea is produced in the first green tea production area, and green tea production areas are wide, and green tea production areas are produced in Guizhou, henan, jiangsu, jiangxi, sichuan, zhejiang, shaanxi, hubei, hunan, guangxi, fujian and the like, and green tea production amounts are changed according to the climate characteristics of the current year. The elevation information of the producing area is that the vertical distance between the producing area of the first green tea and the sea level is the height difference between the producing area and the sea level, and is usually calculated by taking the average sea level as a standard, for example, the yaampsia japonica grows at the elevation of one thousand meters or more. The cultivation period of the first green tea is a growth period required from sowing to maturing of the first green tea, the length of the cultivation period is related to the characteristics of green tea varieties, the air quality information of the production place in the cultivation period is the air quality index or air pollution index information of the production place of the green tea, the air pollution degree and the air quality condition are expressed in a grading manner, and the air pollution degree, the air quality condition and the change trend are reflected. The temperature information and the humidity information of the cultivation period are average temperature and humidity information of the green tea production place in the cultivation period, and the less water vapor is contained in a certain volume of air at a certain temperature, the drier the air is; the more water vapor, the more humid the air. The first influencing factors are comprehensive influencing factors of the altitude information, the air quality information, the temperature information and the humidity information, for example, the optimal temperature for green tea growth is 20-30 ℃, the annual cultivation period of tea trees in most tea areas is about 8-9 months, the climate conditions of abundant rainfall, small annual temperature difference and large day-night temperature difference are suitable for the growth of various types of tea trees. The variety information of the first green tea is species classification of the green tea with the same commonality, such as Inonotus obliquus tea, west lake Longjing, emei snow bud, mei pool green bud, lanxinquehong tea, huiming tea, dongting Biluochun tea, zhongyue Xian tea, jian leaf, ma Bian Yunwu tea, japanese green tea, gu Shu purple tea, wuzi Xianhao, huangshan Mao Feng, liuan Guapian, xinyang Maojian, zhao Pocha, emei camellia, pingfeng green tea and the like.
Furthermore, the tracing quality evaluation model is a logistic regression model, the logistic regression model is a classification model in machine learning, the variety information of the first green tea is used as an abscissa, the first influencing factor is used as an ordinate, a coordinate system is built, a logistic regression line is obtained in the coordinate system based on the logistic regression model, one side of the logistic regression line represents the first output result, the first output result is qualified fourth quality scoring information, the other side of the logistic regression line represents the second output result, the second output result is unqualified fourth quality scoring information, the logistic regression line is controlled by the first position and the first angle, the first position and the first angle of the logistic regression line can be adjusted according to the variety information of the first green tea and the first influencing factor, the logistic regression line is further accurate, the obtained output result is further accurate, and the technical effects of comprehensively analyzing the quality of the green tea by combining the variety, the altitude, the growth air quality, the growth temperature and the humidity factor are achieved.
Further, step S970 in the embodiment of the present application further includes:
Step S974: obtaining tea farmer numbering information of the first green tea;
step S975: acquiring business assessment results of the tea farmers according to the serial number information of the tea farmers;
step S976: obtaining a first correction parameter according to the business assessment score;
step S977: and correcting the fourth quality score information according to the first correction parameter.
Specifically, the tea farmer numbering information of the first green tea is farmer numbering information for planting tea trees, picking tea leaves and making tea leaves, the business assessment results of the tea farmers are performance assessment results of planting business, picking business, sales business and the like of the tea farmers obtained according to the tea farmer numbering information, the first correction parameters are correction parameters obtained according to the business assessment results of the tea farmers, and correction processing is performed on the fourth quality scoring information according to the first correction parameters, namely, the influence effect of the business level capacity of the tea farmers on the quality of the green tea is considered.
Further, step S970 in the embodiment of the present application further includes:
step S978: obtaining a predetermined weight ratio;
step S979: and carrying out weighted calculation on the fourth quality grading information and the third quality grading information according to the preset weight ratio to obtain fifth quality grading information.
