Method for identifying and evaluating the quality of machine-picked fresh tea leaves of a tea tree variety

Abstract

This invention relates to the field of tea tree testing technology and discloses a method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties. This invention has the following advantages and effects: It measures the main quality indicators of machine-harvested fresh leaves of different tea varieties, performs dimensionless processing and principal component analysis on them; based on the standardized values ​​of the quality indicators and the eigenvectors of each principal component's quality indicators, and using the variance contribution rate of the principal components as weighting coefficients, it establishes a quality identification and evaluation model for machine-harvested fresh leaves of tea varieties; it compares the quality identification and evaluation scores of machine-harvested fresh leaves of different tea varieties, with higher scores indicating higher quality. This method effectively avoids the limitations of using a single indicator or very few indicators to evaluate the quality of machine-harvested fresh leaves of tea trees, making the identification and evaluation results more scientific and accurate.

Description

Technical Field

[0001] This invention relates to the field of tea tree testing technology, and in particular to a method for identifying and evaluating the quality of machine-harvested fresh leaves of tea tree varieties. Background Technology

[0002] The tea industry is a labor-intensive industry, with tea picking accounting for over 60% of the annual labor costs for tea garden management and approximately 45% of the production and management expenses. With the continuous improvement of tea production levels and the shift of rural labor markets, the problems of "labor shortages, high labor costs, and difficulty in tea picking" have become increasingly prominent in tea production, seriously hindering the healthy and high-quality development of the tea industry. Mechanized harvesting of fresh tea leaves is an effective technical means to reduce the tea industry's over-reliance on labor. However, because mechanical harvesting lacks the ability to identify new shoots and buds, the quality of machine-harvested fresh leaves exhibits significant varietal specificity due to differences in bud and leaf morphology and biological characteristics among different tea varieties. Therefore, selecting or breeding tea varieties suitable for machine harvesting is a crucial prerequisite for achieving high-quality machine-harvested fresh leaves.

[0003] The quality of machine-harvested fresh tea leaves is a direct indicator of the suitability of machine harvesting for tea trees, and is mainly determined based on the comprehensive mechanical composition of the harvested leaves. Currently, most studies judge the quality of machine-harvested fresh tea leaves solely based on the proportion of single components such as the standard bud and leaf rate and the high-quality rate. However, the mechanical composition of machine-harvested fresh tea leaves is complex and diverse, making it difficult to comprehensively and accurately reflect the quality of machine-harvested fresh tea leaves. This, in turn, affects the efficiency of screening and breeding tea varieties suitable for machine harvesting, as well as the accurate determination of the appropriate time for machine harvesting of tea varieties. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties, thereby improving the accuracy of the quality identification and evaluation of machine-harvested fresh leaves of tea varieties and providing a new method for screening and breeding tea varieties suitable for machine harvesting and for determining the appropriate time for machine harvesting of tea varieties.

[0005] The above-mentioned technical objective of this invention is achieved through the following technical solution: a method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties, comprising the following steps:

[0006] S1: When the proportion of one bud and two leaves or more (one bud and two leaves, one bud and three leaves, ..., paired leaves) on the surface of the tea tree is 60%, machine harvesting is carried out on different tea tree varieties and the machine-harvested fresh leaves are collected.

[0007] S2: Determine the main quality indicators of machine-harvested fresh leaves of different tea varieties;

[0008] S3: Dimensionless processing is performed on the quality indicators of machine-harvested fresh leaves obtained in step S2;

[0009] S4: Perform principal component analysis on the quality indicators of machine-harvested fresh leaves obtained in step S2, and obtain the variance contribution rate (α) of the first three principal components.k ) and the eigenvectors (λ) of each machine-harvested fresh leaf quality index kj );

[0010] S5: Based on the standardized values ​​of the quality indicators of machine-harvested fresh leaves and the eigenvectors of the quality indicators of machine-harvested fresh leaves of each principal component, establish the linear regression equations of the first three principal components, and use the variance contribution rate of the first three principal components as weighting coefficients to establish a quality identification and evaluation model for machine-harvested fresh leaves of tea varieties.

[0011] S6: Compare the quality evaluation scores of machine-harvested fresh leaves of different tea varieties obtained in step S5. The higher the score, the higher the quality of the machine-harvested fresh leaves.

[0012] By employing the above technical solution, this invention measures the main quality indicators of machine-harvested fresh leaves of different tea varieties, performs dimensionless processing and principal component analysis on them, and establishes a quality identification and evaluation model for machine-harvested fresh leaves of tea varieties based on the standardized values ​​of the quality indicators and the eigenvectors of each principal component's quality indicators, using the variance contribution rate of the principal components as weighting coefficients. The quality identification and evaluation scores of machine-harvested fresh leaves of different tea varieties are compared; the higher the score, the higher the quality of the machine-harvested fresh leaves. This method effectively avoids the limitations of using a single indicator or very few indicators to evaluate the quality of machine-harvested fresh leaves of tea, making the identification and evaluation results more scientific and accurate.

[0013] The present invention is further configured such that the main quality indicators of machine-harvested fresh leaves in step S2 are: single bud rate, one bud and one leaf rate, one bud and two leaves rate, one bud and three leaves rate, one bud and three or more leaves rate, single leaf rate, old stem and old leaf rate, broken leaf rate, damaged bud and leaf rate, intact bud and leaf rate, standard bud and leaf rate, high-quality rate, and discard rate.

[0014] A further provision of the present invention is as follows: In step S2, the main quality indicators of machine-harvested fresh leaves are determined by accurately separating the mechanical composition of nine main types of fresh leaves: single bud, one bud and one leaf, one bud and two leaves, one bud and three leaves, one bud and more than one leaf, single leaf, old stem and old leaf, broken pieces, and damaged buds and leaves, and weighing them. The quality indicators of each type of machine-harvested fresh leaf are calculated. Among them, the rate of intact buds and leaves is the sum of the proportions of single buds, one bud and one leaf, one bud and two leaves, one bud and three leaves, and one bud and more than one leaf; the rate of standard buds and leaves is the sum of the proportions of one bud and two leaves and one bud and three leaves; the rate of high-quality buds and leaves is the sum of the proportions of single buds, one bud and one leaf, and one bud and two leaves; and the rate of waste is the sum of the proportions of old stem and old leaf and broken pieces.

[0015] A further provision of the present invention is that, in step S3, the dimensionless processing is performed using the "Z-score standardization" method based on the mean and standard deviation of the original data, i.e. in Let σ be the average value of the j-th indicator. j Let be the standard deviation of the j-th indicator.

[0016] A further provision of the present invention is that, in step S5, the linear regression equations of each principal component are:

[0017] A further provision of the present invention is that, in step S5, the quality identification and evaluation model for machine-harvested fresh leaves of tea varieties is:

[0018] A further setting of the present invention is as follows: In step S6, the tea tree varieties are divided into four grades: I, II, III, and IV. Grade I is a high-quality variety of machine-harvested fresh leaves, Grade II is a medium-quality variety of machine-harvested fresh leaves, Grade III is a poor-quality variety of machine-harvested fresh leaves, and Grade IV is a very poor-quality variety of machine-harvested fresh leaves. The quality assessment score for machine-harvested fresh leaves is S. i ≥0.57 is Level I, 0≤S i <0.57 indicates Level II; -0.57≤S i <0 indicates Level III, S i <-0.57 indicates Level IV.

[0019] A further feature of the present invention is that in step S1, the canopy cultivation method and field management measures for different varieties of tea trees are the same.

[0020] The beneficial effects of this invention are: by performing dimensionless processing on the quality indicators of machine-harvested fresh leaves and establishing a comprehensive evaluation model with multiple indicators using principal component analysis, this invention avoids the limitations of using a single indicator or very few indicators to evaluate the quality of machine-harvested fresh leaves of tea trees, making the identification and evaluation results more scientific and accurate. Attached Figure Description

[0021] Figure 1 This invention classifies the main mechanical components of machine-harvested fresh tea leaves. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] Example 1

[0024] The quality of machine-harvested fresh leaves of nine tea tree varieties (lines), including Echa 201, Echa No. 1, Fuding Dabaicha, Longjing 43, Zhongcha 108, Echa No. 7, Jin Guanyin, 07-7-72, and 07-7-74, was evaluated. Among them, 07-7-72 and 07-7-74 were developed by the research group of the inventor's unit, and the rest were tea tree varieties that had already been developed.

[0025] The specific steps are as follows:

[0026] S1: When the proportion of one bud and two or more leaves (one bud and two leaves, one bud and three leaves, ..., paired leaves) on the canopy surface of each tea variety (line) is 60%, machine harvesting is carried out and the machine-harvested fresh leaves are collected. The canopy surface cultivation method and field management measures are consistent for each variety (line).

[0027] S2: Randomly weigh 500g of machine-harvested fresh leaves, accurately separate them into 9 main mechanical components: single bud, one bud and one leaf, one bud and two leaves, one bud and three leaves, one bud and more than three leaves, single leaf, old stems and old leaves, broken pieces, and damaged buds and leaves, and weigh them. Figure 1 The study calculated 13 key quality indicators for machine-harvested fresh leaves, including single bud rate, one bud and one leaf rate, one bud and two leaves rate, one bud and three leaves rate, one bud and three or more leaves rate, single leaf rate, old stem and old leaf rate, broken leaf rate, damaged bud and leaf rate, intact bud and leaf rate, standard bud and leaf rate, high-quality rate, and discard rate. The intact bud and leaf rate is the sum of the proportions of single buds, one bud and one leaf, one bud and two leaves, one bud and three leaves, and one bud and three or more leaves; the standard bud and leaf rate is the sum of the proportions of one bud and two leaves and one bud and three leaves; the high-quality bud and leaf rate is the sum of the proportions of single buds, one bud and one leaf, and one bud and two leaves; and the discard rate is the sum of the proportions of old stem and old leaves and broken leaves.

[0028] S3: Based on the mean and standard deviation of the original model data, dimensionless processing was performed on the main machine-harvested fresh leaf quality indicators of 10 tea varieties (lines) to obtain standardized values ​​for the main machine-harvested fresh leaf quality indicators of each variety (line). The dimensionless processing was performed using the "Z-score standardization" method based on the mean and standard deviation of the original data, i.e. in Let σ be the average value of the j-th indicator. j Let be the standard deviation of the j-th indicator.

[0029] S4: Perform principal component analysis on the quality indicators of machine-harvested fresh leaves obtained in step S2, and obtain the variance contribution rate (α) of the first three principal components. k ) and the eigenvectors (λ) of each machine-harvested fresh leaf quality index kj );

[0030] S5: Based on the standardized values ​​of the quality indicators of machine-harvested fresh leaves for each variety (line), and the eigenvectors of the quality indicators of machine-harvested fresh leaves for each principal component, the linear regression equation is used to... Calculate the scores of each principal component for each variety (line); and Previously, the variance contribution rates of the three principal components were used as weighting coefficients, according to the quality evaluation equation for machine-harvested fresh leaves. α k Calculate the quality evaluation score of machine-harvested fresh leaves for each variety (line).

[0031] S6: Tea varieties are divided into four grades: I, II, III, and IV. Grade I represents varieties with high-quality machine-harvested fresh leaves, Grade II represents varieties with medium-quality machine-harvested fresh leaves, Grade III represents varieties with poor-quality machine-harvested fresh leaves, and Grade IV represents varieties with extremely poor-quality machine-harvested fresh leaves. The evaluation score for the quality of machine-harvested fresh leaves is S. i ≥0.57 is Level I, 0≤S i <0.57 indicates Level II; -0.57≤S i <0 indicates Level III, S i A score < -0.57 indicates Grade IV. Rankings and grades are based on the evaluation scores for machine-harvested fresh leaves, and are listed in Table 1.

[0032] Table 1. Quality Evaluation Scores of Machine-Harvested Fresh Leaves of Various Varieties (Lines)

[0033]

[0034] Based on the evaluation scores of the machine-harvested fresh leaves of each tea variety, the quality of the machine-harvested fresh leaves of the nine tea varieties is as follows: 07-7-72, Echa 201, Echa No. 1, Zhongcha 108, Fuding Dabaicha, 07-7-74, Longjing 43, Jin Guanyin, and Echa No. 7. Among them, Echa 201 has been identified as a tea variety suitable for machine harvesting, and 07-7-72 is a new tea variety suitable for machine harvesting bred by the research group of the applicant. This shows that the identification results of this method are consistent with the actual situation of the varieties, further demonstrating the accuracy and reliability of the method.

[0035] Example 2

[0036] The specific steps are as follows:

[0037] S1. When the proportion of one bud and two leaves or more (one bud and two leaves, one bud and three leaves, ..., paired leaves) on the surface of the tea tree variety Fuding Da Bai Cha is 50%, 60%, 70%, and 80%, machine harvesting is carried out and the machine-harvested fresh leaves are collected.

[0038] S2: Randomly weigh 500g of machine-harvested fresh leaves, accurately separate them into 9 main mechanical components: single bud, one bud and one leaf, one bud and two leaves, one bud and three leaves, one bud and more than three leaves, single leaf, old stems and old leaves, broken pieces, and damaged buds and leaves, and weigh them. Figure 1 The study calculated 13 key quality indicators for machine-harvested fresh leaves, including single bud rate, one bud and one leaf rate, one bud and two leaves rate, one bud and three leaves rate, one bud and three or more leaves rate, single leaf rate, old stem and old leaf rate, broken leaf rate, damaged bud and leaf rate, intact bud and leaf rate, standard bud and leaf rate, high-quality rate, and discard rate. The intact bud and leaf rate is the sum of the proportions of single buds, one bud and one leaf, one bud and two leaves, one bud and three leaves, and one bud and three or more leaves; the standard bud and leaf rate is the sum of the proportions of one bud and two leaves and one bud and three leaves; the high-quality bud and leaf rate is the sum of the proportions of single buds, one bud and one leaf, and one bud and two leaves; and the discard rate is the sum of the proportions of old stem and old leaves and broken leaves.

[0039] S3: Based on the mean and standard deviation of the original model data, the main machine-harvested fresh leaf quality indicators of the proportion of one bud with two or more leaves (one bud with two leaves, one bud with three leaves, ..., paired leaves) on different canopy surfaces are dimensionlessly processed to obtain standardized values ​​of the main machine-harvested fresh leaf quality indicators for different canopy surfaces. The dimensionless processing is based on the "Z-score standardization" method using the mean and standard deviation of the original data, i.e. in Let σ be the average value of the j-th indicator. j Let be the standard deviation of the j-th indicator.

[0040] S4: Perform principal component analysis on the quality indicators of machine-harvested fresh leaves obtained in step S2, and obtain the variance contribution rate (α) of the first three principal components. k ) and the eigenvectors (λ) of each machine-harvested fresh leaf quality index kj );

[0041] S5: Based on the standardized values ​​of the quality indicators of machine-harvested fresh leaves according to the proportion of one bud and two or more leaves (one bud and two leaves, one bud and three leaves, ..., paired leaves) on different canopy surfaces, and the eigenvectors of the quality indicators of machine-harvested fresh leaves of each principal component, the linear regression equation is used to... Calculate the scores of each principal component with different proportions; and use the variance contribution rate of the first three principal components as weighting coefficients, according to the quality evaluation equation for machine-harvested fresh leaves. The quality assessment scores for machine-harvested fresh leaves with different proportions were calculated, and the specific results are shown in Table 2.

[0042] The scores are divided into four levels: I, II, III, and IV. Level I represents high-quality machine-harvested fresh leaves, Level II represents medium-quality machine-harvested fresh leaves, Level III represents poor-quality machine-harvested fresh leaves, and Level IV represents extremely poor-quality machine-harvested fresh leaves. The quality assessment score for machine-harvested fresh leaves is S. i ≥0.57 is Level I, 0≤S i <0.57 indicates Level II; -0.57≤S i <0 indicates Level III, S i <-0.57 indicates Level IV.

[0043] Table 2. Quality Evaluation Scores of Fresh Leaves Harvested by Different Types of Canopy-Fried Tea in Fuding.

[0044]

[0045] Based on the quality evaluation score of machine-harvested fresh leaves according to the proportion of one bud and two or more leaves (one bud and two leaves, one bud and three leaves, ..., paired leaves) on different canopy surfaces, the quality of machine-harvested fresh leaves from high to low is as follows: the proportion of one bud and two or more leaves on the canopy surface is 60%, 50%, 70%, and 80%, which is consistent with production practice, indicating that this method is accurate and reliable.

