Next Article in Journal
Application of Machine Learning in the Quantitative Analysis of the Surface Characteristics of Highly Abundant Cytoplasmic Proteins: Toward AI-Based Biomimetics
Next Article in Special Issue
Reinforcement Learning with Task Decomposition and Task-Specific Reward System for Automation of High-Level Tasks
Previous Article in Journal
Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators
Previous Article in Special Issue
Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning
 
 
Article
Peer-Review Record

Autonomous Robotic System to Prune Sweet Pepper Leaves Using Semantic Segmentation with Deep Learning and Articulated Manipulator

Biomimetics 2024, 9(3), 161; https://doi.org/10.3390/biomimetics9030161
by Truong Thi Huong Giang 1 and Young-Jae Ryoo 2,*
Reviewer 1:
Reviewer 2:
Biomimetics 2024, 9(3), 161; https://doi.org/10.3390/biomimetics9030161
Submission received: 30 January 2024 / Revised: 27 February 2024 / Accepted: 28 February 2024 / Published: 5 March 2024
(This article belongs to the Special Issue Artificial Intelligence for Autonomous Robots 2024)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

see pdf

Comments for author File: Comments.pdf

Comments on the Quality of English Language

English language is fine. Only minor issues detected.

Author Response

Thanks for the reviewer's comments.

We do our best to revise the manuscript.

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript is relevant and interesting, it is devoted to a robotic system for pruning sweet pepper leaves based on image processing using a neural network.

The manuscript is recommended for printing in this journal, but to improve the manuscript, I recommend paying attention to some points.

1. The manuscript indicates that the dataset was collected in various greenhouses, please indicate the names of the varieties of pepper from which the dataset was removed.

2. Although you have indicated links to other manuscripts related to this study concerning the neural network, in this manuscript I recommend adding graphs and calculation of well-known metrics for the quality of the neural network (Precision, Recall, F1-score), analysis of the loss function depending on the learning epoch.

3. It is proposed to expand the conclusion and compare the results obtained with other similar works,

4. It is also recommended to present the results of the full-scale experiment in more detail.

Author Response

Thanks for the reviewer's comments.

We do our best to revise the manuscript.

Please see the attachment.

Author Response File: Author Response.pdf

Back to TopTop