Specifically, the predetermined weight ratio is a set relative importance percentage value of the fourth quality score information and the third quality score information in overall quality evaluation, and the fifth quality score information is the green tea quality score information obtained by weighting calculation of the fourth quality score information and the third quality score information by the predetermined weight ratio. The technical effect that the green tea quality is more professional and accurate through comprehensively analyzing the appearance, the color and the color change curve trend of the tea and combining the green tea variety, the growth altitude, the growth air quality, the growth temperature, the humidity factor and the tea farmer performance level is achieved.
Further, step S977 in the embodiment of the present application further includes:
step S9771: acquiring first identification information, and storing the air quality information into the first identification information;
step S9772: obtaining second identification information, and storing the temperature information into the second identification information;
step S9773: obtaining third identification information, and storing the humidity information into the third identification information;
step S9774: obtaining fourth identification information, and storing the tea farming number information into the fourth identification information;
Step S9775: obtaining fifth identification information;
step S9776: and integrating the first identification information, the second identification information, the third identification information and the fourth identification information into the fifth identification information through distributed storage.
Specifically, the first identification information includes the air quality information of the place of origin, the second identification information includes the temperature information of the place of origin, the third identification information includes the humidity information of the place of origin, the fourth identification information includes the tea farming number information, the fifth identification information is integrated by distributed storage, the distributed storage is a data storage technology, disk space on each machine in the enterprise is used through a network, and the scattered storage resources form a virtual storage device, the data are stored in all corners of the enterprise in a scattered mode, and the information is stored in one identification code in a block chain mode, so that the effects of improving source irrecoverable and safety are achieved.
Further, step S9776 of the embodiment of the present application further includes:
step S97761: obtaining first identification information, second identification information and up to N identification information according to the identification information of the first green tea;
step S97762: generating a first verification code according to the first identification information, wherein the first verification code is in one-to-one correspondence with the first identification information;
step S97763: generating a second verification code according to the second identification information and the first verification code; and so on, generating an Nth verification code according to the Nth identification information and the N-1 th verification code, wherein N is a natural number larger than 1;
step S97764: and respectively copying and storing all the identification information and the verification code on M pieces of equipment, wherein M is a natural number larger than 1.
Specifically, the block chain storage is to respectively correspond to one verification code for each piece of identification information of the first green tea, wherein a first verification code is generated according to the first identification information, and the first verification code is in one-to-one correspondence with the first identification information; generating a second verification code according to the second identification information and the first verification code; similarly, the nth identification information and the nth-1 verification code generate an nth verification code, wherein N is a natural number greater than 1. And respectively copying and storing all the identification information and the verification code on M pieces of equipment, wherein M is a natural number larger than 1. The first identification information and the first verification code are stored on one device as a first storage unit, the second identification information and the second verification code are stored on one device as a second storage unit, the N identification information and the N verification code are stored on one device as an M storage unit, when the identification information of the green tea needs to be called, each next node receives the data stored by the previous node, checks and stores the data through a common identification mechanism, and concatenates each storage unit through a hash function, so that the training data is not easy to lose and damage, and the block chain technology is also called a distributed account book technology, and is an emerging technology in which a plurality of computing devices participate in accounting together to maintain a complete distributed database together. The blockchain technology has the characteristics of decentralization, disclosure transparency, capability of participating in database recording by each computing device and capability of rapidly synchronizing data among the computing devices, so that the blockchain technology is widely applied in a plurality of fields. The training data is encrypted through the logic of the blockchain, so that the safety of the training data is guaranteed, the training data is stored in a plurality of devices, and the data stored in the plurality of devices is processed through a consensus mechanism, namely a few obeys a majority. When one or more pieces of equipment are tampered, as long as the number of the equipment for storing correct data is larger than that of tampered equipment, the obtained identification information result of the green tea is still correct, so that accurate and reliable data can be ensured to be obtained, the accuracy of the output identification information is further effectively ensured, and the technical effects of more accurate and precise quality analysis of the green tea and compliance with the quality-good tea review principle are achieved.
