Design and Evaluation of a Flexible Shelling and Cleaning Integrated Machine for Camellia oleifera Fruits

Round 1

Reviewer 1 Report (New Reviewer)

Comments and Suggestions for Authors

General Comments

The manuscript presents the design, simulation, and experimental validation of a mechanical system for the classification, shelling, and cleaning of Camellia oleifera fruits. The topic is relevant within agricultural engineering, particularly in mechanization and postharvest processing, and the combination of simulation and experimental work is appropriate.

However, the manuscript presents several important weaknesses that affect its scientific quality and clarity. The most critical issues relate to the poor quality and interpretation of figures, limited statistical rigor, and insufficient integration with recent international literature. Many figures are difficult to interpret or insufficiently explained, and the statistical analysis lacks depth in terms of validation and diagnostics. Additionally, the reference base is uneven: although some recent works (2024–2025) are included, a significant portion of the references are either relatively old or from local journals, with limited connection to high-impact international research. Overall, while the work has technical merit, substantial revisions are required to improve clarity, rigor, and presentation.

Specific Comments

1. (Lines 161–172) The schematic of the machine (Figure 5) is visually dense and difficult to interpret. The numbering of components is provided, but the figure lacks clear annotations and directional flow. The authors should improve readability by simplifying the figure or providing a clearer schematic with highlighted process flow.

2. (Lines 225–246) The description of the shelling device is detailed but difficult to follow due to weak linkage with Figure 6. It is not always clear which elements correspond to specific parts in the figure. The authors should explicitly reference components within the figure and ensure consistency between text and visual representation.

3. The quality of Figure 2 is particularly poor. The plot is difficult to read, axis labels and scales are unclear, and the confidence intervals are not well visualized. Given that this figure is critical for validating the model assumptions, it must be completely redesigned with clearer axes, properly labeled time periods, and well-defined confidence intervals.

4. (Lines 302–305, Figure 10) The response surface plots are not clearly explained. The axes, units, and interaction effects (AC, BC, AB) are not sufficiently described, making interpretation difficult. Additionally, no quantitative interpretation of these surfaces is provided. The authors should explain how these plots support the optimization process and include clearer axis labels and legends.

5. (Lines 308–316, Table 6) The ANOVA results are presented, but the statistical analysis is incomplete. It is not clear whether model assumptions such as normality and homoscedasticity of residuals were verified. Additionally, key metrics such as R² and adjusted R² are not reported. The authors should provide a more complete statistical validation of the model, including residual analysis, normality tests as Shapiro–Wilk, and homogeneity of variance tests for example Levene’s test.

6. (Lines 313–314) The regression equation for productivity is given, but its interpretation is limited. The authors should explain the physical meaning of coefficients, assess the relative importance of variables, and consider providing a sensitivity analysis or standardized regression coefficients.

7. (Lines 150–160, Table 1) The friction coefficient measurements are presented as average values only. There is no indication of variability (standard deviation or confidence intervals), which limits the reliability of the results. The authors should include measures of dispersion and perform repeated-measures analysis or uncertainty quantification.

8. (Lines 343–350) The comparison between simulation and experimental results is described qualitatively as having a “small error,” but no quantitative error metric is provided. The authors should report formal validation metrics such as relative error (%), RMSE, or mean absolute percentage error (MAPE).

9. (Lines 360–362, Table 8) Some experimental values are missing and indicated by “/”. The authors should explain why these data points are absent and whether this affects the robustness of the conclusions.

10. (Lines 364–370) The interpretation of shelling speed effects is descriptive and lacks quantitative rigor. Statements such as “productivity is closer” should be supported with explicit numerical comparisons or percentage differences, and ideally statistical significance testing (ANOVA or post hoc comparisons such as Tukey HSD).

11. (Lines 211–223) The selection of operating parameters (drying temperature of 80°C and duration of 90 minutes) is described, but the justification is not fully developed. The authors should clarify whether these values were optimized experimentally, theoretically derived, or adopted from previous studies.

