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© 2023 by the authors; licensee Asian Online Journal Publishing Group 
 

Agriculture and Food Sciences Research 
Vol. 10, No. 1, 1-7, 2023 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/aesr.v10i1.4839 

© 2023 by the authors; licensee Asian Online Journal Publishing Group 

 

 
 
 
Optimal design of blade parameters for fracturing tea-picking machine 

 
Zehui Jiang1   

Yongguang Hu2   

Wenchao Wu3   

  
( Corresponding Author) 

 
1,2,3Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education Jiangsu Province, 
Zhenjiang, Jiangsu University, China. 
1Email: 2222016029@stmail.ujs.edu.cn  
2Email: deerhu@ujs.edu.cn  
3Email: 2111716005@stmail.ujs.edu.cn  

 
Abstract 

The blade is one of the most critical components in the fracturing tea-picking machine, and this 
study is conducted to optimize the blade's working parameters. In this study, the effects of blade 
width, blade thickness, and cutting angle on the maximum fracturing force of tea stems were 
analyzed using the L9 (34) standard orthogonal table, with the maximum fracturing force used as 
the evaluation index. The results indicate that the main factors affecting the maximum fracturing 
force (MFF) of tea stems are cutting angle (CA), blade width (BW), and blade thickness (BT) in that 
order. Furthermore, microscopic observation of the fracture surface revealed that compared with 
the thickness of the other two blades, the thickness of 0 mm caused the cross-section uneven and 
had lots of burrs, correspondingly resulting in the section's oxidation and the deterioration of tea 
leaf quality. Therefore, the optimal combination of design parameters was a cutting angle of 90°, a 
blade width of 2.0 mm, and a blade thickness of 0.5 mm. The findings of this study can provide 
reference for blade design to reduce the fracturing force of tea-picking machines, lower the working 
power consumption, and improve the quality of freshly plucked tea leaves. 

 
Keywords: Blade, Famous premium tea, Fracturing force, Optimal design, Orthogonal experiment, Tea-picking machines. 

 
Citation | Jiang, Z., Hu, Y., & Wu, W. (2023). Optimal design of 
blade parameters for fracturing tea-picking machine. Agriculture and 
Food Sciences Research, 10(1), 1–7. 10.20448/aesr.v10i1.4839 
History:  
Received: 13 March 2023 
Revised: 26 April 2023 
Accepted: 3 July 2023 
Published: 18 July 2023 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 

Funding: This research is supported by the China and Jiangsu Postdoctoral 
Science Foundation (Grant numbers: 2022M711396 and 2021K614C). 
Institutional Review Board Statement: The Ethical Committee of the Jiangsu 
University, China has granted approval for this study (Ref. No. 
JSDX20230717001). 
Authors’ Contributions: All authors contributed equally to the conception and 
design of the study. All authors have read and agreed to the published version 
of the manuscript. 
Competing Interests: The authors declare that they have no conflict of 
interest. 

 

Contents 
1. Introduction ......................................................................................................................................................................................... 2 
2. Materials and Methods ...................................................................................................................................................................... 3 
3. Results and Discussion ...................................................................................................................................................................... 5 
4. Conclusion ............................................................................................................................................................................................ 6 
References ................................................................................................................................................................................................. 7 
 

 

 

 

 

 

 

 

mailto:2222016029@stmail.ujs.edu.cn
mailto:deerhu@ujs.edu.cn
mailto:2111716005@stmail.ujs.edu.cn
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/aesr.v10i1.4839
https://orcid.org/0009-0000-9017-0406
https://orcid.org/0000-0002-3771-9380
https://orcid.org/0000-0001-6135-8004


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Contribution of this paper to the literature 
This study determined the optimal combination of blade parameters for a breaking-type tea 
harvester using the orthogonal experiment method. Currently, there is no research available on 
the optimization of blade parameters for this type of tea harvester. 

 
1. Introduction 

China is the largest tea producer in the world, with a tea plantation area of 3307.84 thousand hectares in 2021 
[1, 2]. The shortage of tea pickers and high labor cost have become common problems for tea production [3]. In 
view of the non-uniform planting specifications of tea gardens and the complex terrain of tea-growing areas in China, 
the development of portable tea-picking equipment is of practical significance [4, 5]. 

To improve the efficiency and quality of tea picking, people have been constantly exploring and innovating [6]. 
The hand-held tea harvester is a machine that has emerged under this background [7]. Compared with traditional 
manual picking, the hand-held tea harvester can quickly pick tea leaves, greatly improving the efficiency and yield of 
tea picking [8-10]. The fracturing tea-picking machine working principle is to use a rotating leaf-beating rod and a 
blade to cut off the tender shoots of tea trees, and then collect them through a collection device [11]. Compared with 
the reciprocating cutting tea picking machine, the hand-held tea harvester has adaptability to be adjusted according 
to different types of tea trees and crown structures [12] to ensure the quality and efficiency of picking. 

Pan designed a tea stem shear test device using an electronic universal testing machine and a fixed support to 
obtain the force-deformation curve when the stem is cut by the blade [13]. Cao specifically designed a mini-test 
machine that can adjust the entry angle of the tea harvester blade to measure the maximum shear force when the 
stem is cut [14]. 

The orthogonal experiment is to be conducted to analyze the effects of cutting angle, blade width, and blade 
thickness on the maximum fracturing force of tea stems. Their impact level is evaluated through data analysis, and 
the microscopic structure of the section cut by the blades of different parameters is observed. This study aims to 
explore the related content of the design experiment of blade parameters for the hand-held tea harvester, providing 
useful reference and guidance for its research and application. Figure 1 shows the fracturing tea-picking machine. 
Figure 2 shows the blade of fracture tea-picking machine. 
 

 
Figure 1. Fracturing tea-picking machine. 

 

 
Figure 2. Blade of  fracture tea-picking machine. 

 
 
 

Blade 



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2. Materials and Methods 
2.1 Materials 

The selected experiment site is Maichun Tea Farm located in Danyang, Jiangsu Province, China (32°02'26" N, 
119°67'44" E), which belongs to the hilly tea region of the middle and lower reaches of the Yangtze River. The tea 
variety planted is Mao Green, and the planting direction is north-south with a row spacing of 1.5 m and a crown 
width of 1.2 m. The tea garden was moderately pruned on August 10, 2022, and the experiment was conducted on 
October 12, 2022.Five square test areas with equal side lengths of 1.0 m were randomly selected, and 45 stems of 
similar growth stage and stem diameter were randomly selected in each area for leaf removal and subsequent testing. 
The stems are placed on the fixed holder by fixture. 

The maximum fracturing force was measured using a tensiometer (Edinburgh, China, accuracy 0.01 N). The 
microscopic structure of the stem cross-section was observed using a super depth-of-field three-dimensional 
microscope (Keyence, Japan, VHX-900F). The position of the tensile tester was adjusted using a lifting platform, and 
the blade approached the tested stems by a screw push rod. 

Three types of blades with different thicknesses were fixed onto the tensile tester using a clamp, and the stem 
position was measured using a ruler and fixed using a fixture. The experimental instrument is shown in Figure 3. 

 

 
Figure 3. Experiment materials. 

Note:  1. Fixed holder 2. Fixture 3. Stem 4. Blade 5. Clamp 6. Tensiometer 7. 
Screw push rod 8. Lifting platform. 

 

2.2. Orthogonal Experimental Design 
By assessing whether the design parameters of the blade affecting the maximum cutting force can be controlled 

quantitatively, the degree of control difficulty, and the control method selected the main influencing factors are: blade 
width (A), blade thickness (B), and cutting angle (C). To consider the size of the prototype, blade width(BW) range 
of 2.0-4.0mm, blade thickness(BT) of 0-1.0mm, and cutting angle(CA) of 90-135 °. 

Orthogonal tables are statistical tools used for experimental design and data analysis. Common orthogonal tables 
include L4 (23), L8 (827), L16 (215), L9 (34), etc. Take L9 (34)  and so on. In these tables, "L" represents the orthogonal 
table, while the numbers represent specific parameters of the table. 

For instance, L9 (34) means there are 9 horizontal rows, which corresponds to 9 experiments to be conducted. 
There are 3 factors, with each factor having 3 levels, and the maximum number of factors allowed to be arranged is 
4. The degrees of freedom for each factor are 2. Based on the principle that the degrees of freedom of the orthogonal 
table should be greater than or equal to the sum of the degrees of freedom of each factor and the degrees of freedom 
of interaction, as well as the principle of choosing the smallest number of orthogonal tables, L9 (34) was selected for 
the experiment [15]. 

It can be observed that orthogonal tables have two characteristics: (1) In each column, each level of each factor 
appears an equal number of times in the total number of experiments. (2) An ordered series in which different levels 
of any two factor columns appear in pairs, with each pair appearing an equal number of times. Therefore, the 
distribution of each factor level combination in the orthogonal table is balanced dispersed, and neatly comparable 
among all level combinations. The assignment of the interactions in the orthogonal table header design needs to be 
done according to the interaction table, which is mentioned in detail in the paper [15]. The level table of experimental 

factors is shown in Table 1.The experiment was started by replacing each factor level code with a specific level 
setting, and the experimental arrangement and sequence were shown in Table 2, and the maximum fracturing force 
was filled in the result column in Table 2 at the end of the experiment. 
 
Table 1. Table of  factor levels. 

Level 

A B C 
BW/mm BW/mm CA/° 

1 2 0 (Sharp edge) 90 
2 3 0.5 120 
3 4 1 135 

 
 



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Table 2. Table of  orthogonal tests. 

Test number A B C D Y 

1 1 1 1 1 1.79 
2 1 2 2 2 3.32 
3 1 3 3 3 4.34 
4 2 1 2 3 3.23 

5 2 2 3 1 4.31 
6 2 3 1 2 2.54 
7 3 1 3 2 4.35 
8 3 2 1 3 2.83 

9 3 3 2 1 4.48 

 

2.3. Fracturing Experiment 
Stem fracturing experiments were performed on a liftable platform and tensiometer. A clamp is used on the 

tensiometer to fix the blade for fracturing. The measured internode is fractured on the stem, the specimen is placed 
on the fixed holder, the blade position and the stem inclination are adjusted, the stem is slowly fractured and the 
maximum fracturing force is read on the tensiometer. The blade width is the distance between the blade and the 
fixture fixing the stem, the blade thickness is the thickness of the blade, and the cutting angle is the angle formed 
between the blade cutting into the stem and the stem in the direction of the stem. Each experiment was repeated five 
times, and the average maximum fracturing force was recorded in the result column of Table 2. Figures 4-6 show 
the different cutting angle. 
 

 
Figure 4. 90°CA. 

 

 
Figure 5. 120° CA. 

 

 
Figure 6. 135° CA. 

 
2.4. Stem Section Observation 

Cross-sectional sections of the stems were cut by the conventional sectioning method, and the cross-sectional 
features were compared under different blade fractures using an ultra-deep field 3D microscope. 



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3. Results and Discussion 
3.1. Orthogonal Test Results and Analysis 

For the processing and analysis of experimental results, there are usually two methods. One is the visual analysis 
method, also called range analysis method; the other is the analysis of variance method. The following two methods 
are used to compare and analyze the results of orthogonal experiments. 
 

3.1.1. Range Analysis 
The analysis steps of range analysis method are as follows: first, the experimental results of level one for each 

factor of each experiment are calculated, and the mean of k1. then the mean of level two for each factor of each 
experiment is calculated and the mean of k2, then the mean of level three for each factor of each experiment is 
calculated, and the mean of k3. then the range R of the mean of the three levels and k1, k2 and k3 is found, and finally, 
R is sorted from largest to smallest to determine the degree of influence of the factors. The results of range analysis 
are shown in Table 3. The principle of range analysis is the larger range, the more important the influence of the 
corresponding factor. In addition, the optimal level of each factor can be determined according to the average size of 
the results corresponding to the level of each factor. If the indicator is expected to be larger and better, the larger 
the average level is taken; if the indicator is expected to be smaller and better, the smaller the average level is taken. 
It can be seen that the required indicator in this test is the smaller the better, so the preliminary optimal level derived 
by the range analysis is A1B1C1. 

 
Table 3. Table of  range analysis. 

Test number A B C D Y 

1 1 1 1 1 1.79 

2 1 2 2 2 3.32 

3 1 3 3 3 4.34 

4 2 1 2 3 3.23 

5 2 2 3 1 4.31 

6 2 3 1 2 2.54 

7 3 1 3 2 4.35 

8 3 2 1 3 2.83 

9 3 3 2 1 4.48 

K1 9.45 9.37 7.16 10.58 / 

K2 10.08 10.46 11.03 10.21 / 

K3 11.66 11.36 13 10.4 / 

k1 3.15 3.12 2.39 3.53 / 

k2 3.36 3.49 3.68 3.40 / 

k3 3.89 3.79 4.33 3.47 A1B1C1 

Range R 0.74 0.66 1.95 0.12 C>A>B 

 
The orthogonal tests were performed according to the designed L9 (34) with a total of 9 test combinations, and 

the obtained test results are shown in Table 3. In order to accurately estimate the importance of the influence of the 
three design parameters of the blade on the maximum fracturing force, especially considering the interaction between 
the factors, it is necessary to conduct ANOVA on the orthogonal test results. The ANOVA results are shown in 
Table 2. The variances of A, B and C are less than 0.05, so A, B and C all have a significant influence on the test 
results, and the main order of the factors is: C>A>B. 
 

3.1.2. Analysis of Variance 
ANOVA is used to distinguish whether the differences in the investigated factors corresponding to experimental 

results due to different levels are caused by level changes or by experimental errors, in order to further test which 
factors have an effect on the results and which don’t and to distinguish which factors are major and which are minor. 
The analysis steps of ANOVA are as follows: first, the degrees of freedom of each factor, the degrees of freedom of 
interaction, the degrees of freedom of error, and the total degrees of freedom are listed. Then the sum of squares of 
deviations corresponding to each of these factors is calculated as Equation 1. 

 𝑆𝑆𝑇 = ∑ (𝑥𝑖 − �̅�)2𝑛
𝑖=1 . (1) 

Then the mean sum of squares of deviations corresponding to each factor is found by dividing the sum of squares 
of deviations by the degrees of freedom (DF). Equation 2 yields the mean sum of squares of deviations corresponding 
to A factor. 

  𝑆𝐴
2 =

𝑆𝑆𝐴

𝑓𝐴
.  (2) 

 
Then the F-value corresponding to each item is found by dividing the average deviation sum of squares by the 

average deviation sum of squares of errors. Equation 3 yields the F-value of A factor. 

  𝐹𝐴 =
𝑆𝐴

2

𝑆𝑒
2.  (3) 

The P-value is calculated using Excel's F.DIST.RT function. If the factor's P-value is ≤ α, there is (1-α) certainty 

that the factor has a significant impact. Commonly used values of α are α=0.01, α=0.05, and α=0.10. When PA≤0.01, 
it indicates an extremely significant impact, noted as "**"; when 0.01≤PA≤0.05, it indicates a significant impact, 
noted as "*"; and when PA≥0.05, there is no significant impact. Table 4 shows the ANOVA results for the orthogonal 
experiments. Factor C had an extremely significant impact, while factors A and B had a significant impact, with the 
main order of factors being C>A>B. This is consistent with the results of the range analysis. 
 

 



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Table 4. Analysis of  variance (ANOVA) table. 

Source of variance SST DF S2 F P Significance 

A 0.864 2 0.432 37.865 0.026 * 

B 0.662 2 0.331 29.008 0.033 * 

C 5.885 2 2.942 257.855 0.004 ** 

D (Difference) 0.023 2 0.011 1.000 1.000 / 

 

3.2. Analysis of Microscopic Features of the Cross-Section 
The difference in stem fracture between the different groups was in the fracture caused by the blade with a sharp 

edge and the blade without a sharp edge. The blade with a sharp edge fracture is shown in Figure 7, the 0.5 mm thick 
blade fracture is shown in Figure 8, and the 1 mm thick blade fracture is shown in Figure 9. Under the blade with a 
sharp edge fracturing picking, the blade squeezed the stem and formed an oval incision. The fracture interface was 
uneven and showed a large number of burrs. The stem tissue was torn at the fracture point and the fractured interior 
was also damaged by the squeezing action, resulting in the cavity observed in the wood pith. After fracturing picking, 
the fractured portion and the damaged internal tissues are extensively oxidized. The oxidation produces substances 
that darken the tea leaves [9]. Fracturing picking with a blade with a sharp edge disrupts the tissue within the stem, 
and this rupture produces excessive tissue fluid, which further increases the rate of oxidation. In contrast, fracturing 
with an unopened blade produces a flat, burr-free fracture. The oxidized area in the incisions of hand-picked shoots 
was significantly smaller than that of the blade fracturing picked ones. After the incisions were oxidized by ambient 
air, the xylem and pith in the stem tissue took on a brownish-red color, which could affect the color and flavor of the 
finished tea [16]. 
 

 
Figure 7. 0mm blade section.      

 

 
Figure 8. 0.5 mm blade section. 

 

 
   Figure 9.1mm blade section. 

 

4. Conclusion 
(1) Through the orthogonal test of three design parameters of the blade, the results show that the main and 

secondary factors affecting the maximum fracturing force in the order of cutting angle, blade width, and blade 
thickness. Among them, the cutting angle on the maximum fracturing force is very significant, the blade width, and 
blade thickness on the maximum fracturing force is significant, the preliminary optimal combination of design 
parameters level for the cutting angle: 90 °, blade width: 2.0 mm, blade thickness: 0 mm. 



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(2) Analysis of the stem sections fractured by different blades revealed that the fracture interface formed by the 
0mm blade was uneven relative to the other blades and showed a large number of burrs. The stem tissue was torn at 
the fracture point and the fractured interior was also damaged by the crushing action, with extensive oxidation of 
the fractured portion and the damaged tissue inside. The substance produced by oxidation will darken the tea leaves 
and affect the color and taste of the finished tea leaves. Therefore, a blade thickness of 0.5 mm should ultimately be 
selected. The optimal structural parameter level combination is the optimal structural parameter level combination 
of cutting angle: 90°, blade width: 2.0 mm, and blade thickness: 0.5 mm. 

 

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