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Article
Peer-Review Record

Semi-Supervised Semantic Segmentation-Based Remote Sensing Identification Method for Winter Wheat Planting Area Extraction

Agronomy 2023, 13(12), 2868; https://doi.org/10.3390/agronomy13122868
by Mingmei Zhang 1, Yongan Xue 2, Yuanyuan Zhan 3 and Jinling Zhao 3,*
Reviewer 1: Anonymous
Reviewer 2:
Agronomy 2023, 13(12), 2868; https://doi.org/10.3390/agronomy13122868
Submission received: 27 October 2023 / Revised: 19 November 2023 / Accepted: 20 November 2023 / Published: 22 November 2023

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The quality of the English language is good enough for the manuscript.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors present an interesting paper proposing a Semi-Supervised Semantic Segmentation-Based method for extracting winter wheat planting areas. However, it is advisable to improve some aspects of the paper, namely:

1. - In the literature review, they should give more detail about the methods they use as comparative, as well as identify the limitations/constraints of each of them. Furthermore, substantiates the gains from its application to the case study.

2.- During the description of the method/approach presented by the authors, it would be interesting if they included a "structure of fusion architecture", or "the overall framework model".

3.- Furthermore, it is not clear when it is recommended to use the MPA and MIoU methods/techniques, is it related to some previously defined threshold "between each segment of image"? Is your application dependent on entropy?

Finally, congratulations on your work!

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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