Yield prediction for crops by gradient-based algorithms

Pavithra Mahesh; Rajkumar Soundrapandiyan

doi:10.1371/journal.pone.0291928

Peer Review History

Original SubmissionJune 6, 2023
4 Jul 2023 Decision Letter - Nebojsa Bacanin, Editor PONE-D-23-17460Yield Prediction for Crops by Gradient Based AlgorithmsPLOS ONE Dear Dr. Soundrapandiyan, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== ACADEMIC EDITOR: Dear Authors, please revise proposed manuscript thoroughly according to all reviewers' comments.Additionally, my own comments are provided below.All the best,AE ============================== Please submit your revised manuscript by Aug 18 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. 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Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. 5. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ Additional Editor Comments: Dear Authors, please revise proposed manuscript thoroughly according to all reviewers' comments. Additionally, please do the following: - Visualization of obtained results must be improved. - Motivation behind proposed research should be more clearly explain. Please elaborate what is "beyond state-of-the-art" of proposed. study. - To prove the significance of obtained results, statistical tests must be conducted. There are many statistical tests appropriate for validating results, please choose some tests from the following reference: https://www.sciencedirect.com/science/article/pii/S2210650211000034 - For the sake of clarity, best obtained metrics in each table should be marked e.g. by using the bold style. - Make sure that the source code is available according to PLOS ONE publication policies. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: No Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: This manuscript evaluates various machine learning algorithms used to predict product performance. The authors compare the results of three machine learning algorithms, CatBoost, LightGBM and XGBoost. Their results show that CatBoost has the highest accuracy in yield prediction. I went through the manuscript carefully. At the detailed level, the following notes are my suggestions: 1) Although the ABSTRACT structure is good, I suggest removing the first 5-6 lines and the last 3-4 lines. They are not really the descriptions that the reader expects to see in the ABSTRACT. 2) In my opinion, the INTRODUCTION section needs to be revised. In this section there should be three points: 1) motivation, 2) a summary of the challenges of previous studies, and 3) contribution. Also, the research contributions should be mentioned in a bullet-form at the end of the INTRODUCTION. 3) The INTRODUCTION section is too long. It is almost 10 pages! Instead, authors should reduce the INTRODUCTION to 2 pages. Then add a section called RELATED WORKS that provides a summary of previous studies. Also having subsection 1-1 doesn't help. Merge it with Section 1. 4) It is not clear to me which formulas were invented by the authors themselves and which ones are derived from other references. I found evidence that some formulas are derived from other references and there are similarities. 5) Authors should also use common supervised learning metrics such as accuracy, precision, and recall. For this purpose, I recommend adding the following reference and using the definitions of the above metrics from there: https://www.tandfonline.com/doi/abs/10.1080/0952813X.2022.2153279 6) There are still some grammatical errors in the manuscript. Authors should use software such as Grammarly for proof-checking. 7) The tense of the verbs in the CONCLUSION section must be past tense. In this section, the most important numerical improvements of the proposed method should be mentioned and marginal explanations should be avoided. Also, the suggestions mentioned for further research should be presented in a new paragraph. Reviewer #2: 1. Try to avoid using acronyms in the abstract. 2. Introduction is too long, please reduce it to max two pages. 3. Provide additional section - Background and related work, where you should provide literature survey. 4. Literature background must be expanded, as it is very limited. Include more recent relevant papers dealing with metaheuristics-based models, to provide stronger background on ML models. Include the following: https://www.mdpi.com/2305-6304/11/4/394 https://www.sciencedirect.com/science/article/abs/pii/S2352710222010555 https://peerj.com/articles/cs-956/ https://ieeexplore.ieee.org/abstract/document/9840700 https://link.springer.com/chapter/10.1007/978-981-19-7753-4_60 https://www.atlantis-press.com/proceedings/iciitb-22/125984192 5. Figures should be provided in much better quality. 6. Elaborate in more details why gradient boosting methods were selected for this task, among other ML models. 7. The proposed model should be explained in more details. 8. There are some errors in equations (square signs where operators should be). 9. Make sure that each parameter in every equation has been explained in the text. 10. Elaborate in more details how the hyperparameters for the models were selected. 11. Finally, I would recommend performing a statistical analysis, to establish if the obtained results are statistically significant in comparison to other methods. 12. Paper needs extensive proofreading, as there are numerous grammatical and language errors in the text. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0291928.r001
Revision 1
6 Sep 2023 Author Response • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers. • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf Author Response: We would like to thank the reviewer for pointing out this. We have revised the paper presented in PLOS ONE Format 2. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, all author-generated code must be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practices and facilitates reproducibility and reuse. Autor Response: Noted and the code will be shared after receiving acceptance for the article for publication 3. Thank you for stating the following financial disclosure: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” a) Please clarify the sources of funding (financial or material support) for your study. List the grants or organizations that supported your study, including funding received from your institution. b) State what role the funders took in the study. If the funders had no role in your study, please state: “The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.” c) If any authors received a salary from any of your funders, please state which authors and which funders. d) If you did not receive any funding for this study, please state: “The authors received no specific funding for this work.” Autor Response: No funding has been received for the study. We have mentioned this in the paper as advised. Please include your amended statements within your cover letter; we will change the online submission form on your behalf. 4. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. Author Response: The dataset used for this research is publicly and freely available on the internet. We described data availability in the cover letter. 5. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ Autor Response: The corresponding author (Rajkumar S ) has an ORCID iD: 0000-0001-5701-9325 Additionally, please do the following: -Visualization of obtained results must be improved. Autor Response: We have improved visualization of obtained results in the revised manuscript. - Motivation behind proposed research should be more clearly explain. Author Response: We would like to thank the reviewer for raising this important point in the motivation of the study. We have enhanced the motivation of the study where it reads; [#page 4] - Please elaborate what is "beyond state-of-the-art" of proposed. study. Author Response: The main intent of the study is to verify the effectiveness of the gradient methods and the algorithms are finely modified to avoid overfitting and underfitting. The same is mentioned in the Introduction. - To prove the significance of obtained results, statistical tests must be conducted. - There are many statistical tests appropriate for validating results, please choose some tests from the following reference: https://www.sciencedirect.com/science/article/pii/S2210650211000034 - For the sake of clarity, best obtained metrics in each table should be marked e.g. by using the bold style. - Make sure that the source code is available according to PLOS ONE publication policies. - [Note: HTML markup is below. Please do not edit.] Author Response: We would like to thank the reviewer for raising this important point .As per reviewer comments we have conducted one way ANOVA statistical test to prove the significants of obtained results. [Page#21-22]. Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Partly ________________________________________ 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No ________________________________________ 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ________________________________________ 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: No Reviewer #2: Yes ________________________________________ 5. Review Comments to the Author Reviewer #1: This manuscript evaluates various machine learning algorithms used to predict product performance. The authors compare the results of three machine learning algorithms, CatBoost, LightGBM and XGBoost. Their results show that CatBoost has the highest accuracy in yield prediction. I went through the manuscript carefully. At the detailed level, the following notes are my suggestions: 1) Although the ABSTRACT structure is good, I suggest removing the first 5-6 lines and the last 3-4 lines. They are not really the descriptions that the reader expects to see in the ABSTRACT. Author Response: We would like to thank the reviewer for raising this important point in the abstract of the study. We have enhanced the abstract of the study where it reads; Abstract: A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall & avg. temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost), Light Gradient-Boosting Machine LightGBM, and Extreme Gradient Boosting XGBoost are compared and found more accurate than other algorithms in predicting crop yields. The RMSE and R2 values were calculated to compare the predicted and observed rice yields, resulting in the following values: Cat-Boost with 800 (0.24), LightGBM with 737 (0.33), and XGBoost with 744 (0.31). Among these three machine learning algorithms, CatBoost demonstrated the highest precision in predicting yields, achieving an accuracy rate of 99.123%. 2) In my opinion, the INTRODUCTION section needs to be revised. In this section there should be three points: 1) motivation, 2) a summary of the challenges of previous studies, and 3) contribution. Also, the research contributions should be mentioned in a bullet-form at the end of the INTRODUCTION. Author Response: We would like to thank the reviewer for raising this important point in the motivation of the study. We have enhanced the motivation of the study where it reads; [Page#4] Motivation of the study is to develops an effective machine learning-based approach to crop yield prediction. Model a Prediction tool based on accurate crop yield predictions that can assist farmers and decision-makers about crop management, resource allocation, and risk mitigation strategies. With the uncertain weather patterns due to global warming, such a tool can be particularly useful in helping farmers adapt to changing conditions and ensure food security for the growing population. Author Response: Summary of the challenges of previous studies has been included in the revised manuscript [Page#3] Author Response: Contribution of the research have been mentioned in bullet-form at the end of the Introduction [Page#5] Author Response: The main contribution of the work presented in this paper is outlined below • Leveraging publicly available data on weather, agricultural practices, pesticides, and chemicals, a predictive model capable of accurately forecasting crop yields in India have been developed . • One-Hot Encoding has been used to convert categorical variables to the one-hot numeric array • Three different machine learning algorithms (CatBoost, LightGBM, and XGBoost) has been adopted in the model for achieving accurate prediction results for crops • The developed robust prediction framework has been modelled to effectively avoid overfitting and underfitting scenarios. 3) The INTRODUCTION section is too long. It is almost 10 pages! Instead, authors should reduce the INTRODUCTION to 2 pages. Then add a section called RELATED WORKS that provides a summary of previous studies. Also having subsection 1-1 doesn't help. Merge it with Section 1. Author Response: We would like to thank the reviewer for raising this important point in the introduction of the study. We have to reduce the introduction section in two pages [Page#2] Author Response: We have included the following articles in the related works [Page#5]. [9]Jeong JH, Resop JP, Mueller ND, Fleisher DH, Yun K, et al. (2016) Random Forests for Global and Regional Crop Yield Predictions. PLOS ONE 11(6): e0156571. https://doi.org/10.1371/journal.pone.0156571 [13]Jovanovic, G.; Perisic, M.; Bacanin, N.; Zivkovic, M.; Stanisic, S.; Strumberger, I.; Alimpic, F.; Stojic, A. Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate. Toxics 2023, 11, 394. https://doi.org/10.3390/toxics11040394 [14] Jui-Sheng Chou, Chi-Yun Liu, Handy Prayogo, Riqi Radian Khasani, Danny Gho, Gretel Gaby Lalitan, Predicting nominal shear capacity of reinforced concrete wall in building by metaheuristics-optimized machine learning, Journal of Building Engineering, Volume 61, 2022, https://doi.org/10.1016/j.jobe.2022.105046. [15] Zivkovic M, Tair M, K V, Bacanin N, Hubálovský Š, Trojovský P. 2022. Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification. PeerJ Computer Science 8:e956 https://doi.org/10.7717/peerj-cs.956 [16] A. Petrovic, I. Strumberger, M. Antonijevic, D. Jovanovic, D. Mladenovic and A. Chabbra, "Firefly-Xgboost Approach for Pedestrian Detection," 2022 IEEE Zooming Yield Prediction for Crops by Gradient Based Algorithms 19 / 19 Innovation in Consumer Technologies Conference (ZINC), Novi Sad, Serbia, 2022, pp. 197- 202, doi: 10.1109/ZINC55034.2022.9840700. [17] Jovanovic, L. et al. (2023). Tuning XGBoost by Planet Optimization Algorithm: An Application for Diabetes Classification. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems. Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_60 [18] Petrovic, A., Antonijevic, M., Strumberger, I., Jovanovic, L., Savanovic, N., & Janicijevic, S. (2023, January). The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection. In Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022) (Vol. 104, p. 219). Springer Nature. [23] Shook J, Gangopadhyay T, Wu L, Ganapathysubramanian B, Sarkar S, et al. (2021) Crop yield prediction integrating genotype and weather variables using deep learning. PLOS ONE 16(6): e0252402. https://doi.org/10.1371/journal.pone.0252402 4) It is not clear to me which formulas were invented by the authors themselves and which ones are derived from other references. I found evidence that some formulas are derived from other references and there are similarities. Author Response: The formulae are adopted from reference papers.[not invented by authors] The basic equations are similar/same and will not change. However we have framed objective function from the basic equations to suit crop prediction. 5) Authors should also use common supervised learning metrics such as accuracy, precision, and recall. For this purpose, I recommend adding the following reference and using the definitions of the above metrics from there: https://www.tandfonline.com/doi/abs/10.1080/0952813X.2022.2153279 Author Response: We agree with the reviewer, the metric mentioned are commonly used. But We have adopted the metrics used in reference paper to compare the results. Hence kindly excuse for not adding the reference recommended. 6) There are still some grammatical errors in the manuscript. Authors should use software such as Grammarly for proof-checking. Author Response: Thank you for pointing grammatical errors in our manuscript. We have carefully studied the manuscript and minimized the grammatical mistakes using Grammarly. 7) The tense of the verbs in the CONCLUSION section must be past tense. In this section, Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0291928.r002
26 Oct 2023 Decision Letter - Nebojsa Bacanin, Editor PONE-D-23-17460R1Yield Prediction for Crops by Gradient-Based AlgorithmsPLOS ONE Dear Dr. Soundrapandiyan, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Dec 10 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Nebojsa Bacanin Academic Editor PLOS ONE Additional Editor Comments: Dear Authors, please revise your manuscript carefully according to reviewers' comments. Thank you. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed Reviewer #3: (No Response) Reviewer #4: (No Response) Reviewer #5: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: (No Response) Reviewer #3: No Reviewer #4: Yes Reviewer #5: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: (No Response) Reviewer #3: No Reviewer #4: Yes Reviewer #5: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: (No Response) Reviewer #3: No Reviewer #4: Yes Reviewer #5: No ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: (No Response) Reviewer #3: No Reviewer #4: Yes Reviewer #5: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors appreciated my comments. I have no other comment. From my husband, this manuscript can be published. Reviewer #2: (No Response) Reviewer #3: The article presents a study on Yield Prediction for Crops by Gradient Based Algorithms. In comparison of various other studies published on crop prediction in other journals, I found that this article lacks novelty in terms of originality and technicality. The methods used for crop analysis is very trivial and outdated. Moreover, the results do not provide any useful information for agriculture domain as well. I would suggest a rejection. Reviewer #4: 1. Introduction may be improved, adding the highlights and the problem statements. 2. You could improve writing, link better the ideas flow in the Introduction. 3. Review references because some of them are unstandardized. 4. The conclusion needs improvements towards major claimed contribution. 5. Write some future directions in the conclusion section. Reviewer #5: The comparative assessment of different models may be statistically tested. Authors may use Diebold Mariano test for this purpose. you may see following paper https://doi.org/10.1371/journal. pone.0270553 MAPE (mean absolute prediction error) may also be reported. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No Reviewer #4: No Reviewer #5: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0291928.r003
Revision 2
7 Dec 2023 Author Response Response to Reviewers Manuscript Number: PONE-D-23-17460R1 Title: Yield Prediction for Crops by Gradient-Based Algorithms PLOS ONE Dear editors and reviewers, Thank you for consideration and for your efforts to bring the work in better way. We hereby submit a revised version of the manuscript that addresses the points raised during the review process. Please find below our response to each point raised by the academic editor and reviewer(s). We here by submit the below documents along with response to reviewers: � A separate file labelled 'Revised Manuscript with Track Changes' is uploaded. The marked-up copy of your manuscript that highlights changes made to the original version. � A separate file labelled 'Manuscript' An unmarked version of the revised paper without tracked changes. 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict-of-interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed Reviewer #3: (No Response) Reviewer #4: (No Response) Reviewer #5: All comments have been addressed Author Response: No response required for the comment, since reviewers have concurred for addressing the comments ________________________________________ 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: (No Response) Reviewer #3: No Reviewer #4: Yes Reviewer #5: Yes Author Response: Performance Evaluation of Catboost, XGBoost, and LightGBM by Diebold-Mariano test for comparison has been additionally included and except one reviewer, other feel the results presented in the paper is adequate. ________________________________________ 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: (No Response) Reviewer #3: No Reviewer #4: Yes Reviewer #5: Yes Author Response: Statistical analysis by Diebold-Mariano test has been additionally included and except one reviewer, other feel the results presented in the paper is adequate. ________________________________________ 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: (No Response) Reviewer #3: No Reviewer #4: Yes Reviewer #5: No Author Response: Dataset obtained from third party used for experimentation has been referred in the paper [24] and results obtained during experimentation have been recorded fully in the paper. If information in specific is required, kindly mention. The authors are willing to send. ________________________________________ 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: (No Response) Reviewer #3: No Reviewer #4: Yes Reviewer #5: Yes Author Response: It can be noted the reviewers (except Reviewer #3) considered the paper is written in standard English and in better manner. Considering the Reviewer #3 response, we have made few corrections for better understanding and reading. 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors appreciated my comments. I have no other comment. From my husband, this manuscript can be published. Reviewer #2: (No Response) Reviewer #3: The article presents a study on Yield Prediction for Crops by Gradient Based Algorithms. In comparison of various other studies published on crop prediction in other journals, I found that this article lacks novelty in terms of originality and technicality. The methods used for crop analysis is very trivial and outdated. Moreover, the results do not provide any useful information for agriculture domain as well. I would suggest a rejection. Author Response: Kindly note, our intent is to investigate the capability of gradient methods for crop predictions. The experimentation results provide the proof for capacity of gradient methods. The results give us confident to enhance the model considering below aspects to reach a complete frame work. • Integrated Framework Development • Incorporation of Additional Environmental Factors • Enhanced Predictive Models • Continuous Environmental Monitoring System • Exploration of Multi-Crop Recommendations • Integration with Precision Agriculture Technologies • Community Engagement and Farmer Education • Long-Term Impact Assessment Reviewer #4: 1. Introduction may be improved, adding the highlights and the problem statements. Author Response: We would like to thank the reviewer for pointing out this. Problem statement with highlights of the work included in introduction of the revised paper [Page#3]. 2. You could improve writing, link better the ideas flow in the Introduction. Author Response: We would like to thank the reviewer for raising this important point in the introduction, the paper starts with aspects of framing, problem statement (now included), continued with a brief about IOT and data Analysis, motivation of the study, contributions and content covered in sessions of the paper. A paragraph of details to link the flow of ideas has been included. 3. Review references because some of them are unstandardized. Author Response: We would like to thank the reviewer for raising this important point. As per reviewer comments we have included references from standard journals and revised the paper [Page#28-31]. 4. The conclusion needs improvements towards major claimed contribution. Author Response: As per the reviewer’s suggestion we have improved the conclusion toward major claimed contribution. [Page#26-27] 5. Write some future directions in the conclusion section. Author Response: As per the suggestion of the reviewer the future directions have been included in conclusion section. [Page#26-27] Reviewer #5: The comparative assessment of different models may be statistically tested. Authors may use Diebold Mariano test for this purpose. you may see following paper https://doi.org/10.1371/journal. pone.0270553 MAPE (mean absolute prediction error) may also be reported. Author Response: Thank you for raising this important comment. The comparative assessment of different models has been statistically tested by Diebold-Mariano test in revised paper [Page# 21-22]. Moreover, as per reviewer’s suggestion, we have included mean absolute percentage error [Page#19-20]. 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No Reviewer #4: No Reviewer #5: No ________________________________________ Author Response: Noted Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0291928.r004
1 Feb 2024 Decision Letter - Nebojsa Bacanin, Editor Yield Prediction for Crops by Gradient-Based Algorithms PONE-D-23-17460R2 Dear Dr. Soundrapandiyan, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Nebojsa Bacanin Academic Editor PLOS ONE Additional Editor Comments (optional): Dear Authors, thank you for revising your manuscript. Warmest, AE Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #4: All comments have been addressed Reviewer #5: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #4: Yes Reviewer #5: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #4: Yes Reviewer #5: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #4: Yes Reviewer #5: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #4: Yes Reviewer #5: (No Response) ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #4: All my concerns have been addressed. In the revised version of the manuscript, the authors met all the requirements and comments given in the previous review, so I recommend this paper for publishing. Reviewer #5: The suggestions have been incorporated by the authors in the revised manuscript. The paper is improved signfiicantly ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #4: No Reviewer #5: No ******** https://doi.org/10.1371/journal.pone.0291928.r005
Formally Accepted
29 Mar 2024 Acceptance Letter - Nebojsa Bacanin, Editor PONE-D-23-17460R2 PLOS ONE Dear Dr. Soundrapandiyan, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Nebojsa Bacanin Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0291928.r006

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