Claims

1. A method for identifying and evaluating the quality of machine-harvested fresh leaves of a tea variety, characterized in that: Includes the following steps, S1: When the proportion of one bud and two or more leaves on the surface of the tea tree is 60%, machine harvesting is carried out on different tea tree varieties and the machine-harvested fresh leaves are collected. S2: Determine the main quality indicators of machine-harvested fresh leaves of different tea varieties; S3: Dimensionless processing is performed on the quality indicators of machine-harvested fresh leaves obtained in step S2; S4: Perform principal component analysis on the quality indicators of machine-harvested fresh leaves obtained in step S2, and obtain the variance contribution rate (α) of the first three principal components. k ) and the eigenvectors (λ) of each machine-harvested fresh leaf quality index kj ); S5: Based on the standardized values ​​of the quality indicators of machine-harvested fresh leaves and the eigenvectors of the quality indicators of machine-harvested fresh leaves of each principal component, establish the linear regression equations of the first three principal components, and use the variance contribution rate of the first three principal components as weighting coefficients to establish a quality identification and evaluation model for machine-harvested fresh leaves of tea varieties. S6: Compare the quality evaluation scores of machine-harvested fresh leaves of different tea varieties obtained in step S5. The higher the score, the higher the quality of the machine-harvested fresh leaves.

2. The method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties according to claim 1, characterized in that: The main quality indicators of machine-harvested fresh leaves in step S2 are: single bud rate, one bud and one leaf rate, one bud and two leaves rate, one bud and three leaves rate, one bud and three or more leaves rate, single leaf rate, old stem and old leaf rate, broken leaf rate, damaged bud and leaf rate, intact bud and leaf rate, standard bud and leaf rate, high-quality rate, and discard rate.

3. The method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties according to claim 2, characterized in that: In step S2, the main methods for determining the quality indicators of machine-harvested fresh leaves are as follows: accurately separate the nine main mechanical components of fresh leaves into single buds, one bud and one leaf, one bud and two leaves, one bud and three leaves, one bud and more than one leaf, single leaf, old stems and old leaves, broken pieces, and damaged buds and leaves, and weigh them. Calculate the quality indicators of each machine-harvested fresh leaf. Among them, the complete bud and leaf rate is the sum of the proportions of single buds, one bud and one leaf, one bud and two leaves, one bud and three leaves, and one bud and more than one leaf; the standard bud and leaf rate is the sum of the proportions of one bud and two leaves and one bud and three leaves; the high-quality bud and leaf rate is the sum of the proportions of single buds, one bud and one leaf, and one bud and two leaves; and the discard rate is the sum of the proportions of old stems and old leaves and broken pieces.

4. The method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties according to claim 1, characterized in that: In step S3, the dimensionless processing is performed using the "Z-score standardization" method based on the mean and standard deviation of the original data, i.e., Z-score. in Let σ be the average value of the j-th indicator. j Let be the standard deviation of the j-th indicator.

5. The method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties according to claim 4, characterized in that: In step S5, the linear regression equations for each principal component are:

6. A method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties according to claim 4 or 5, characterized in that: In step S5, the quality assessment model for machine-harvested fresh tea leaves of tea varieties is as follows:

7. The method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties according to claim 1, characterized in that: In step S6, tea varieties are divided into four grades: I, II, III, and IV. Grade I represents varieties with high-quality machine-harvested fresh leaves, Grade II represents varieties with medium-quality machine-harvested fresh leaves, Grade III represents varieties with poor-quality machine-harvested fresh leaves, and Grade IV represents varieties with extremely poor-quality machine-harvested fresh leaves. The quality assessment score for machine-harvested fresh leaves is S. i ≥0.57 is Level I, 0≤S i <0.57 indicates Level II; -0.57≤S i <0 indicates Level III, S i <-0.57 indicates Level IV.

8. The method for identifying and evaluating the quality of machine-harvested fresh leaves of tea varieties according to claim 1, characterized in that: In step S1, the canopy cultivation methods and field management measures for different tea varieties are the same.

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