Measurement of Overlapping Leaf Area of Ice Plants Using Digital Image Processing Technique
Round 1
Reviewer 1 Report (Previous Reviewer 1)
The manuscript has been improved and my concerns have been addressed.
Author Response
Thank you very much for the valuable suggestion and comments.
Author Response File: 
Author Response.docx
Reviewer 2 Report (Previous Reviewer 2)
Despite the manuscript type is “Communication” rather than “Article”, I evaluated this manuscript with the standard of a regular journal article. This is a poorly prepared manuscript that does not deserve to be a journal publication. Very little effort was put into the revised manuscript, which did not improve the manuscript quality in any meaningful way. The revised manuscript has the same fundamental issues as before: no novelty, little workload, vague descriptions, short discussion, etc. The image processing work and experiments involved in the study is far from something that can be considered research due to their simple and non-novel nature. I do not believe the manuscript can be revised into a publishable form without significant new experiment design and execution and extensive new writings.
Author Response
Thank you very much for the valuable suggestion and comments. I will plan to investigate your suggestion in future using deep learning algorithms. Even though, the novelty of this research is the overlapping percentage of ice plants using a destructive and non-destructive method. Moreover, this investigation proposed an image segmentation algorithm to classify the ice plant canopy image with a threshold segmentation technique by grey colour model and calculating the degree of green colour in the HSV model. The novelty of the method is also manifested in all images that were transformed from RGB to HSV colour space to wipe out the noise introduced from the background.
Author Response File: Author Response.docx
Reviewer 3 Report (New Reviewer)
The manuscript, entitled "Measurement of overlapping leaf area of ice plants using digital image processing technique" presents the development of a new non-destructive leaf area estimation method.
This investigation proposed an image segmentation algorithm to classify the ice plant (Mesembryanthemum crystallinum L.) canopy image with a threshold segmentation technique by grey colour model and calculating the degree of green colour in the HSV (huge saturation value) model. The novelty of the method is also manifested in all images were transformed from RGB to HSV colour space to wipe out the noise introduced from the background. The description of the method and the presentation of its theoretical foundations are sufficiently detailed. The development results can improve the efficiency and accuracy of the leaf surface estimation tests of individual plants.
In addition to the excellent informative value of the manuscript, I consider it a shortcoming that the Conclusion chapter lacks, on the one hand, a systematic summary of its own results, and on the other hand, a more detailed comparison with similar new developments.
Author Response
Reply: Thank you very much for the valuable suggestion and comments. I addressed the conclusion part and fill the shortcoming by adding your paragraphs and highlighting some restrictions and novelty of the proposed method.
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report (Previous Reviewer 2)
I expect significant modifications in the material, methods, results and discussion sections based on my previous comments, while only introduction was updated in the revised manuscript. As I do not see my previous comments were being sufficiently addressed, my recommendation remains the same.
Author Response
Dear Reviewer
Journal - Agriculture
Manuscript ID: agriculture-1793779
Status: Major revision
I am Bolappa Gamage Kaushalya Madhavi. The first author of the above-mentioned article. That was reconsidered by the editor as a major revision. We are thankful to the editor and reviewers for carefully reviewing the manuscript and for the constructive suggestions offered. We greatly appreciate the valuable efforts of the reviewers to improve manuscript quality. A revision of the manuscript has been carried out with me and redeveloped the manuscript by addressing the comments given by reviewers. I believe that the revised version has been significantly improved and is suitable for consideration for publication in the MDPI Agriculture Journal.
We have done our best to address all of the points raised. Each comment is followed by the corresponding reply, which is highlighted in green colour. In addition, we made corrections and clarifications as the reviewers suggested in the revised manuscript.
Reviewer 2
I expect significant modifications in the material, methods, results and discussion sections based on my previous comments, while the only introduction was updated in the revised manuscript. As I do not see my previous comments being sufficiently addressed, my recommendation remains the same.
Reply: Thank you very much for the valuable suggestion and comments. I improved the introduction, materials and methods, results and discussion and conclusion. I added new figures (Figures number 5 and 6) and also a validation procedure and found the R2 value for the correlation between canopy area and total leaf area. All new paragraphs that I newly added are highlighted in green colour. Could you please check it, again? If need more to add, please guide me. I kindly request you to give your comments to improve further without rejecting this manuscript.
(Materials and methods: lines 157-158; 180-182; 206-208; 210-211; 249-250; 264-275; 278-294; 361-363; 367-375; 381-388; 390-397)
Author Response File: Author Response.docx
This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.
Round 1
Reviewer 1 Report
The author proposed an image segmentation to classify the ice plant and measure overlapping leaf area. The use of threshold-based segmentation is not new but the idea of measuring overlapping leaf areas is potentially interesting. I recommend the authors focus on the analysis of overlapping leaf areas as it might relate to the light interception, vegetation photosynthesis, and foliage clumping effect.
1. The significance of overlapping leaf areas should be emphasized in the introduction. And the background of indirect leaf area measurement should be reviewed as this paper is studying the leaf area!
Ryu, Y., Sonnentag, O., Nilson, T., Vargas, R., Kobayashi, H., Wenk, R., Baldocchi, D.D., 2010. How to quantify tree leaf area index in an open savanna ecosystem: A multi-instrument and multi-model approach. Agricultural and Forest Meteorology 150, 63-76.
Yan, G., et al. (2019). "Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives." Agricultural and Forest Meteorology 265: 390-411.
2. The novelty of the study needs to be clearly stated. As the canopy image was segmented according to a threshold-based method proposed by previous studies.
Reviewer 2 Report
The manuscript described an application of color thresholding segmentation for leaf area estimation.
I see no novelty in the study. The segmentation method is simple, and has no innovation and very limited applications (e.g. only in greenhouse with colored LEDs).
The manuscript can be inconvenient to read. I caught a few places that either have grammar errors or are not making sense. For example, line 9-10 “are replaced” should be “are replacing”, Line 12 “presenting” makes no sense, line 48 “implement many leaves” should be “implement for many leaves”, line 63 “the limitation associated with this formula is not uniform” makes no sense.
The knowledge gap in current literature is not identified. Research questions and objectives are not specified. The necessity of the study is not justified.
I also noticed some places where false claims are made. For example, line 66-67, the authors said “Therefore, compared to destructive measurements, accuracy is high in nondestructive approaches”, while the accuracy evaluation of nondestructive methods typically relies on the results of destructive methods as ground truths. I am unsure what the authors’ claim is based on. Line 124, converting image to HSV from RGB itself doesn’t change anything, which certainly doesn’t reduce noise.
Methods are vaguely described. For example, line 124-137, no numeric details were given for other researchers to replicate the method.
