New Methodology for Corn Stress Detection Using Remote Sensing and Vegetation Indices

Round 1

Reviewer 1 Report

This paper presented an application of well-known methods to detect stressed corn using UAV with an RGB camera. The method core is the Ex-green index, widely used in UAV studies, and the index histogram. In my view, the setback of the methodology is the fieldwork needed to gather the ground truth for each plot.  Although the authors mentioned the ‘probably stressed’ class as the main problem of their method. Also, they do not completely discuss how this new methodology improves the results of Kim et al. (2018, https://doi.org/10.3390/rs10040563).

Specific comments are in the attached pdf file.

Comments for author File: Comments.pdf

Author Response

Dear Reviewer,

Thank you for your valuable comments and suggestions. We made an effort to improve our paper.

Please see the attachment for answers on your comments.

Best regards,
Nikola Cvetković 
On behalf of the authors 

Author Response File: Author Response.pdf

Reviewer 2 Report

1. Please point out the results in your abstract
2. Could you provide your validation site in Sec. 2?
3. Could you tell me why you select ExG as your vegetation indices and why not choose others? Could you provide the comparisons among different vegetation indices for corn stress detection?
4. I see you use Hierarchical Cluster Analysis to classify the health conditions, while why not use some supervised classifiers? such as Random forest, SVM, etc.
5. In Table 3, how do you calculate the Error%? and I think it is something wrong for Error in Table 3? (why use “,” and why not “.”?)
6. some references could be considered to cite:

(1) Zheng, J., Fu, H., Li, W., Wu, W., Yu, L., Yuan, S., ... & Kanniah, K. D. (2021). Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 95-121.

(2) Wen, Y., Li, X., Mu, H., Zhong, L., Chen, H., Zeng, Y., ... & Huang, J. (2022). Mapping corn dynamics using limited but representative samples with adaptive strategies. ISPRS Journal of Photogrammetry and Remote Sensing, 190, 252-266.

(3) Nex, F., Armenakis, C., Cramer, M., Cucci, D. A., Gerke, M., Honkavaara, E., ... & Skaloud, J. (2022). UAV in the advent of the twenties: Where we stand and what is next. ISPRS journal of photogrammetry and remote sensing, 184, 215-242.

Author Response

Dear Reviewer,

Thank you for your valuable comments and suggestions. We made an effort to improve our paper.

Please see the attachment for answers on your comments.

Best regards,
Nikola Cvetković 
On behalf of the authors 

Author Response File: Author Response.pdf

Reviewer 3 Report

Abstract: It is better to use the term "crop" to refer to corn and not "plant", which is better to name a single individual.

 

Line 31: It is better to use the term "crop" to refer to corn and not "plant", which is better to name a single individual.

 

Lines 39-41: What happens with inadequate or deficient irrigation systems?

 

Lines 161-162: How to calibrate the reflectance in the RGB bands?. You can make a citing of the method?.

 

Line 168: How do you find healthy plants? You must go to the field and identify the healthy plants? Please detail this procedure with an quote.

 

Line 171: The histogram of healthy plants is calculated from the same image. Do you manage to separate healthy plants from stressed plants in the image by some kind of classification? Please could you explain in more detail this point of the methodology.

 

Lines 182-183: How do you define the areas of interest?. Can you provide a citation for this?

 

Lines 214-219: In this paragraph it is much clearer how to identify healthy plants. I think it is better to mention this in methods. If possible, rewrite this paragraph and improve 2.4.

 

Lines 245-246: Why this point was not discussed in the description of the methodology?.

 

Lines 263-264: How to interpreting the colors in figure 6(b)?.

 

Lines 301-303: The UAV can be purchased by small farmers, however, I do not think it is possible to generalize that they (Farmers) could apply this methodology.

Comments for author File: Comments.pdf

Author Response

Dear Reviewer,

Thank you for your valuable comments and suggestions. We made an effort to improve our paper.

Please see the attachment for answers on your comments.

Best regards,
Nikola Cvetković 
On behalf of the authors 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors have addressed my all issues well. While, I have only one comment:

In the fourth paragraph in Section 1, you can add another related paper about plant recognition from remote sensing images.

Zheng, J., Yuan, S., Wu, W., Li, W., Yu, L., Fu, H., & Coomes, D. (2023). Surveying coconut trees using high-resolution satellite imagery in remote atolls of the Pacific Ocean. Remote Sensing of Environment, 287, 113485.