Design and Optimization of Sugarcane Spiral Fertilizer Applicator Based on Response Surface Methodology and Artificial Neural Networks
Round 1
Reviewer 1 Report
Please improve the sections/paragraphs concerning with the "research design" and the "methods".
Comments for author File: 
Comments.pdf
I think that a moderate editing of English language is required.
Author Response
Dear Editor and Reviewers,
Thanks for providing us with this great opportunity to submit a revised version of our manuscript. We appreciate the detailed and constructive comments provided by the reviewers. We have carefully revised the manuscript by incorporating all the suggestions from the review panel.
We tried our best to polish the language in the revised manuscript and made some changes to the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked them in blue in the revised paper. We appreciate for Reviewers’ warm work earnestly and hope that the correction will meet with approval.
We also appreciate your clear and detailed feedback and hope that the explanation has fully addressed all of your concerns.
Thank you for the editor’s time and consideration.
Sincerely,
The Authors
Author Response File: Author Response.pdf
Reviewer 2 Report
I would like to extend my appreciation to the authors for their comprehensive and practical analysis on the Design and Optimization of the Sugarcane Spiral Fertilizer Applicator, using both Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). The depth with which both RSM and ANN methodologies have been articulated is commendable.
To enhance the paper's contribution further I would suggest that it might be valuable to explore a hybrid approach that integrates the strengths of RSM and ANN. Such an approach could leverage the insights from RSM, especially in feature selection, based on the significance of experimental factors. Moreover, the interaction effects identified via RSM can be ingeniously used to craft additional features for the ANN. This hybrid strategy not only stands to amplify the model's predictive prowess but also bolsters its interpretability.
Author Response
Dear Editor and Reviewers,
Thanks for providing us with this great opportunity to submit a revised version of our manuscript. We appreciate the detailed and constructive comments provided by the reviewers. We have carefully revised the manuscript by incorporating all the suggestions from the review panel.
We tried our best to polish the language in the revised manuscript and made some changes to the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked them in blue in the revised paper. We appreciate for Reviewers’ warm work earnestly and hope that the correction will meet with approval.
We also appreciate your clear and detailed feedback and hope that the explanation has fully addressed all of your concerns.
Thank you for the editor’s time and consideration.
Sincerely,
The Authors
Author Response File: Author Response.pdf
