Multicolor Fluorescence Imaging for the Early Detection of Salt Stress in Arabidopsis

Round 1

Reviewer 1 Report

The manuscript agronomy-1414229 is well-written and interesting. I recommend Accept it after the following minor comments.

1- Write the full name for abbreviation such as CCD. LED, RGB..etc. in the abstract and introduction then you can use abbreviations in the other sections of the manuscript.

2- Check typing errors such as space between number and nm such as 440nm or  740 nm. Revise the instructions for authors. 

3- Figure 6 needs to be bigger. The authors can put it in more space on page 9.

Author Response

Dear  reviewer

We would like to thank you and the reviewers for providing constructive comments and suggestions for our manuscript. They are very helpful for us to improve our work. We have read the comments carefully and revised our manuscript accordingly. The modifications are highlighted in the revised manuscript. In the following pages, you will find our response to each comment from the reviewers. We hope we have provided satisfactory answers to all reviewer’s comments and questions.

Please let us know if you have questions about the revised manuscript.

Sincerely,

Dapeng Ye, Haiyong Weng and Ya Tian

Responses to reviewer:

The manuscript agronomy-1414229 is well-written and interesting. I recommend Accept it after the following minor comments.

• Write the full name for abbreviation such as CCD. LED, RGB..etc. in the abstract and introduction then you can use abbreviations in the other sections of the manuscript.

Light-emitting diode (LED), charge coupled device (CCD), Red Green Blue (RGB), principal component analysis (PCA) and support vector machine (SVM). The full name for abbreviation such as CCD,. LED, RGB, etc. in the abstract and introduction has been added (see line 16-19, 42-43, 86-92)

• Check typing errors such as space between number and nm such as 440nm or 740 nm. Revise the instructions for authors.

We have revised the space between number and nm. The other typing errors also have been corrected according to the instructions for authors.

• Figure 6 needs to be bigger. The authors can put it in more space on page 9.

We have adjusted the size of Figure 6 in page 9.

Reviewer 2 Report

In this manuscript (agronomy-1414229), the Authors aim was to develop a new classification method to distinguish control and salt stressed Arabidopsis plants using multicolor fluorescence imaging parameters. They developed a new imaging system to detect plants’ autofluorescence at 440 nm, 520 nm, 690 nm and 740 nm, after applying 365 nm as excitation light. The Authors developed a classification method based on principal component analysis (PCA) and support vector machine (SVM) using data obtained from the fluorescent images, which can be used to detect early salt stress in Arabidopsis plants.

The Abstract should be rewritten to emphasize the novelty and usability of the developed method described in the study.

Introduction could be improved. Some sentences and paragraphs are lacking of references such as lines 37-39 and lines 50-51. I suggest reconsidering the style of the introduction at least in lines 68-81, try to avoid starting the sentence referring to the author (e.g. Pérez-Bueno et al. and Pineda et al.).

Description of leaf area determination should be added to the material and methods.

Data interpretation should be more clearly presented and results and discussion section should be rewritten. Some smaller suggestions are listed below:

For instance the authors state that “early salt-induced changes made limited effects on the health of the plants”. Could they provide any measurements (lipid peroxidation, viability, electrolyte leakage, photosynthetic pigment contents or other parameters) done in frame of this study or from the literature?

There is no significant difference between control and salt treated plants on the 1^st and 3^rd days of treatment, thus sentence in lines 188-190 is incorrect.

Avoid referring on the Figure or table in the sentence instead refer on the data represented in the Figure e.g. instead of “Additionally, Figure 6 showed that” can be written “Additionally, principal component analysis showed that”.

Which parameter was responsible for maximum variation in PC1 and PC2? In my opinion results on PCA and SVM analysis has to be discussed as clearly as possible, because these are the main results of the study.

Overall, the language has to be improved in the manuscript and typos within the text need to be corrected.

Further remarks:

A few example for typos: line 42 – which provides supports for the; line 65 - by chlorophyll a. in the; line 101 - m-2s-1; line 158 - with classification with labeling; line 172 - over time. and the; line 246 - Although theses parameters; etc.

In line 215, instead of increase of flavonoid would be more accurate increase of flavonoid content.