12. (Figures 5, 6, 10, and 13; multiple sections) The overall quality of figures is insufficient. Several figures lack clarity, proper labeling, or detailed captions. In particular, Figure 13 (Lines 356–358) provides limited information about the experimental setup and does not clearly indicate measurement points or variables. All figures should be revised to improve resolution, labeling, and explanatory content.

13. (Lines 547–557, References) The reference list includes some recent studies (2024–2025), which is positive. However, many references are from local or regional journals, and there is limited inclusion of high-impact international literature. In addition, some references are relatively old (e.g., 2016 or earlier), which may not fully reflect the current state of the field. The authors should strengthen the reference base by incorporating more recent (last 5–8 years) and internationally recognized studies on agricultural machinery design, DEM simulation, and postharvest processing systems.

14. (Lines 547–557) The reference section lacks balance and coherence. Some citations appear highly specific or narrowly focused without clear integration into a broader scientific discussion. The authors should ensure that references are not only recent but also relevant and properly contextualized within the field.

15. The statistical treatment of experimental data is insufficient. The authors should clearly report the number of replicates and include measures of variability such as standard deviation or standard error. Additionally, inferential statistical tests should be applied where appropriate. 

16. The conclusion mainly summarizes results without clearly positioning the contribution relative to existing technologies. The authors should better highlight the advantages of the proposed system and its practical implications.