In summary, the method and system for analyzing the quality of green tea provided by the embodiments of the present application have the following technical effects:
1. the first image information comprises image information of a first green tea shape, second image information is obtained, the second image information comprises image information of tea soup of the first green tea, the first image information and the second image information are input into a green tea quality initial evaluation model, first quality grading information of the first green tea is obtained, tea soup color change curve trend information of the first green tea under standard water temperature is obtained, first fitness information is obtained according to the tea soup color change curve trend information of the first green tea and the standard tea soup color change curve trend information under the standard water temperature, and third quality grading information is obtained according to the first quality grading information and the second quality grading information. Thereby achieving the technical effects of more accurate and strict green tea quality analysis and further conforming to the tea evaluation principle with good quality.
2. The first image information and the second image information are input into the neural network model, so that the output first quality scoring information is more reasonable and accurate, and the technical effect of analyzing the green tea quality through multiple factors and obtaining more accurate and strict analysis results is achieved.
3. The technical effect of enabling the green tea quality analysis to be more professional and accurate is achieved by adopting the method of comprehensively analyzing and evaluating the appearance, the color and the color change curve of the tea and combining the green tea variety, the growth altitude, the growth air quality, the growth temperature, the humidity factor and the tea farmer performance level.
Example two
Based on the same inventive concept as the method for analyzing the quality of green tea in the foregoing embodiment, the present invention further provides a system for analyzing the quality of green tea, as shown in fig. 2, the system includes:
a first obtaining unit 11, the first obtaining unit 11 being configured to obtain first image information including image information of a first green tea outline;
a second obtaining unit 12 for obtaining second image information including image information of tea soup of the first green tea;
a third obtaining unit 13, wherein the third obtaining unit 13 is configured to input the first image information and the second image information into a green tea quality primary evaluation model, and obtain first quality score information of the first green tea;
a fourth obtaining unit 14, wherein the fourth obtaining unit 14 is used for obtaining trend information of a tea soup color change curve of the first green tea at a standard water temperature;
A fifth obtaining unit 15, wherein the fifth obtaining unit 15 is used for obtaining trend information of a standard tea soup color change curve;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to obtain first fitness information according to the trend information of the tea soup color change curve of the first green tea and the trend information of the standard tea soup color change curve at the standard water temperature;
a seventh obtaining unit 17, where the seventh obtaining unit 17 is configured to obtain second quality score information of the first green tea according to the first fitness information;
an eighth obtaining unit 18, where the eighth obtaining unit 18 is configured to obtain third quality score information according to the first quality score information and the second quality score information.
Further, the system further comprises:
the first input unit is used for inputting the first image information and the second image information into a green tea quality primary evaluation model, the green tea quality primary evaluation model is obtained through training of a plurality of sets of training data, and each set of training data in the plurality of sets of training data comprises: the first image information, the second image information, and identification information for identifying a first result;
A ninth obtaining unit configured to obtain output information in the green tea quality primary evaluation model, wherein the output information includes the first result, and wherein the first result is first quality score information of the first green tea.
Further, the system further comprises:
a tenth obtaining unit for obtaining a place of origin of the first green tea;
an eleventh obtaining unit for obtaining altitude information of the place of origin;
a twelfth obtaining unit for obtaining a cultivation period of the first green tea;
a thirteenth obtaining unit for obtaining air quality information, temperature information, and humidity information of the place of origin during the incubation period;
a fourteenth obtaining unit for obtaining a first influencing factor from the altitude information and the air quality information, temperature information, and humidity information;
a fifteenth obtaining unit for obtaining variety information of the first green tea;
a sixteenth obtaining unit, configured to input the variety information of the first green tea and the first influencing factor into a traceability quality assessment model, and obtain fourth quality score information of the first green tea.
Further, the system further comprises:
a first as unit for taking the variety information of the first green tea as an abscissa;
the second serving as a unit is used for constructing a two-dimensional rectangular coordinate system by taking the first influence factor as an ordinate;
the first construction unit is used for constructing a logistic regression line in the two-dimensional rectangular coordinate system according to a logistic regression model and constructing a traceability quality evaluation model, wherein one side of the logistic regression line represents the first output result, the other side of the logistic regression line represents the second output result, the first output result is qualified fourth quality grading information, and the second output result is unqualified fourth quality grading information.