In line 221, in vivo should be written with italic.

In Figure 5 the legend should be corrected to Pearson’s correlation and some additional description would help to understanding the correlation analysis.

Table 1 is too large and it is hard to interpret in the present form. I suggest placing this table to the supplementary material, and reordering it.

Author Response

Dear  Reviewer

We would like to thank you and the reviewers for providing constructive comments and suggestions for our manuscript. They are very helpful for us to improve our work. We have read the comments carefully and revised our manuscript accordingly. The modifications are highlighted in the revised manuscript. In the following pages, you will find our response to each comment from the reviewers. We hope we have provided satisfactory answers to all reviewer’s comments and questions.

Please let us know if you have questions about the revised manuscript.

Sincerely,

Dapeng Ye, Haiyong Weng and Ya Tian

Responses to reviewer

In this manuscript (agronomy-1414229), the Authors aim was to develop a new classification method to distinguish control and salt stressed Arabidopsis plants using multicolor fluorescence imaging parameters. They developed a new imaging system to detect plants’ autofluorescence at 440 nm, 520 nm, 690 nm and 740 nm, after applying 365 nm as excitation light. The Authors developed a classification method based on principal component analysis (PCA) and support vector machine (SVM) using data obtained from the fluorescent images, which can be used to detect early salt stress in Arabidopsis plants.

1. The Abstract should be rewritten to emphasize the novelty and usability of the developed method described in the study.

In this work, we are aimed to detect the early salt stress using multicolor fluorescence imaging technology, and previous studies has been confirmed that it could be used for the detection of early stresses (Buschmann et al., 2000; Pérez-Bueno et al., 2016; Pineda et al., 2017). Hence, this study was aimed evaluated the potential of this technology applying in the early detection in salt-stressed plants. The abstract has been revised as follows:

Abstract: Salt stress is one of abiotic factors that make adverse effects on plants and there is an urgent need to detect salt stress on plants as early as possible. Multicolor fluorescence imaging as one of powerful tools in plant phenotyping can provide information about primary and secondary metabolism in plants to detect the responses of the plants exposed to stress in the early stage. The purpose of this study was to evaluate the potential of multicolor fluorescence imaging applying in the early detection in salt-stressed plants. In this study, the measurements were conducted on Arabidopsis and the multicolor fluorescence images were acquired at 440, 520, 690 and 740 nm with a self-developed imaging system consisting of a UV light-emitting diode (LED) panel for an excitation at 365 nm, a charge coupled device (CCD) camera, interference filters, and a computer. We developed a classification method using the imaging analysis of multicolor fluorescence based on principal component analysis (PCA) and support vector machine (SVM). The results showed that the four principal fluorescence feature combinations were the ideal indicators as the inputs of the SVM model, and the classification accuracies of the control and salt-stressed treatment at 5 days and 9 days were 92.65% and 98.53%, respectively. The results indicated that multicolor fluorescence imaging combined with PCA and SVM could act as a tool for the early detection in salt-stressed plants.

Reference:

Cerovic, Z.G.; Samson, G.; Morales, F.; Tremblay, N.; Moya, I. Ultraviolet-Induced Fluorescence for Plant Monitoring: Present State and Prospects. Agronomie 1999, 19, 543–578, doi:10.1051/agro:19990701.

Pérez-Bueno, M.L.; Pineda, M.; Cabeza, F.M.; Barón, M. Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping. Front. Plant Sci. 2016, 7, doi:10.3389/fpls.2016.01790.

Pineda, M.; Pérez-Bueno, M.L.; Paredes, V.; Barón, M.; Pineda, M.; Pérez-Bueno, M.L.; Paredes, V.; Barón, M. Use of Multicolour Fluorescence Imaging for Diagnosis of Bacterial and Fungal Infection on Zucchini by Implementing Machine Learning. Functional Plant Biol. 2017, 44, 563–572, doi:10.1071/FP16164.

2. Introduction could be improved. Some sentences and paragraphs are lacking of references such as lines 37-39 and lines 50-51. I suggest reconsidering the style of the introduction at least in lines 68-81, try to avoid starting the sentence referring to the author (e.g. Pérez-Bueno et al. and Pineda et al.).

