A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population
Round 1
Reviewer 1 Report
The article “A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population” presents new research on the use of image analysis, the use of different image processing methods of UAV RGB and Multispectral Imaging. This research certainly represents a great potential that can be used practically in agriculture, in the study of maturity, the degree of nutrient nutrition of plants and also the degree of plant health.
The theoretical foundation and knowledge of the subject's literature are at a high level. The research methodology was selected accurately.
Language correctness stands at a moderate level. The article is written in a poorly readable way. There are many stylistic and punctuation errors, such as lack of cohesion, improper use of commas and prepositions (page 1, line 17: the field, and). Often the verb "to be" is misused, does not agree with the subject ( page 1, line 16: (UAV) are widely). Some words doesn't seem to fit this context (page 2, line 64 hardly-hard). Consider replacing it with a different one. Punctuation and stylistic errors occur throughout the whole paper.
The high merit of the discussion and conclusion article is suitable for publication after proofreading the whole text.
The research methodology was sufficient and should be further develped.The theoretical foundation and understanding of the subject's literature are at a very high level. The description of research findings should be expanded to include statistical studies. Conclusions in the current form are not acceptable, they should be clarified and supported with data from research results. Is it possible to suggest one of the research methods on the basis of the conducted research and which of them would work best under which conditions?
Author Response
Baofeng Su
College of mechanical and electronic engineering, Northwest A & F University
December 11, 2021
Dear Reviewer,
Thanks very much for taking your time to review our manuscript entitled “A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population” (ID:remotesensing-1478966). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made correction which we hope meet with approval. The main corrections in the paper and the responds to the reviewer’s comments are as follows:
Responds to the reviewer’s comments:
- Response to comment: Extensive editing of English language and style required
Response: We regret there were problems with the English and these problems are distributed in each part of the article. We thank the reviewer for pointing this out. We have carefully revised the manuscript according to the reviewers' comments, and also have re-scrutinized and corrected these problems to improve the English. Thank you very much for your comments. They are very helpful to our work. Because these questions are too scattered, it is difficult to reply one by one here. We marked them in the original manuscript with a revision mode.
- Response to comment: the conclusions are not supported by the results
Response: I'm very sorry for the conclusions we wrote before are not acceptable and they should be clarified and supported with data from research results. We rearranged the conclusions according to the research results. The rewritten conclusions are as follows:
Conclusions
The efficiency of SG phenotyping in wheat field breeding can be significantly in-creased using appropriate HTP platforms and reliable analysis methods. The study initially demonstrated the potential of the indices derived from RGB and a 5-band multi-spectral images in SG phenotyping of diversified wheat genotypes. The temporal indices could track the phenology of wheat in late growth stage with different variation forms, and indices containing the red edge or near-infrared band could reveal more microscopic de-tails than indices containing only visible bands. Based on the dynamic curves of mono-tonic indices, the proposed SGR can serve as interpretable secondary phenotypes to com-pare the SG differences in wheat genotypes quantitatively. The SGR of MSI indices (NDRE, CIRE and NDVI) showed higher correlations with samples’ yield and more timely potential to distinguish different SG grades samples than that of color indices in RGB and MSI. The SGR of the color indices (NGRDI, GLI) in MSI were more specific in revealing changes of color phenotypes than in RGB images. All in all, the performance of the multispectral im-aging platform in this study is better than low-cost RGB imaging platform in SG phenotyping of diversified wheat germplasm.
We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list all changes but marked in red in revised paper.
We appreciate for your warm work earnestly, and hope that the correction will meet with approval.
Once again, thank you very much for your comments and suggestions.
Thank you and best regards.
Yours sincerely,
Baofeng Su
Reviewer 2 Report
All of the comments (scientific and English) have been posted inside the texts; please see and fix them.
Comments for author File: 
Comments.pdf
Author Response
Baofeng Su
College of mechanical and electronic engineering, Northwest A & F University
December 11, 2021
Dear Reviewer,
Thanks very much for taking your time to review our manuscript entitled “A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population” (ID:remotesensing-1478966). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have studied comments carefully and have made correction which we hope meet with approval. The main corrections in the paper and the responds to the reviewer’s comments are as follows:
Responds to the reviewer’s comments:
- Response to comment: Extensive editing of English language and style required
Response: We regret there were problems with the English and these problems are distributed in each part of the article. We thank the reviewer for pointing this out. We have carefully revised the manuscript according to the reviewers' comments, and also have re-scrutinized and corrected these problems to improve the English. Thank you very much for your comments. They are very helpful to our work. Because these questions are too scattered, it is difficult to reply one by one here. We marked them in the original manuscript with a revision mode.