Author Response

Response to Reviewer 1 Comments
1. Summary
Thank you very much for taking the time to review this manuscript. We have already read all the comments and recommendations carefully, and tried our best to revise the manuscript accordingly. Please find the detailed responses below and the corresponding revisions highlighted red in the re-submitted files. We sincerely hope that this revised manuscript has addressed your comments and suggestions and appreciate your warm work earnestly. We have used the author services provided by MDPI to perform a complete, professional rewriting and polishing of the entire manuscript, including the Abstract, main text, and technical terminology. The English editing certificate number is English-108174.
2. Point-by-point response to Comments and Suggestions for Authors
Comments 1: (Lines 161–172) The schematic of the machine (Figure 5) is visually dense and difficult to interpret. The numbering of components is provided, but the figure lacks clear annotations and directional flow. The authors should improve readability by simplifying the figure or providing a clearer schematic with highlighted process flow.
Response 1: Thank you for pointing this out. We have revised and simplified the schematic figure to improve clarity. We have also added explicit annotations for each numbered component and highlighted the directional process flow in the figure to make the working principle easier to follow.
Comments 2: (Lines 225–246) The description of the shelling device is detailed but difficult to follow due to weak linkage with Figure 6. It is not always clear which elements correspond to specific parts in the figure. The authors should explicitly reference components within the figure and ensure consistency between text and visual representation.
Response 2: Thank you for pointing this out. We have revised the schematic figure to improve clarity. Following the reviewer’s suggestion, we have carefully revised the relevant description. We have added explicit references to the numbered components in figure throughout the text and carefully checked to ensure consistency between the textual labels and the visual elements in the figure.
Comments 3: The quality of Figure 2 is particularly poor. The plot is difficult to read, axis labels and scales are unclear, and the confidence intervals are not well visualized. Given that this figure is critical for validating the model assumptions, it must be completely redesigned with clearer axes, properly labeled time periods, and well-defined confidence intervals.
Response 3: Thank you for pointing this out. We have completely redesigned and redrawn the figure. The revised version includes clearer coordinate axes, standardized and legible axis scales and labels, accurately marked time periods, and distinctly displayed confidence intervals to ensure better readability and persuasiveness.
Comments 4: (Lines 302–305, Figure 10) The response surface plots are not clearly explained. The axes, units, and interaction effects (AC, BC, AB) are not sufficiently described, making interpretation difficult. Additionally, no quantitative interpretation of these surfaces is provided. The authors should explain how these plots support the optimization process and include clearer axis labels and legends.
Response 4: Thank you for pointing this out. We have comprehensively revised the relevant content. We have standardized and clearly labeled axes and corresponding units, supplemented detailed descriptions of two-factor interaction effects (AB, AC, BC), and added quantitative interpretation of the response surfaces. Furthermore, we have elaborated on how these plots support the analysis and determination of optimal process parameters. The axis labels and legends have also been further optimized to improve readability.
Comments 5: (Lines 308–316, Table 6) The ANOVA results are presented, but the statistical analysis is incomplete. It is not clear whether model assumptions such as normality and homoscedasticity of residuals were verified. Additionally, key metrics such as R² and adjusted R² are not reported. The authors should provide a more complete statistical validation of the model, including residual analysis, normality tests as Shapiro–Wilk, and homogeneity of variance tests for example Levene’s test.
Response 5: Thank you for pointing this out. we have supplemented a complete statistical validation for the model. Specifically, we have added residual analysis, including the Shapiro–Wilk test to verify the normality of residuals and Levene’s test to assess the homogeneity of variance. Meanwhile, we have supplemented and clearly reported the R² and adjusted R² values to fully demonstrate the goodness of fit and reliability of the model.
Comments 6: (Lines 313–314) The regression equation for productivity is given, but its interpretation is limited. The authors should explain the physical meaning of coefficients, assess the relative importance of variables, and consider providing a sensitivity analysis or standardized regression coefficients.
Response 6: Thank you for pointing this out. We have added a detailed explanation of the physical meaning of each regression coefficient in the revised manuscript. We have also analyzed the relative importance of each independent variable and supplemented sensitivity analysis and/or standardized regression coefficients to quantitatively evaluate the influence degree of each factor on productivity. These additions make the model more interpretable and the results more convincing.
Comments 7: (Lines 150–160, Table 1) The friction coefficient measurements are presented as average values only. There is no indication of variability (standard deviation or confidence intervals), which limits the reliability of the results. The authors should include measures of dispersion and perform repeated-measures analysis or uncertainty quantification.
Response 7: Thank you for pointing this out. Following the reviewer’s suggestion, we have now supplemented all friction coefficient results with standard deviation (SD) and 95% confidence intervals (CI) as measures of data variability. These additions significantly improve the reliability and reproducibility of the experimental results.
Comments 8: (Lines 343–350) The comparison between simulation and experimental results is described qualitatively as having a “small error,” but no quantitative error metric is provided. The authors should report formal validation metrics such as relative error (%), RMSE, or mean absolute percentage error (MAPE).
