Use of deep learning for structural analysis of computer tomography images of soil samples

Soil samples from several European countries were scanned using medical computer tomography (CT) device and are now available as CT images. The analysis of these samples was carried out using deep learning methods. For this purpose, a VGG16 network was trained with the CT images (X). For the annotation (y) a new method for automated annotation, ‘surrogate’ learning, was introduced. The generated neural networks (NNs) were subjected to a detailed analysis. Among other things, transfer learning was used to check whether the NN can also be trained to other y-values. Visually, the NN was verified using a gradient-based class activation mapping (grad-CAM) algorithm. These analyses showed that the NN was able to generalize, i.e. to capture the spatial structure of the soil sample. Possible applications of the models are discussed.

-clear formulation of the question -methods: very brief but well comprehensible; more detailed description for clarification of the research question, however, not at all necessary -results very well understandable; only 32 samples are critical for a serious discussion of the results. However, the authors themselves cite this point of criticism. But: As a feasibility study, the paper impressively shows the potential of the described approach. minor changes proposed: 1. p.7line13 "a proxy for (y)" ??? sorry, I do not understand 2. structure analysis: explained in prose only, some formalization would make this step more clear 3. in the figures: add precise scale and description to the axis; give some more words for the figure titles! Decision letter (RSOS-201275.R0) We hope you are keeping well at this difficult and unusual time. We continue to value your support of the journal in these challenging circumstances. If Royal Society Open Science can assist you at all, please don't hesitate to let us know at the email address below.

Dear Dr Wieland
On behalf of the Editors, we are pleased to inform you that your Manuscript RSOS-201275 "Use of Deep Learning for structural analysis of CT-images of soil samples" has been accepted for publication in Royal Society Open Science subject to minor revision in accordance with the referees' reports. Please find the referees' comments along with any feedback from the Editors below my signature.
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Reviewer comments to Author: Reviewer: 1 Comments to the Author(s) The authors proposed a nice work to capture soil structure by deep learning based on computer tomography (CT) images of soil samples from several European countries. The study introduced novel techniques to enhance the ability of artificial intelligence (AI) to detect soil structure metrics, which would be interested for both soil and AI scientists. Some comments are as follows.
Authors could provide some comments on the advantages of deep learning comparing to other machine learning methods. The necessity and significance of deep learning for soil structure analysis should be further highlighted.
At the beginning of the Introduction, some background and importance of soil structure should be mentioned.
The first paragraph of Results seems more like methods, which could be considered to move the Methods. Similarly, the first mentioning of grad-CAM should also be done in Methods. The language should be further checked, for example, in the third paragraph of page 4, the "land" should be removed in "grassland land soil ...".
Reviewer: 2 Comments to the Author(s) general remarks: -very pragmatic approach in the interdisciplinary field between soil science and AI learning methods -clear formulation of the question -methods: very brief but well comprehensible; more detailed description for clarification of the research question, however, not at all necessary -results very well understandable; only 32 samples are critical for a serious discussion of the results. However, the authors themselves cite this point of criticism. But: As a feasibility study, the paper impressively shows the potential of the described approach. minor changes proposed: 1. p.7line13 "a proxy for (y)" ??? sorry, I do not understand 2. structure analysis: explained in prose only, some formalization would make this step more clear 3. in the figures: add precise scale and description to the axis; give some more words for the figure titles! ===PREPARING YOUR MANUSCRIPT=== Your revised paper should include the changes requested by the referees and Editors of your manuscript. You should provide two versions of this manuscript and both versions must be provided in an editable format: one version identifying all the changes that have been made (for instance, in coloured highlight, in bold text, or tracked changes); a 'clean' version of the new manuscript that incorporates the changes made, but does not highlight them. This version will be used for typesetting. Please ensure that any equations included in the paper are editable text and not embedded images.
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Decision letter (RSOS-201275.R1)
We hope you are keeping well at this difficult and unusual time. We continue to value your support of the journal in these challenging circumstances. If Royal Society Open Science can assist you at all, please don't hesitate to let us know at the email address below.

Dear Dr Wieland,
It is a pleasure to accept your manuscript entitled "Use of Deep Learning for structural analysis of CT-images of soil samples" in its current form for publication in Royal Society Open Science. The comments of the reviewer(s) who reviewed your manuscript are included at the foot of this letter.
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Thank you for your fine contribution. On behalf of the Editors of Royal Society Open Science, we look forward to your continued contributions to the Journal. Dear Liane, dear Andrew, thank you both for your great support during the submission process. Only with your help was I able to complete the work. I have revised the work according to the suggestions of the reviewers. They have also done a great job. Please also address my thanks to Professor Peter Haynes for his supervision of the revision process.
Dear Reviewer: 1, many thanks for your review and your helpful comments. They helped me a lot to make the paper better.
Comments to the Author(s) The authors proposed a nice work to capture soil structure by deep learning based on computer tomography (CT) images of soil samples from several European countries. The study introduced novel techniques to enhance the ability of artificial intelligence (AI) to detect soil structure metrics, which would be interested for both soil and AI scientists. Some comments are as follows.
Authors could provide some comments on the advantages of deep learning comparing to other machine learning methods. The necessity and significance of deep learning for soil structure analysis should be further highlighted.
-Deep learning is the only way to find the structures in a soil sample that determine its physical properties, in this case the flow and storage of water. I have added a comment in the paper.
At the beginning of the Introduction, some background and importance of soil structure should be mentioned.
-I have added a comment and a citiation.
The first paragraph of Results seems more like methods, which could be considered to move the Methods. Similarly, the first mentioning of grad-CAM should also be done in Methods.
-I have moved the grad-CAM into the method section. The language should be further checked, for example, in the third paragraph of page 4, the "land" should be removed in "grassland land soil ...".
-I changed it.
Many thanks again, Ralf Wieland.

Appendix A
Dear Reviewer: 2, many thanks for your review and your helpful comments. They helped me a lot to make the paper better.
Comments to the Author(s) general remarks: -very pragmatic approach in the interdisciplinary field between soil science and AI learning methods -clear formulation of the question -methods: very brief but well comprehensible; more detailed description for clarification of the research question, however, not at all necessary -results very well understandable; only 32 samples are critical for a serious discussion of the results. However, the authors themselves cite this point of criticism. But: As a feasibility study, the paper impressively shows the potential of the described approach. Dear Reviewer: 1, many thanks for your review and your helpful comments. They helped me a lot to make the paper better.
Comments to the Author(s) The authors proposed a nice work to capture soil structure by deep learning based on computer tomography (CT) images of soil samples from several European countries. The study introduced novel techniques to enhance the ability of artificial intelligence (AI) to detect soil structure metrics, which would be interested for both soil and AI scientists. Some comments are as follows.
Authors could provide some comments on the advantages of deep learning comparing to other machine learning methods. The necessity and significance of deep learning for soil structure analysis should be further highlighted.
-Deep learning is the only way to find the structures in a soil sample that determine its physical properties, in this case the flow and storage of water. I have added a comment in the paper.
At the beginning of the Introduction, some background and importance of soil structure should be mentioned.
-I have added a comment and a new citiation.
The first paragraph of Results seems more like methods, which could be considered to move the Methods. Similarly, the first mentioning of grad-CAM should also be done in Methods.
-I have moved the grad-CAM into the method section. The language should be further checked, for example, in the third paragraph of page 4, the "land" should be removed in "grassland land soil ...".
-I changed it.
Many thanks again, Ralf Wieland.