The lines 37-39 has been revised in line 41-43. Spectral imaging technologies are powerful non-destructive tools that have been widely applied in evaluating the performance of plants exposed to abiotic stress, such as salinity (Alvarez et al., 2021; Zhou et al., 2018), water (Chan et al., 2021; Krishna et al., 2021), and heat (Park et al., 2021).

The lines 50-51 has been revised in line 54-56. These studies above indicated that spectral imaging technologies could be used to analyze plant phenotype changes responding to abiotic stresses and enable to provide useful information for detection of the plants exposed to abiotic stresses. (Al-Rahbi et al., 2019; Feng et al., 2020; Lee et al., 2019; Simko et al., 2016; Struthers et al., 2015).

The style of introduction has been revised, and we try to avoid starting the sentence referring to the author as the reviewer suggested. (see lines 45-53, 74-83)

References:

Al-Rahbi, S., Al-Mulla, Y.A., Jayasuriya, H., Choudri, B., 2019. Analysis of True-color Images from Unmanned Aerial Vehicle to Assess Salinity Stress on Date Palm. JARS 13, 034514. https://doi.org/10.1117/1.JRS.13.034514

Awlia, M., Nigro, A., Fajkus, J., Schmoeckel, S.M., Negrão, S., Santelia, D., Trtílek, M., Tester, M., Julkowska, M.M., Panzarová, K., 2016. High-Throughput Non-destructive Phenotyping of Traits that Contribute to Salinity Tolerance in Arabidopsis thaliana. Front. Plant Sci. 7. https://doi.org/10.3389/fpls.2016.01414

Buschmann, C., Langsdorf, G., Lichtenthaler, H.K., 2000. Imaging of the Blue, Green, and Red Fluorescence Emission of Plants: An Overview. Photosynthetica 38, 483–491. https://doi.org/10.1023/A:1012440903014

Feng, X., Zhan, Y., Wang, Q., Yang, X., Yu, C., Wang, H., Tang, Z., Jiang, D., Peng, C., He, Y., 2020. Hyperspectral Imaging Combined with Machine Learning as A Tool to Obtain High-throughput Plant Salt-stress Phenotyping. The Plant Journal 101, 1448–1461. https://doi.org/10.1111/tpj.14597

Lee, A.Y., Kim, S.Y., Hong, S.J., Han, Y., Choi, Y., Kim, M., Yun, S.K., Kim, G., 2019. Phenotypic Analysis of Fruit Crops Water Stress Using Infrared Thermal Imaging. J. Biosyst. Eng. 44, 87–94. https://doi.org/10.1007/s42853-019-00020-2

Pérez-Bueno, M.L., Pineda, M., Cabeza, F.M., Barón, M., 2016. Multicolor fluorescence imaging as a candidate for disease detection in plant phenotyping. Front. Plant Sci. 7. https://doi.org/10.3389/fpls.2016.01790

Pineda, M., Pérez-Bueno, M.L., Paredes, V., Barón, M., Pineda, M., Pérez-Bueno, M.L., Paredes, V., Barón, M., 2017. Use of multicolour fluorescence imaging for diagnosis of bacterial and fungal infection on zucchini by implementing machine learning. Functional Plant Biol. 44, 563–572. https://doi.org/10.1071/FP16164

Simko, I., Hayes, R.J., Furbank, R.T., 2016. Non-destructive Phenotyping of Lettuce Plants in Early Stages of Development with Optical Sensors. Frontiers in Plant Science 7, 1985. https://doi.org/10.3389/fpls.2016.01985

Struthers, R., Ivanova, A., Tits, L., Swennen, R., Coppin, P., 2015. Thermal Infrared Imaging of The Temporal Variability in Stomatal Conductance for Fruit Trees. International Journal of Applied Earth Observation and Geoinformation 39, 9–17. https://doi.org/10.1016/j.jag.2015.02.006

3. Description of leaf area determination should be added to the material and methods.

The description of leaf area determination has been added to the material and methods in lines 127-130 as follows: In order to observe the effect of salt stress on plant morphology features, the RGB images were acquired at day 1, 3, 5, 7, 9 after salt stress using RGB camera (Nikon D5600, Tokyo, Japan) and the projected leaf area of plants was calculated from the total pixels.