- Response to comment: The abstract needs to be written more specifically and briefly
Response: I'm very sorry that the abstract we wrote before is not specifically and briefly. Thank you very much for your suggestions. We have rewritten abstract as follows:
Abstract: High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI, and spectral indices (SIs) from MSI were extracted, respectively. The four indices ( CIs in RGB, CIs in MSI, SIs in MSI, and CIs+SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and those indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stages detection, classifiers best average accuracy reached 93.20%-98.60% and 93.80%-98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), Normalized Difference Vegetation Index (NDVI), in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.
- Response to comment: The research design is not appropriate.
Response: I'm very sorry for the ‘Experimental Site and Materials’ we wrote before is defective. Thank you very much for your comments. We have rewritten the ‘2.1. Experimental Site and Materials’ as follows:
This study was carried out on Cao Xinzhuang experimental farm (34°18'15"N, 108°5'40.77" E) in Yangling, Shaanxi, China (Figure 1) from April to June 2021. The test area belongs to warm temperate zone and semi-humid and semi-arid region of East Asia. The average annual temperature and precipitation in this area is about 13.0 °C and 630 mm, respectively, and the rainfall in recent years is mainly concentrated from August to October. There were 565 wheat(Triticum aestivum L.) genotypes in this study, including the main domestic varieties, core germplasm, local farm varieties, backbone parents, representative varieties of different periods in China, and critical materials introduced from abroad. The experiment adopted a nonreplicated augmented design with five replicated checks varieties (‘Zhoumai18’, ‘Jimai22’, ‘Bainong207’, ‘Xinong511’, and ‘Yanzhan4110’) and plots of 6 m2 (1.2 m wide × 5 m long) with six rows spaced 0.20 m apart. A total of 640 sample plots were planted in October 18, 2020 with a planting density of 270 plants/m2. The organic matter, pH value, NH+ 4-N, NO− 3-N, available phosphorus, and available potassium of the soil (0-40 cm) in this experimental field, were 13.66-15.25 g/kg, 7.89-8.10, 0.38-0.51 mg/kg, 27.70-35.60 mg/kg, 9.96-14.41 mg/kg, and 137.72-153.03 mg/kg, respectively. Wheat samples were protected from weeds, pests and diseases during growth. For follow-up analysis, we selected 450 samples (including 406 genotypes) with a slight phenological difference and without lodging in a late growth period.
- Response to comment: Using the standard units is easier for the reader than using local units
Response: We all agree that this is a very correct suggestion. We have changed all the original yield unit (kg/mu) in the text to a standard unit (t/ha).
- Response to comment: I am not sure that the x-axis font will be fine for readers, too small
Response: We thank the reviewer for pointing this out. We have enlarged Figure 8.
- Response to comment: the conclusions are not supported by the results
Response: I'm very sorry that the conclusions we wrote before are not acceptable and they should be clarified and supported with data from research results. We rearranged the conclusions according to the research results. The rewritten conclusions are as follows:
The efficiency of SG phenotyping in wheat field breeding can be significantly in-creased using appropriate HTP platforms and reliable analysis methods. The study initially demonstrated the potential of the indices derived from RGB and a 5-band multi-spectral images in SG phenotyping of diversified wheat genotypes. The temporal indices could track the phenology of wheat in late growth stage with different variation forms, and indices containing the red edge or near-infrared band could reveal more microscopic de-tails than indices containing only visible bands. Based on the dynamic curves of mono-tonic indices, the proposed SGR can serve as interpretable secondary phenotypes to com-pare the SG differences in wheat genotypes quantitatively. The SGR of MSI indices (NDRE, CIRE and NDVI) showed higher correlations with samples’ yield and more timely potential to distinguish different SG grades samples than that of color indices in RGB and MSI. The SGR of the color indices (NGRDI, GLI) in MSI were more specific in revealing changes of color phenotypes than in RGB images. All in all, the performance of the multispectral im-aging platform in this study is better than low-cost RGB imaging platform in SG phenotyping of diversified wheat germplasm.
We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list all changes but marked in red in revised paper.
We appreciate for your warm work earnestly, and hope that the correction will meet with approval.
Once again, thank you very much for your comments and suggestions.
Thank you and best regards.
Yours sincerely,
Baofeng Su
Round 2
Reviewer 1 Report
The article "A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population" has been corrected as suggested. The substantive part of the article is acceptable for printing. The English spelling correction is still worthwhile before the article is published.