Response 8: Thank you for pointing this out. we have supplemented quantitative error indicators including relative error (%) to objectively evaluate the agreement between simulation and experimental data. These quantitative metrics have been added to the revised manuscript to improve the accuracy and reliability of the model validation.
Comments 9: (Lines 360–362, Table 8) Some experimental values are missing and indicated by “/”. The authors should explain why these data points are absent and whether this affects the robustness of the conclusions.
Response 9: Thank you for pointing this out. These data are missing mainly because when the rotation speed is below 150 rpm, the equipment cannot properly complete the shelling task, and the camellia seeds get stuck in the device. Therefore, data for speeds not exceeding 150 rpm cannot be collected. Since these points are not within the key parameter range and do not affect the main trend and regularity of the experimental results, the robustness and reliability of the conclusions are not influenced.
Comments 10: (Lines 364–370) The interpretation of shelling speed effects is descriptive and lacks quantitative rigor. Statements such as “productivity is closer” should be supported with explicit numerical comparisons or percentage differences, and ideally statistical significance testing (ANOVA or post hoc comparisons such as Tukey HSD).
Response 10: Thank you for pointing this out. We have revised the relevant content by supplementing explicit numerical comparisons and percentage differences to support our conclusions, instead of using vague expressions such as “productivity is closer.”
Comments 11: (Lines 211–223) The selection of operating parameters (drying temperature of 80°C and duration of 90 minutes) is described, but the justification is not fully developed. The authors should clarify whether these values were optimized experimentally, theoretically derived, or adopted from previous studies.
Response 11: Thank you for pointing this out. These values were optimized experimentally, Within 90 minutes of drying, the cracking rate rises rapidly to approximately 90%, which is significantly higher than that at 60°C (red line) and 40°C (black line). When the duration reaches 90 minutes, the cracking rate at 80°C approaches the maximum value, and a further extension of duration (from 90 to 180 minutes) results in only a marginal increase in the cracking rate.
Comments 12: (Figures 5, 6, 10, and 13; multiple sections) The overall quality of figures is insufficient. Several figures lack clarity, proper labeling, or detailed captions. In particular, Figure 13 (Lines 356–358) provides limited information about the experimental setup and does not clearly indicate measurement points or variables. All figures should be revised to improve resolution, labeling, and explanatory content.
Response 12: Thank you for pointing this out. We have comprehensively revised all figures to improve their resolution, clarity, and standardization of labels. For Figure 13 specifically, we have redrawn the schematic to clearly mark the measurement points and corresponding variables, and supplemented a more detailed and informative caption. All revised figures have been updated in the manuscript to enhance readability and academic presentation.
Comments 13: (Lines 547–557, References) The reference list includes some recent studies (2024–2025), which is positive. However, many references are from local or regional journals, and there is limited inclusion of high-impact international literature. In addition, some references are relatively old (e.g., 2016 or earlier), which may not fully reflect the current state of the field. The authors should strengthen the reference base by incorporating more recent (last 5–8 years) and internationally recognized studies on agricultural machinery design, DEM simulation, and postharvest processing systems.
Response 13: Thank you for pointing this out. we have comprehensively updated and optimized the reference list. We have added a number of high-quality, internationally recognized studies published in the past 5–8 years focusing on agricultural machinery design, DEM simulation, and postharvest processing systems. We have also appropriately replaced outdated references and integrated the new literature into relevant sections to better reflect the state-of-the-art in the field and strengthen the academic foundation of this work. All updates have been marked in the revised manuscript.
Comments 14: (Lines 547–557) The reference section lacks balance and coherence. Some citations appear highly specific or narrowly focused without clear integration into a broader scientific discussion. The authors should ensure that references are not only recent but also relevant and properly contextualized within the field.
Response 14: Thank you for pointing this out. We have carefully re‑evaluated and revised the citation structure. We have streamlined overly specialized references, strengthened the contextualization of key citations, and integrated them into the overall research background and academic discussion. These revisions have improved the logical coherence and academic rigor of the manuscript.
Comments 15: The statistical treatment of experimental data is insufficient. The authors should clearly report the number of replicates and include measures of variability such as standard deviation or standard error. Additionally, inferential statistical tests should be applied where appropriate.
Response 15: Thank you for pointing this out. We are grateful for the reviewer’s critical and helpful comment on improving the statistical analysis of experimental data. Following the suggestion, we have thoroughly strengthened the statistical treatment throughout the manuscript. All revisions are clearly marked in the revised manuscript. We hope these changes meet the reviewer’s requirements.
Comments 16: The conclusion mainly summarizes results without clearly positioning the contribution relative to existing technologies. The authors should better highlight the advantages of the proposed system and its practical implications.
Response 16: Thank you for pointing this out. In the part of discussion, we have discussed the advantages of the proposed system and its practical implications. At the same time, we have revised and expanded the conclusion, highlight its novelty, performance advantages, and technical improvements, and further elaborate on its practical implications, application prospects, and engineering value in agricultural production and postharvest processing. These revisions enhance the academic positioning and practical significance of the work.

