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Article
Peer-Review Record

Early-Stage Sensor Data Fusion Pipeline Exploration Framework for Agriculture and Animal Welfare

AgriEngineering 2025, 7(7), 215; https://doi.org/10.3390/agriengineering7070215
by Devon Martin 1, David L. Roberts 2 and Alper Bozkurt 1,*
Reviewer 1:
Reviewer 2:
Reviewer 3: Anonymous
AgriEngineering 2025, 7(7), 215; https://doi.org/10.3390/agriengineering7070215
Submission received: 17 April 2025 / Revised: 13 June 2025 / Accepted: 18 June 2025 / Published: 3 July 2025

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript focuses on the design of data fusion pipelines for precision agriculture applications. While the study is highly relevant, the following comments must be addressed before the manuscript is accepted for publication.

  1. The authors have considered the use of fusion algorithms for precision agriculture for their literature review. However, other techniques, such as edge mining for precision agriculture, have not been considered in relation to their study. The authors are recommended to refer to the paper titled 'Precision farming: Sensor analytics' by S. Ivanov, et. al. and other work published by the authors.
  2. In the proposed data pipelines, the authors have evaluated 1-2 techniques for data pre-processing tasks. It is unclear why these methodologies were chosen over other existing techniques for tasks such as feature extraction, dimensionality reduction, and classification. A comparative analysis with other techniques would improve the robustness of the proposed work. 
  3. Since analyses typically vary by datasets and application, it would be useful to design frameworks that take as arguments different algorithms for each stage of the data fusion pipeline. This would allow for more flexibility in the analysis as opposed to hard-coding algorithms to be used. 
  4. The authors note that the "data fusion pipeline framework was evaluated using custom-designed pipelines applied to the four datasets". This is a significant limitation of the study as the proposed frameworks have only been evaluated for the datasets that were used for the design. While the datasets are diverse, it is difficult to comment on the applicability of the proposed work to unseen datasets. The authors must evaluate the framework on at least a couple of different types of datasets to validate their work.

Author Response

Please see the attached PDF for responses.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

See uploaded file

Comments for author File: Comments.pdf

Author Response

Please see the attached PDF for responses.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

please see the attached pdf file

Comments for author File: Comments.pdf

Author Response

Please see the attached PDF for responses.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have appropriately addressed reviewer's comments.

Author Response

Thank you for responding with your approval of our manuscript. 

Reviewer 2 Report

Comments and Suggestions for Authors

see attached file

Comments for author File: Comments.pdf

Comments on the Quality of English Language

in attached comments file

Author Response

Please see the attached file for a point-by-point response. Thank you

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors I am satisfied with how the authors have addressed my recommendations. So, I recommend accepting the paper in its  current form.

Author Response

Thank you for responding with your approval of our manuscript. 

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