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Article
Peer-Review Record

YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5

Agronomy 2023, 13(7), 1901; https://doi.org/10.3390/agronomy13071901
by Yaodi Li 1, Jianxin Xue 1,*, Mingyue Zhang 1, Junyi Yin 1, Yang Liu 1, Xindan Qiao 1, Decong Zheng 1 and Zezhen Li 2
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3:
Agronomy 2023, 13(7), 1901; https://doi.org/10.3390/agronomy13071901
Submission received: 27 June 2023 / Revised: 15 July 2023 / Accepted: 17 July 2023 / Published: 19 July 2023
(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture)

Round 1

Reviewer 1 Report

Please see attached document.

Comments for author File: Comments.pdf

There were several instances of grammatical mistakes. I have provide suggested revisions for those instances where the authors' meaning was readily interpretable and marked "grammar" for those that were ambiguous.

Author Response

Dear Reviewer, please see the attachment, thank you very much!

Author Response File: Author Response.pdf

Reviewer 2 Report

1. The research is to the scope of the journal.

2. There has detailed discussion of the methodology; in addition, the improvement as shown in Fig 7 shows the superiority of the proposed method. But can the improvement also be explained in the yields of strawberry harvesting? Adding a paragraph or two to highlight the contribution of proposed method in agronomy is strongly recommended.

Author Response

Dear reviewer, please see the attachment, thank you very much!

Author Response File: Author Response.pdf

Reviewer 3 Report

The authors have carried out an intense work on the detection of strawberry based on modified YOLOv5 algorithm. The paper has a well-designed research and technical infrastructure. But in order to have a final decision, the author(s) should do the following minor revisions:

v The abbreviation ‘ASFF’ need to be expanded in the title.

v At the end of the introduction, authors can highlight the outline of the paper in addition to the contribution of the work.

v Add specific explanation for the preprocessing of the acquired images.

v Authors can add more details on the implementation of the code to perform the analysis and library involved in this task.

v The parameters used in each algorithm should be mentioned explicitly.

v The results can be compared with the other algorithms and discussions can be given on each.

v The model architectures are explained. Add more training, parameter details to it.

v Discuss the major results. Capture some limitations spanning the intermediate results.

v The paper should indicate how the current work can be scaled up or can prove to be utilitarian for other kinds of work. The authors can suggest limitations while indicating the same.

 Minor editing of English language required

Author Response

Dear reviewer, please see the attachment, thank you very much!

Author Response File: Author Response.pdf

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