 

Author Response File: Author Response.pdf

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

The authors wrote an article entitled: "Design and Evaluation of a Flexible Shelling and Cleaning Integrated Machine for Camellia Oleifera Fruits", which deals with the design, construction and experimental verification of an integrated machine for post-harvest processing of Camellia oleifera fruits.
Ideas for improvement:
- line 290, equations should be numbered. Throughout the article.
- It is not sufficiently explained how fruit samples were selected and prepared for individual experiments. Discuss this more.
- In one place, gaps of 23, 37 and 52 mm are given, in another 20, 35 and 50 mm. It is not sufficiently explained which values ​​apply to the final design and why the change occurred.
- It is not clear how exactly the agreement of the simulation with the experiment was verified at the level of particle trajectories, throughput or product distribution into classes. The simulation focuses only on the sorting part, while the device is presented as an integrated system
- Averages are presented for bench tests, but without a deeper statistical comparison between modes. The article does not show whether the differences between the variants are practically and statistically significant
- The discussion of the results is more summary than analytical. The authors do not show what their machine is actually better at, by how much and at what price of compromises.

Author Response

Response to Reviewer 2 Comments
1. Summary
Thank you very much for taking the time to review this manuscript. We have already read all the comments and recommendations carefully, and tried our best to revise the manuscript accordingly. Please find the detailed responses below and the corresponding revisions highlighted red in the re-submitted files. We sincerely hope that this revised manuscript has addressed your comments and suggestions and appreciate your warm work earnestly.
2. Point-by-point response to Comments and Suggestions for Authors
Comments 1: line 290, equations should be numbered. Throughout the article.
Response 1: Thank you for pointing this out. We have carefully checked all equations throughout the article and added consecutive equation numbers in accordance with the journal’s formatting requirements. All equations are now uniformly numbered to improve readability and citation convenience.
Comments 2: It is not sufficiently explained how fruit samples were selected and prepared for individual experiments. Discuss this more.
Response 2: Thank you for pointing this out. In the measurement phase, to ensure more random sampling, the materials were repeatedly inverted and thoroughly mixed. Samples were then collected from the upper, middle, and lower layers as well as the periphery of the material pile, and this process was repeated to obtain 500 experimental samples. In the bench test phase, the sample size is large enough, we directly use the weighing method to select the number of test samples we need.
Comments 3: In one place, gaps of 23, 37 and 52 mm are given, in another 20, 35 and 50 mm. It is not sufficiently explained which values apply to the final design and why the change occurred.
Response 3: Thank you for pointing this out. The gaps of 20, 35 and 50 mm are set for Camellia oleifera fruits before drying-induced cracking. As the fruits crack during drying, their dimensions will increase appropriately. The gaps of 23, 37 and 52 mm are designed to accommodate the Camellia oleifera fruits after drying.
Comments 4: It is not clear how exactly the agreement of the simulation with the experiment was verified at the level of particle trajectories, throughput or product distribution into classes. The simulation focuses only on the sorting part, while the device is presented as an integrated system
Response 4: Thank you for pointing this out. The computational model is simplified and mainly used for preliminary theoretical analysis and throughput estimation. It focuses on reflecting the circumferential motion and screening penetration tendency of Camellia oleifera fruits in the drum, rather than serving as a rigorous three-dimensional dynamic simulation. For the classification stage, characterized by strong multi-factor interaction, collaborative optimization of dual indicators, and sufficient simulation data support, the response surface methodology is adopted as the appropriate scientific approach. In contrast, the cleaning system features weak factor interaction and progressive optimization of a single indicator, with equal consideration given to test efficiency and engineering practicality; thus, the single-factor method combined with parameter coupling verification is utilized. This strategy not only avoids the over-design associated with the response surface methodology, but also ensures the accuracy of parameter optimization through supplementary verification. The selection of the two methods is precisely matched to the characteristics of each process, ultimately achieving the optimal performance objectives for each procedure and reflecting the integration of scientific experimental design and engineering practicability.
Comments 5: Averages are presented for bench tests, but without a deeper statistical comparison between modes. The article does not show whether the differences between the variants are practically and statistically significant
Response 5: Thank you for pointing this out. In this study, we only conducted simulation tests for classifying research, lacking comparative simulation tests of the integrated machine. Therefore, in the bench tests of the integrated machine, we examined whether the five actual indicators (shelling rate, breakage rate, impurity rat, loss rate and productivity) of the integrated machine reached the target values and met industry standards. Next, we are going to conduct a simulation test of the integrated machine, we can show whether the differences between the variants are practically and statistically significant.
Comments 6: The discussion of the results is more summary than analytical. The authors do not show what their machine is actually better at, by how much and at what price of compromises.
Response 6: Thank you for pointing this out. We have revised the discussion section to strengthen the analytical content. We have added the practical significance of the study and objectively analyzed the corresponding compromises and applicable conditions. These revisions make the discussion more in-depth and convincing.

