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Article
Peer-Review Record

Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods

Remote Sens. 2022, 14(18), 4511; https://doi.org/10.3390/rs14184511
by Caiwang Zheng 1,2,*, Amr Abd-Elrahman 1,2, Vance Whitaker 1,3 and Cheryl Dalid 1,3
Reviewer 1:
Reviewer 2: Anonymous
Remote Sens. 2022, 14(18), 4511; https://doi.org/10.3390/rs14184511
Submission received: 26 July 2022 / Revised: 5 September 2022 / Accepted: 5 September 2022 / Published: 9 September 2022
(This article belongs to the Special Issue Digital Farming with Remote Sensing)

Round 1

Reviewer 1 Report

This is an interesting manuscript with UAV and multispectral sensors, and I believe it adds technical knowledge to remote sensing applications. This manuscript aimed to explore the biomass model of strawberries using UAV multispectral imagery with machine learning methods. Six regression methods and two types of variables were studied and evaluated for biomass prediction. I believe the manuscript will interest the audience of Remote Sensing Journal. Following specific comments that may help the authors to improve this manuscript.

 

1. The term vegetative biomass appears in the title, and fresh biomass is also mentioned in the manuscript. However, the author uses dry biomass most often in the manuscript. I want the authors to explain the relationship between these three terms in the introduction.

2. How do the authors understand the biomass and yield of strawberries? As a cash crop, we are more concerned with its yield than biomass.

3. The authors obtained UAV images at an altitude of 15m. Does the image obtained by the UAV at different flight altitudes have any effect on the results of this manuscript?

4. In figure 1a. Is it possible to replace this picture? I'd rather see a picture with a flying drone and the multispectral sensor. I feel there is no need to do a comparison experiment with UAV imagery and ground base imagery. I would like to see the picture of the ground-based equipment if the authors insisted on doing a comparative experiment.

5. In figure3. Is the module of CHM calculation not associated with other parts? Not enough arrows in this picture.

6. In 2.4.4. The authors should add clarification on the choice of the six regression models. Why use these six methods?

Author Response

We have addressed the reviewer’s comments carefully and made all required revisions. All the edits in the manuscript are tracked. The following is a detailed itemized list of of our responses to all reviewer’s comments. Please see the attachments

Author Response File: Author Response.docx

Reviewer 2 Report

Major revision is recommended. Please see the attached file.

Comments for author File: Comments.pdf

Author Response

We have addressed the reviewer’s comments carefully and made all required revisions. All the edits in the manuscript are tracked. The following is a detailed itemized list of of our responses to all reviewer’s comments. Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

see attached

Comments for author File: Comments.docx

Author Response

Thank you for the reviewer's suggestions. Please see the attachment.

Author Response File: Author Response.docx

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