Svoboda | Graniru | BBC Russia | Golosameriki | Facebook
Next Article in Journal
Probabilistic Chain-Enhanced Parallel Genetic Algorithm for UAV Reconnaissance Task Assignment
Next Article in Special Issue
Predicting Grape Yield with Vine Canopy Morphology Analysis from 3D Point Clouds Generated by UAV Imagery
Previous Article in Journal
Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment
Previous Article in Special Issue
Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning
 
 
Review
Peer-Review Record

Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms

by Takashi Sonam Tashi Tanaka 1, Sheng Wang 1, Johannes Ravn Jørgensen 1, Marco Gentili 1, Armelle Zaragüeta Vidal 2, Anders Krogh Mortensen 3, Bharat Sharma Acharya 4, Brittany Deanna Beck 1 and René Gislum 1,*
Reviewer 1: Anonymous
Reviewer 3:
Submission received: 29 April 2024 / Revised: 15 May 2024 / Accepted: 16 May 2024 / Published: 21 May 2024
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The review presents a comprehensive coverage on the topic of using UAV for crop phenotyping. Various sensors, applications and types of UAV used are discussed. This review was created with the goal of educating the reader to current trends in research. Overall, this is a very impressive review in terms of information covered and presentation and can be accepted after the following minor changes are done.

In long reviews such as this one, it is advisable to add a small section in the Introduction that describes the structure of the study.

For example, Section 1 covers XXX, Section 2 describes XXX, Section 3 provides concluding statements….

Is the LiDAR acronym (Light Detection and Ranging) shown anywhere in-text?

Section 3.5 does not mention the expansion of the acronym.

It is unclear how sub-sections of section 5 “Algorithms” fit under this section.

Why is sub-section 5.2 “Ground sampling distance” included under section 5: Algorithms? They are not algorithms.

Similar query for section 5.1, 5.3 and 5.4

“Microwave sensors” should be section 3.6 and not 3.1.

I am slightly concerned with the authors interpretation of figure 5.

As acknowledged by them, they have taken the figure from ref [56] and modified it.

https://www.sciencedirect.com/science/article/pii/S1010603017309127

However, I feel they have simply cropped a part of the original figure and presented it as "modified" figure. The authors need to create their own version of this figure in a drawing tool and then acknowledge ref [56].

Has figure 6 been produced by the authors or has it been similarly derived from an existing source?

 

Author Response

Responses to Reviewer 1’s comments

General comments: The review presents a comprehensive coverage on the topic of using UAV for crop phenotyping. Various sensors, applications and types of UAV used are discussed. This review was created with the goal of educating the reader to current trends in research. Overall, this is a very impressive review in terms of information covered and presentation and can be accepted after the following minor changes are done.

Response: We appreciate your careful review of our manuscript.

 

Comment 1: In long reviews such as this one, it is advisable to add a small section in the Introduction that describes the structure of the study. For example, Section 1 covers XXX, Section 2 describes XXX, Section 3 provides concluding statements….

Response: The outlines of each section are presented as suggested (line 88–95).

 

Comment 2: Is the LiDAR acronym (Light Detection and Ranging) shown anywhere in-text? Section 3.5 does not mention the expansion of the acronym.

Response: The abbreviation was moved to first use (line 54).

 

Comment 3: It is unclear how sub-sections of section 5 “Algorithms” fit under this section. Why is sub-section 5.2 “Ground sampling distance” included under section 5: Algorithms? They are not algorithms. Similar query for section 5.1, 5.3 and 5.4

Response: We’ve changed the heading for section 5 as “Extracting phenotypic traits from UAV image” and added a small introduction under the section (line 314316­­).

 

Comment 4: “Microwave sensors” should be section 3.6 and not 3.1.

Response: The section number has been revised as suggested (line 227).

 

Comment 5: I am slightly concerned with the authors interpretation of figure 5.

As acknowledged by them, they have taken the figure from ref [56] and modified it.

https://www.sciencedirect.com/science/article/pii/S1010603017309127

However, I feel they have simply cropped a part of the original figure and presented it as "modified" figure. The authors need to create their own version of this figure in a drawing tool and then acknowledge ref [56].

Response: The figure 5 has been deleted after we carefully consider the necessity of this according to the comments from another reviewer

 

Comment 6: Has figure 6 been produced by the authors or has it been similarly derived from an existing source?

Response: Figure 6 is modified from https://ncsu-geoforall-lab.github.io/uav-lidar-analytics-course/lectures/2017_Flight_planning.html#/

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This review can guide new practitioners aiming to implement and use UAV-based phenotyping.

The work is well organized and exhaustively described.

 

Author Response

Response to Reviewer 2’s comments

Comment 1: This review can guide new practitioners aiming to implement and use UAV-based phenotyping.

The work is well organized and exhaustively described.

Response: Thank you for your review.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors


Comments for author File: Comments.pdf

Comments on the Quality of English Language

The English language is fine.

Author Response

# Line numbers correspond to the number in All Markup (track changes).

 

Responses to Reviewer 3’s comments

General comments: This review summarizes UAV platform, sensor, and data processing advances to demonstrate how high-throughput phenotyping could revolutionize crop breeding. UAV-based phenotyping is more accessible and useful because of enhanced software and sensors, enabling agricultural science research and applications.

Integrating different sensing inputs and machine learning techniques to improve forecast accuracy highlights the field's creative approaches. This study helps new practitioners use UAV-based phenotyping in their research and applications. It provides a moderate level of understanding of the UAVs applications. However, some significant changes can enhance the quality of the manuscript.

Response: Thank you for your constructive suggestions on our manuscript.

 

Comment 1: (a) Mention in one paragraph the clear objectives. (b) Create an order and reasonable symmetry in the whole manuscript, better to give a chart. (c) Signify the objectives shortly.

Response: To create an order and symmetry in our manuscript, the outlines of each section have been described in the end of the introduction (see line 88–95). We believe that our manuscript has already presented clear objectives as written in lines 68–72 and 82–87. However, several redundant sentences would make it difficult to signify the objectives, so we decided to move or delete those sentences (moved to line 52–54 and deleted from 72–82). We now hope the reviewer would agree the objectives are clear.

 

Comment 4: (d) Give future implications

Response: Future implications are presented as follows:

  • This will raise the necessity of developing efficient and easy-to-use data transfer and sharing technologies specifically for UAV-based phenotypic dataset from multiple da-ta repositories or research outputs (see lines 644–646).
  • This will undoubtfully may help hypotheses generation of what crop physiological traits should be taken into account for developing precise and interpretable machine learning models beneficial for crop breeding. Future studies on UAV phenotyping and machine learning should focus not only on the prediction accuracy but also on interpretability of collected data (newly added sentence, lines 670–674).
  • Expanding the application of UAV-based phenotyping in minor crops or mixed cropping will be a prospective research direction (see lines 675–676).

 

Comment 5: (e) Give flow charts for the sections 4 and 5

Response: Currently, we have a lot of figures. To facilitate understanding of the overview on each section, brief description has been added just after the headings (lines 245–248 and 314–316).

 

Specific comments:

Comment 6: Fig.3: Revise this

Response: The resolution and font size have been increased.

 

Comment 7: Fig.5: What is the purpose of showing this figure? How have you related this to UAVs?

Response: We have carefully discussed it and decided to delete it without losing important information.

 

Comment 8: Section 6.1: It seems repeated heading with the previous section

Response: We are not sure which section are referred to. Headline. 6.1 differs from 6. Maybe, the reviewer is referring to 4.3, but the heading is still different. Could you please elaborate which one is referred to?

 

Comment 9: Revise the conclusion section, Make it short.

Response: Two redundant sentences have been deleted to make it shorter (lines 697699 and 702703).

Author Response File: Author Response.docx

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have well revised the manuscript.

Back to TopTop