Further, the system further comprises:
a seventeenth obtaining unit for obtaining tea farming number information of the first green tea;
an eighteenth obtaining unit, configured to obtain a business assessment score of the tea farmer according to the tea farmer numbering information;
a nineteenth obtaining unit, configured to obtain a first correction parameter according to the business assessment score;
And the twentieth obtaining unit is used for correcting the fourth quality score information according to the first correction parameter.
Further, the system further comprises:
a twenty-first obtaining unit for obtaining a predetermined weight ratio;
and a twenty-second obtaining unit, configured to perform weighted calculation on the fourth quality score information and the third quality score information according to the predetermined weight ratio, and obtain fifth quality score information.
Further, the system further comprises:
a twenty-third obtaining unit configured to obtain first identification information, and store the air quality information into the first identification information;
a twenty-fourth obtaining unit configured to obtain second identification information, and store the temperature information into the second identification information;
a twenty-fifth obtaining unit configured to obtain third identification information, and store the humidity information into the third identification information;
a twenty-sixth obtaining unit, configured to obtain fourth identification information, and store the tea farming number information into the fourth identification information;
A twenty-seventh obtaining unit configured to obtain fifth identification information;
a twenty-eighth obtaining unit configured to integrate the first identification information, the second identification information, the third identification information, and the fourth identification information into the fifth identification information by distributed storage.
The various modifications and embodiments of the method for analyzing the quality of green tea in the first embodiment of fig. 1 are equally applicable to the system for analyzing the quality of green tea in the present embodiment, and those skilled in the art will be aware of the implementation of the system for analyzing the quality of green tea in the present embodiment through the detailed description of the method for analyzing the quality of green tea in the foregoing, so that the detailed description will not be repeated here for brevity.
Exemplary electronic device
An electronic device of an embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the method for analyzing the quality of green tea according to the above embodiments, the present invention further provides a system for analyzing the quality of green tea, on which a computer program is stored, which program, when executed by a processor, implements the steps of any one of the methods for analyzing the quality of green tea described above.
Where in FIG. 3 a bus architecture (represented by bus 300), bus 300 may comprise any number of interconnected buses and bridges, with bus 300 linking together various circuits, including one or more processors, represented by processor 302, and memory, represented by memory 304. Bus 300 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., as are well known in the art and, therefore, will not be further described herein. Bus interface 306 provides an interface between bus 300 and receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e. a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store data used by the processor 302 in performing operations.
The embodiment of the invention provides a method for analyzing the quality of green tea, which comprises the following steps: obtaining first image information, wherein the first image information comprises image information of a first green tea appearance; obtaining second image information, wherein the second image information comprises image information of tea soup of the first green tea; inputting the first image information and the second image information into a green tea quality primary evaluation model to obtain first quality grading information of the first green tea; obtaining trend information of a tea soup color change curve of the first green tea at a standard water temperature; obtaining trend information of a standard tea soup color change curve; obtaining first fitness information according to the tea soup color change curve trend information of the first green tea and the standard tea soup color change curve trend information under the standard water temperature; obtaining second quality scoring information of the first green tea according to the first fitness information; and obtaining third quality grading information according to the first quality grading information and the second quality grading information. The technical problem that the quality of the green tea is identified in the prior art and the poor professional performance leads to the next best commercial behavior is solved, and the technical effects of more accurate and strict analysis of the quality of the green tea and fitting the tea review principle with excellent quality are achieved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (4)
1. A method of analyzing the quality of green tea, wherein the method comprises:
obtaining first image information, wherein the first image information comprises image information of a first green tea appearance;
obtaining second image information, wherein the second image information comprises image information of tea soup of the first green tea;
inputting the first image information and the second image information into a green tea quality primary evaluation model to obtain first quality grading information of the first green tea;
obtaining trend information of a tea soup color change curve of the first green tea at a standard water temperature;
obtaining trend information of a standard tea soup color change curve;
obtaining first fitness information according to the trend information of the tea soup color change curve of the first green tea and the trend information of the standard tea soup color change curve at the standard water temperature;
obtaining second quality scoring information of the first green tea according to the first fitness information;
Obtaining third quality scoring information according to the first quality scoring information and the second quality scoring information;
obtaining a place of origin of the first green tea;
acquiring altitude information of the producing area;
obtaining a incubation period of the first green tea;
acquiring air quality information, temperature information and humidity information of the producing area in the cultivation period;
acquiring a first influence factor according to the altitude information, the air quality information, the temperature information and the humidity information;
obtaining variety information of the first green tea;
inputting the variety information of the first green tea and the first influencing factors into a traceability quality assessment model to obtain fourth quality scoring information of the first green tea;
the inputting the variety information of the first green tea and the first influencing factors into a traceability quality assessment model to obtain fourth quality scoring information of the first green tea includes:
taking the variety information of the first green tea as an abscissa;
constructing a two-dimensional rectangular coordinate system by taking the first influence factor as an ordinate;
constructing a logistic regression line in the two-dimensional rectangular coordinate system according to a logistic regression model, and constructing a traceability quality evaluation model, wherein one side of the logistic regression line represents a first output result, the other side of the logistic regression line represents a second output result, the first output result is qualified fourth quality grading information, and the second output result is unqualified fourth quality grading information;
The inputting the first image information and the second image information into a green tea quality initial evaluation model to obtain first quality scoring information of the first green tea comprises the following steps:
inputting the first image information and the second image information into a green tea quality primary evaluation model, wherein the green tea quality primary evaluation model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets of training data comprises: the first image information, the second image information, and identification information for identifying a first result;
obtaining output information in the green tea quality initial assessment model, wherein the output information comprises the first result, and the first result is first quality scoring information of the first green tea;
obtaining tea farmer numbering information of the first green tea;
acquiring business assessment results of the tea farmers according to the serial number information of the tea farmers;
obtaining a first correction parameter according to the business assessment score;
correcting the fourth quality scoring information according to the first correction parameters;
obtaining a predetermined weight ratio;
and carrying out weighted calculation on the fourth quality grading information and the third quality grading information according to the preset weight ratio to obtain fifth quality grading information.
2. The method of claim 1, wherein the method comprises:
acquiring first identification information, and storing the air quality information into the first identification information;
obtaining second identification information, and storing the temperature information into the second identification information;
obtaining third identification information, and storing the humidity information into the third identification information;
obtaining fourth identification information, and storing the tea farming number information into the fourth identification information;
obtaining fifth identification information;
and integrating the first identification information, the second identification information, the third identification information and the fourth identification information into the fifth identification information through distributed storage.
3. An analysis system for green tea quality, wherein the system comprises:
a first obtaining unit configured to obtain first image information including image information of a first green tea outline;
a second obtaining unit for obtaining second image information including image information of tea soup of the first green tea;