 

Round 2

Reviewer 1 Report (New Reviewer)

Comments and Suggestions for Authors

All the comments and issues were addressed. Congratulations to the authors, good job. 

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript presents the design and testing of a combined shelling and cleaning machine for Camellia oleifera fruits. While the topic addresses an area of practical relevance in post-harvest processing, the overall quality of the writing and the scientific contribution of the work are unfortunately not sufficient to warrant publication in its current form. The authors are encouraged to consider the following major concerns:

L2: The Latin name for the crop, Camellia oleifera, should be italicized. Please correct this throughout the entire manuscript.

L13-16: While the introduction lists several issues with existing technologies, this study only addresses three specific aspects (see L16). The text describing the problems could be significantly condensed to better align with the paper's scope.

L18: Response Surface Methodology (RSM) is a multi-objective optimization tool. To clarify its application, please briefly state its working principle in the context of the classification process here.

L19: Delete "finally" to create a more concise subordinate clause and improve sentence flow.

L22-26: The descriptions of the hulling and cleaning systems are presented in a jumbled manner, mixing their functions and working principles. It is recommended to follow the clearer structure used for the classification device in L17-21, describing each subsystem separately.

L27-28: The presented performance metrics appear to be target or approximate results, not absolute fixed values. Please use appropriate terminology such as "less than," "does not exceed," or "reaching" to reflect this.

L28: The term "tea seed" is a literal translation from a machine translator and is incorrect in this context. Please review the full text for instances of "tea" and replace them with the correct terminology for camellia seeds. This highlights the need for a comprehensive revision of the English writing throughout.

L30: Can the conclusions of this single study truly claim to solve the "key technical bottlenecks" of the entire industry? This is highly unlikely. Please rewrite the significance and implications of the research in lines L30-34 more modestly and accurately.

L32: There appears to be a formatting or layout issue with the manuscript text at this line.

L35: Please add keywords related to "classification." Furthermore, the terms "hulling" and "shelling" are used inconsistently. Please standardize the terminology throughout the manuscript.

L42: It is advisable to provide an explanation for "mu," for example: "… 75 million mu (1 mu = 1/15 hectare)."

L49, L84, L120: These three sections discuss different aspects, but the excessive detail over 130 lines makes the technical narrative difficult to follow. It would be more effective to condense this content by more than half and clearly outline the main thread of the technical problems and the context.

L194: Would this be a more logical place to start a new paragraph?

L201: "Oil Tea" is another example of a perfect literal translation that is incorrect. Please carefully revise this fundamental issue throughout the manuscript (also see L356, etc.).

L202: This should be written as "Shuangfeng County." or "Shuang Feng County."

L203: Please define the abbreviation "DBH" upon first use.

L208: Is the measurement "35-45mm" correct? Please review and correct all numerical values and units.

L217-L220: Figures 3 and 4, in their current form, do not demonstrate basic academic writing standards. What specific technical operation are these figures intended to illustrate? Please annotate them clearly. The purpose is not merely to show that an experiment was conducted, but to explain the techniques and methods used to conduct it.

L241: Are the annotations in these figures correct?

L244-L267: This is an excessively long paragraph. Please break it into several smaller, more focused paragraphs for better readability.

L269: Section headings should be capitalized. Please check this throughout (see also L515).

L296: Is "K1" intended to be subscript? Please verify and correct similar formatting errors in other equations.

L311: There seems to be a formatting issue with an equation here; it is not displaying correctly.

L318: Are you an avid follower of Dr. Liao? Please show your appreciation more discreetly by using a proper citation.

L339: What are the specific structural features presented in this figure? What are the innovations of this structure? Please describe these not just in the text but also add clear annotations to Figure 6 and its corresponding description.

L388: Figures 8 and 9 are visually appealing, but what is the academic point you are trying to convey? Are they meant to illustrate a concept or merely to showcase an image? The description could be more accurate, for example, by referring to the article: "Construction and experiment of discrete element flexible model for breaking shell of Camellia oleifera fresh fruit."

L393-L461: The analysis presented here uses very common and standard methods. It is not clear how this analysis specifically relates to, or was tailored for, the structural design proposed in this paper. Please elaborate on this connection.

L445: In Table 6, there is an inconsistent use of notation for powers of ten (e.g., "10^5" vs. "E+05"). Please standardize this throughout the manuscript.

L489: In Table 8, all parentheses "（）" are in the Chinese full-width format. This is not acceptable. Please correct this throughout.

L501, L504: The last sentences of these paragraphs end with a colon "：". These should be changed to periods.

L528: This is the Discussion section, the most critical part of a scientific paper. Unfortunately, it lacks any substantive discussion:

(1) The lengthy first paragraph is largely irrelevant to the study's findings and should be deleted.

(2) The second paragraph is similarly verbose and subjective. If both are deleted, the section would be empty, effectively justifying a rejection.

(3) If the authors disagree, this entire section must be completely rewritten based on a proper understanding of how to write a scientific discussion. Consulting Dr. Liang Fujun's book on academic writing would be a good starting point.

L570: Please correct the double colon "::" found in the text.

L579: The references cited are, on the whole, quite dated. The first reference, for instance, is from 1999. The literature review should be updated to include more recent and relevant work.

L656: Many references contain formatting errors in their presentation, for example, "1 -8" should be "1-8". Please review and correct the entire reference list.

 

=================

Specific Questions for Authors:

Core Contribution: What is the single most significant contribution of this paper? If it addresses three aspects, what are they, and what is the overarching contribution that ties them together?

Nature of Optimization: Does the main contribution lie in the mechanical structure innovation, or in the application of a specific optimization method to find working parameters? What aspects of this optimized structure or method are valuable and could be instructive for other researchers?

Declaration of AI Use: It appears that parts of this manuscript may have been processed using AI for translation or editing. Beyond refining the language to remove these stylistic traces, the authors must declare the role of AI in their work, as per standard academic publishing guidelines.

Comments on the Quality of English Language

The manuscript exhibits a substandard level of English proficiency that significantly impedes scientific comprehension. Numerous instances of literal, word-for-word translations from Chinese (e.g., "Oil Tea," "tea seed") render critical terminology inaccurate and unprofessional.

The writing lacks fluency, contains pervasive grammatical errors, and demonstrates inconsistent use of key technical terms such as "hulling" versus "shelling." These issues are not isolated but systemic throughout the text, from the abstract to the references.

A superficial language edit will be insufficient; the manuscript requires a complete, professional re-writing by either a native English speaker with subject-matter expertise or a reputable scientific editing service before it could even be considered for review. In its current state, the language barrier alone provides sufficient grounds for rejection.

Reviewer 2 Report

Comments and Suggestions for Authors

Observations:

1. In my opinion the Discussion section is too short, and not appropriated for the subject, it should more detailed, even the discussion part of the results moved in this section.
2. The References are not following the MDPI Agriculture template.
3. It seems that the equations are not edited according to the template, there are mixed characters, in pdf seems they use higher font than the text, dots should be not used between a formula notation.
4. In Figure 1 are two images, what’s the difference between them? Should be provided a figure sub caption for each image.
5. To have at the beginning a clear view of the structural design of general hulling and cleaning machine Figure 5 should be larger, much more visible.
6. Table 8 and 9 should be rearranged.
7. It is not clearly formulated if only the classification simulation were carried out in EDEM software.

Reviewer 3 Report

Comments and Suggestions for Authors

1. How does the 2D mathematical model for classification throughput account for the 3D tumbling and interlocking of irregular fruits during high-speed rotation?
2. Does the mathematical model for the classification device incorporate the friction-induced scrubbing that affects fruit orientation between the auger and roller?
3. Given that camellia fruits are naturally irregular, how is the filling coefficient in the throughput math adjusted to prevent overestimation?
4. Is there a specific mathematical formulation for the slip ratio between the polyurethane hulling drum and the fruit to account for power loss?
5. Does the paper provide a mathematical model showing the impact force distribution of the flexible beef tendon rods to justify the claimed low breakage rate?
6. Is there a dynamic equation explaining how the center of mass shift during processing affects machine vibration and structural fatigue?
7. Since friction was measured at 65% moisture, does the math account for how these parameters change as the fruit dries during processing?
8. Equation (1) calculates throughput, but does it mathematically define the physical point of clogging or material backup?
9. What is the mathematical basis for setting the pitch equal to the blade diameter  in terms of sorting precision?
10. Is there a calculated velocity gradient for the material layer on the cleaning conveyor belt to ensure uniform separation?
11. Why does the paper lack a theoretical explanation for why a 1 deviation from the 9.6 rise angle impacts efficiency so significantly?
12. Does the model account for the rotational inertia of the internal beating rods when calculating startup torque requirements?
13. What specific Hertzian contact theory parameters were used to model the fruit-to-metal impact in the simulation?
14. How is the low-temperature airflow mentioned in the abstract mathematically coupled with the mechanical separation components?
15. Is there a formal mathematical model for the total power consumption and energy efficiency of the integrated machine?
16. Does the EDEM simulation account for natural biological variance, such as fruit deformities, or does it rely only on perfect  scanned models?
17. Is the sample size of 500 fruits statistically significant enough to represent the diversity of camellia crops across different provinces?
18. What was the specific time-step value used in the EDEM simulation to ensure the stability of the contact physics?
19. Does the simulation data include the transient phase during the first 60 seconds of feeding, or only the steady-state operation?
20. What specific boundary conditions, such as wall friction or air resistance, were omitted from the simplified simulation model?
21. How was the 65% moisture content maintained or monitored precisely during the physical bench tests to ensure data consistency?
22. Was a transport delay or latency factor included in the control simulation for the cleaning system’s feedback loop?
23. Why was Response Surface Methodology used for the classification stage but omitted for the cleaning system tests?
24. Does the paper provide simulation data regarding the specific vibration frequencies of the grid roller during high-capacity loads?
25. Is there a wear model in the simulation to predict the performance degradation of the polyurethane components over time?
26. Does the simulation environment account for high-humidity atmospheric conditions typical of camellia harvesting regions?
27. What happens mathematically to fruits that fall exactly on the size threshold of the 37mm grid bar spacing?
28. Does the experimental setup include sensors to measure when the motors reach their peak torque saturation limits?
29. Can you specify where the 3.66% tea seed loss occurs, is it during classification, hulling, or the cleaning stage?
30. Is the 1.99% impurity rate composed mostly of shell fragments, or does it include dust and soil?
31. How does the 2614 kg/h productivity rate scale if the machine is required to run continuously for 24-hour shifts?
32. How often do the drum screen holes require manual cleaning to prevent blockages from sticky fruit residue?
33. Does the paper provide a quantified cost-per-kg benefit to support the claim that this machine is more economical than laser sorters?
34. Is there a mathematical analysis of the safety overload protection for the conveyor belt in the event of a jam?
35. Would the optimal machine parameters change significantly for different varieties of camellia seeds from regions outside of Shuangfeng County?
36. How does the removable climbing robot module mentioned in the metadata impact the overall structural rigidity of the frame?
37. Does the cleaning rate of 52 remain consistent across all three fruit classification sizes?
38. Is there an energy-efficient alternative discussed for the 90-minute, 80 pretreatment process?
39. Is the 5mm–10mm gap adjustment range sufficient to handle the full variance of the 500 fruit samples measured?
40. What is the operational impact if the terrain slope exceeds the 20-30inclination range of the conveyor?
41. What specific high-speed camera or sensor hardware was used to verify the jumping behavior of the seeds in the cleaning stage?
42. Non-linear friction model must be included in the mathematical model. Cite this paper explaining the non-linear friction model: https://ieeexplore.ieee.org/abstract/document/9026949

 

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