Using Canopy Measurements to Predict Soybean Seed Yield

Round 1

Reviewer 1 Report

In this Manuscript, the authors tried to determine if measurements of canopy development could be used to predict soybean yield, and if canopy measurements can predict yield, determine the most accurate and most practical strategy for yield prediction.

The outset of the paper is good, but in its current form, it lacks the scientific evidence needed to support its hypothesis

Overall, this is a clear, concise, and well-written manuscript. Unfortunately, the authors missed the discussion section

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

The paper tried to investigate the possible use of different secondary yield-related traits such as canopy, PAR, and NDVI as a good indicator, for predicting soybean yield at the R5 growth stage using different regression methods. The results indicated the efficiency of using stepwise regression in predicting soybean seed yield using different traits. The results seem to be promising for soybean producers to predict yield at the early growth stage. However, there are some points that need to be clarified before accepting the paper for publishing in this journal.

• There are different types of NDVI that authors should discuss the NDVI that they used. Also, they should provide more information about the use of NDVI in predicting soybean yield at R5. They can use the suggested references for more information.
• It would be great to mention the total number of plots in the M&M
• Why you used two regression methods to predict soybean yield? did you have any criteria in your mind when you chose to work with those methods?
• In conclusion, you need to reference previous works that have been done on detecting the best growth stage for measuring NDVI for predicting yield. You can use the mentioned references for validating your results.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

This paper proposed to use canopy measurements to predict soybean seed yield. Overall, the structure of this paper is well organized, and the presentation is relatively clear. However, there are still some crucial problems that need to be carefully addressed before a possible publication. More specifically,

1. Some related works should be reviewed by discussing and citing the following papers, particularly hyperspectral data processing and analysis e.g., “Graph Convolutional Networks for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(7): 5966-5978.”, “Nonconvex-Sparsity and Nonlocal-Smoothness-Based Blind Hyperspectral Unmixing, IEEE Transactions on Image Processing, 2019, 28(6): 2991-3006”
2. The running time and algorithm complexity should be analyzed.
3. Parameter sensitivity analysis, e.g., hyper-parameters, should be given to show the effectiveness of the proposed method.
4. It is well-known that the remote sensing images tend to suffer from various degradation, noise effects, or variabilities in the process of imaging by referring to An Augmented Linear Mixing Model to Address Spectral Variability. Please give the discussion and analysis. The reviewer is wondering what will happen if the proposed method meets the various variabilities.

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

No comment.

Reviewer 3 Report

The authors have well addressed the reviewer's concerns. No more comments.