a third obtaining unit configured to input the first image information and the second image information into a green tea quality initial evaluation model, and obtain first quality score information of the first green tea;
A fourth obtaining unit for obtaining trend information of a tea color change curve of the first green tea at a standard water temperature;
a fifth obtaining unit for obtaining trend information of the standard tea color change curve;
the sixth obtaining unit is used for obtaining first fitness information according to the trend information of the tea soup color change curve of the first green tea and the trend information of the standard tea soup color change curve under the standard water temperature;
a seventh obtaining unit, configured to obtain second quality score information of the first green tea according to the first fitness information;
an eighth obtaining unit configured to obtain third quality score information according to the first quality score information and the second quality score information;
a tenth obtaining unit for obtaining a place of origin of the first green tea;
an eleventh obtaining unit for obtaining altitude information of the place of origin;
a twelfth obtaining unit for obtaining a cultivation period of the first green tea;
A thirteenth obtaining unit for obtaining air quality information, temperature information, and humidity information of the place of origin during the incubation period;
a fourteenth obtaining unit for obtaining a first influencing factor from the altitude information and the air quality information, temperature information, and humidity information;
a fifteenth obtaining unit for obtaining variety information of the first green tea;
a sixteenth obtaining unit, configured to input the variety information of the first green tea and the first influencing factor into a traceable quality assessment model, and obtain fourth quality score information of the first green tea;
a first as unit for taking the variety information of the first green tea as an abscissa;
the second serving as a unit is used for constructing a two-dimensional rectangular coordinate system by taking the first influence factor as an ordinate;
the first construction unit is used for constructing a logistic regression line in the two-dimensional rectangular coordinate system according to a logistic regression model and constructing a traceability quality evaluation model, wherein one side of the logistic regression line represents a first output result, the other side of the logistic regression line represents a second output result, the first output result is qualified fourth quality grading information, and the second output result is unqualified fourth quality grading information;
The first input unit is used for inputting the first image information and the second image information into a green tea quality primary evaluation model, the green tea quality primary evaluation model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets of training data comprises: the first image information, the second image information, and identification information for identifying a first result;
a ninth obtaining unit configured to obtain output information in the green tea quality primary evaluation model, wherein the output information includes the first result, wherein the first result is first quality score information of the first green tea;
a seventeenth obtaining unit for obtaining tea farming number information of the first green tea;
an eighteenth obtaining unit, configured to obtain a business assessment score of the tea farmer according to the serial number information of the tea farmer;
a nineteenth obtaining unit, configured to obtain a first correction parameter according to the business assessment score;
a twentieth obtaining unit, configured to correct the fourth quality score information according to the first correction parameter;
A twenty-first obtaining unit for obtaining a predetermined weight ratio;
and a twenty-second obtaining unit, configured to perform weighted calculation on the fourth quality score information and the third quality score information according to the predetermined weight ratio, and obtain fifth quality score information.
4. A green tea quality analysis system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method of any one of claims 1-2 when the program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110856425.2A CN113670917B (en) | 2021-07-28 | 2021-07-28 | Analysis method and system for green tea quality |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110856425.2A CN113670917B (en) | 2021-07-28 | 2021-07-28 | Analysis method and system for green tea quality |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113670917A CN113670917A (en) | 2021-11-19 |
| CN113670917B true CN113670917B (en) | 2023-12-19 |
Family
ID=78540406
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110856425.2A Active CN113670917B (en) | 2021-07-28 | 2021-07-28 | Analysis method and system for green tea quality |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113670917B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114965907B (en) * | 2022-04-02 | 2023-04-07 | 浙江大学 | Rapid tea grade detection method based on turbidity difference |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102279160A (en) * | 2011-04-08 | 2011-12-14 | 浙江经贸职业技术学院 | Method for evaluating quality of famous green tea |