4. Data interpretation should be more clearly presented and results and discussion section should be rewritten. Some smaller suggestions are listed below:
   • For instance the authors state that “early salt-induced changes made limited effects on the health of the plants”. Could they provide any measurements (lipid peroxidation, viability, electrolyte leakage, photosynthetic pigment contents or other parameters) done in frame of this study or from the literature?

We appreciate reviewer’s comments. In this section, we are aimed to demonstrate that the early salt stress could make limited effects on the structural traits of plants like leaf area, color, roundness, which could be acquired from RGB images. And the results we obtained were similar to the previous findings (Awlia et al., 2016). We have revised the description in the manuscript (see lines 184-186).

Reference:

Awlia, M.; Nigro, A.; Fajkus, J.; Schmoeckel, S.M.; Negrão, S.; Santelia, D.; Trtílek, M.; Tester, M.; Julkowska, M.M.; Panzarová, K. High-Throughput Non-Destructive Phenotyping of Traits That Contribute to Salinity Tolerance in Arabidopsis Thaliana. Front. Plant Sci. 2016, 7, doi:10.3389/fpls.2016.01414.

• There is no significant difference between control and salt treated plants on the 1st and 3rd days of treatment, thus sentence in lines 188-190 is incorrect.

The significant increases in F440 and F520 occurred from day 5 and day 3 after salt stress and the significant decrease of F690 and F740 both appeared from day 5 after treatment. The difference between control and salt-stressed plants became more significant from day 3 after salt stress treatment. The description has been revised more accurately in the manuscript (see lines 203-205)

• Avoid referring on the Figure or table in the sentence instead refer on the data represented in the Figure e.g. instead of “Additionally, Figure 6 showed that” can be written “Additionally, principal component analysis showed that”.

The expression style has been revised in line262 and line 272.

• Which parameter was responsible for maximum variation in PC1 and PC2? In my opinion results on PCA and SVM analysis has to be discussed as clearly as possible, because these are the main results of the study.

Principal component analysis could reduce the dimensionality of datasets by projecting a set of original fluorescence parameters into new a coordinate according to their impact on total response of the plants to salt stress where the first several components can express most contribution of original data. In this study, the contribution rate of the first four components all could obtain 99%, which could explain most of variance in original data. The first two components account for a large proportion. The loading matrix of the variables onto the components at day 1, 3, 5, 7 and 9 was shown in Supplementary Table S1. From the data as represented in Supplementary Table S1, it could be found that the parameters to the formation of PC1 and PC2 differentially responded to salt stress at day 1 and 3 after treatment. However, with the extension of stress time, the parameters with high positive sensitivity in PC1 and PC2 were common from day 5 after treatment. From the data as represented in Supplementary Table S1, F440/F690, F440/F740, F520/F690 and F520/F740 were all with high values from day 5 after salt stress treatment, which indicated these parameters could explain the most of variance in PC1 and F740 was responsible for maximum variation in PC2 from day 5 after salt stress treatment. The discussion has been rewritten in lines 298-308.

Overall, the language has to be improved in the manuscript and typos within the text need to be corrected.

Further remarks:

A few example for typos: line 42 – which provides supports for the; line 65 - by chlorophyll a. in the; line 101 - m-2s-1; line 158 - with classification with labeling; line 172 - over time. and the; line 246 - Although theses parameters; etc.

The line 42 has been corrected in line 46. The line 65 has been corrected in line 69. The line 101 has been corrected in line 107. The line 158 has been corrected in lines 167. The line 172 has been corrected in line 181. The line 246 has been corrected in line 254.

In line 215, instead of increase of flavonoid would be more accurate increase of flavonoid content.

The line 215 has been corrected in line 223.

In line 221, in vivo should be written with italic.

The line 221 has been corrected in line 229.

In Figure 5 the legend should be corrected to Pearson’s correlation and some additional description would help to understanding the correlation analysis.

The legend has been corrected in line 258.

Table 1 is too large and it is hard to interpret in the present form. I suggest placing this table to the supplementary material, and reordering it.

The table has been placed to supplementary material.