| CN102539326A (en) * | 2012-01-13 | 2012-07-04 | 江苏大学 | Method for carrying out quantitative evaluation on soup hue quality of tea |
| CN103719308A (en) * | 2014-01-26 | 2014-04-16 | 贵州省茶叶研究所 | Green tea withering method |
| CN104814170A (en) * | 2015-03-16 | 2015-08-05 | 杭州千岛湖丰凯实业有限公司 | Continuous production line based on processing of black tea, green tea, yellow tea and white tea, and method thereof |
| CN106525849A (en) * | 2016-11-02 | 2017-03-22 | 江苏大学 | Tea intelligent blending method and system |
| CN108287161A (en) * | 2017-01-09 | 2018-07-17 | 中国计量大学 | A kind of Intelligent tea organoleptic evaluation method and system |
| CN108323584A (en) * | 2017-11-01 | 2018-07-27 | 宣恩县伍台昌臣茶业有限公司 | A method of improving No. 10 excellent green tea shape green qualities of Hubei Province tea |
| CN112884735A (en) * | 2021-02-09 | 2021-06-01 | 浙江农林大学 | Evaluation method for color quality of green tea soup |
| CN113029975A (en) * | 2021-04-06 | 2021-06-25 | 中国计量大学 | Method for identifying quality of freeze injury tea |
-
2021
- 2021-07-28 CN CN202110856425.2A patent/CN113670917B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102279160A (en) * | 2011-04-08 | 2011-12-14 | 浙江经贸职业技术学院 | Method for evaluating quality of famous green tea |
| CN102539326A (en) * | 2012-01-13 | 2012-07-04 | 江苏大学 | Method for carrying out quantitative evaluation on soup hue quality of tea |
| CN103719308A (en) * | 2014-01-26 | 2014-04-16 | 贵州省茶叶研究所 | Green tea withering method |
| CN104814170A (en) * | 2015-03-16 | 2015-08-05 | 杭州千岛湖丰凯实业有限公司 | Continuous production line based on processing of black tea, green tea, yellow tea and white tea, and method thereof |
| CN106525849A (en) * | 2016-11-02 | 2017-03-22 | 江苏大学 | Tea intelligent blending method and system |
| CN108287161A (en) * | 2017-01-09 | 2018-07-17 | 中国计量大学 | A kind of Intelligent tea organoleptic evaluation method and system |
| CN108323584A (en) * | 2017-11-01 | 2018-07-27 | 宣恩县伍台昌臣茶业有限公司 | A method of improving No. 10 excellent green tea shape green qualities of Hubei Province tea |
| CN112884735A (en) * | 2021-02-09 | 2021-06-01 | 浙江农林大学 | Evaluation method for color quality of green tea soup |
| CN113029975A (en) * | 2021-04-06 | 2021-06-25 | 中国计量大学 | Method for identifying quality of freeze injury tea |
Non-Patent Citations (2)
| Title |
|---|
| 张渤等.《武夷茶种》.复旦大学出版社,2020,(第1版),第125-131页. * |
| 绿茶茶水放置过程中主要呈味物质动态变化研究;张霞等;《长江大学学报 （自然科学版）》;第12卷(第21期);第50-54, 58页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113670917A (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106845497B (en) | Corn early-stage image drought identification method based on multi-feature fusion | |
| Petropoulos et al. | Fuzzy logic tool for wine quality classification | |
| CN113670917B (en) | Analysis method and system for green tea quality | |
| CN112001242B (en) | Intelligent gardening management method and device | |
| CN109444355A (en) | Grape organoleptic quality evaluations system and evaluation method | |
| CN107241403A (en) | Method for pushing and pusher, server, equipment and readable storage medium storing program for executing | |
| CN106846334A (en) | Field corn plant recognition methods based on Support Vector data description | |
| CN111310639B (en) | Evergreen artificial forest remote sensing identification method and evergreen artificial forest growth remote sensing monitoring method | |
| CN112836623A (en) | Facility tomato farming decision auxiliary method and device | |
| CN114219227A (en) | Method and system for precise fertilization decision and plot level display | |
| Kangotra et al. | Economic viability of button mushroom cultivation in Himachal Pradesh, India | |
| CN109493335A (en) | Piece type coefficient characterizing method, system, computer storage medium and the equipment of piece cigarette | |
| Letyago et al. | Quality of Varieties of Spring Soft Wheat Grain and their Ranking for Baking Strength | |
| CN116762676A (en) | Dynamic irrigation control method and system based on crop phenotype image | |
| CN113358829B (en) | Evaluation method and device for quality of black tea | |
| CN112084462B (en) | Crop nitrogen nutrition index estimation model evaluation method based on digital image | |
| CN115909063A (en) | Medium-resolution rice extraction method and system | |
| CN107624569A (en) | Utilize method of the nitrogen paddy than screening Taste quality and the double high japonica rice varieties of protein content | |
| CN112818810A (en) | Kumquat intelligent sorting method and device based on big data | |
| Vu et al. | Varietal and trait preferences for boiled sweetpotato in urban Hanoi and implications for breeding | |
| CN108549897A (en) | A kind of method and intelligence control system of proportion of crop planting and sorting linkage | |
| CN113033075B (en) | Storage and preservation method and device for kumquat fruits | |
| CN117235322B (en) | Crop salt-tolerant high-product seed screening method and device, electronic equipment and storage medium | |
| CN113034623A (en) | Preparation method and device of uncaria medicine-entering part | |
| CN107894988A (en) | A kind of hunger sign in plant detection method, electronic equipment, storage medium and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |