Study on the Response of Cotton Leaf Color to Plant Water Content Changes and Optimal Irrigation Thresholds

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Please open the attached file. 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The whole manuscript should be revised by a native English or professional in English language, as there are several points with no proper syntax. 

Author Response

Comment 1: L 24: When you use acronyms, you should explain them the first time, then use them without explanation. Please explain the term RGB.

Respond: It has been changed. red, green, and blue color values measured using LScolor technology

 

Comment 2: Keywords: Do not use the same words as the title.

Respond: It has been changed. Irrigation Water Use Efficiency; Moisture monitoring; Precision irrigation; RGB color analysis

 

Comment 3: L 87: Please revise from "during different growth stages" to "during each growth stage"

Respond: It has been changed. During each growth stage

 

Comment 4: Table 1 presents some results, so it must be removed from the Materials & Methods section and moved to the Results section. No details are given on how the measurements of Table 1 were done, e.g. are the values of bulk density the average of several samples?

Respond: It has been changed. Soil samples were randomly collected from 4 sampling points with 3 replicates, and the baseline values of the experimental site were determined as follows: alkali-hydrolyzable nitrogen 26 mg·kg⁻¹; available phosphorus 47 mg·kg⁻¹; available potassium 349 mg·kg⁻¹; soil organic matter 13 g·kg⁻¹; soil pH 8.4; electrical conductivity 1,511 μS·cm⁻¹; bulk density 1.49 g·cm⁻³. The soil texture of the study site was classified as clay loam.

 

Comment 5: L 110: replace "are" with "were"

Respond: It has been changed. "are" with "were"

 

Comment 6: No replications are mentioned in the experimental design, did you miss writing about them, or did you not have replications of the treatments?

Respond: It has been changed. Color values of functional leaves from 10 cotton plants with uniform and continuous growth were collected during these five time periods each day, and the plant water content of the cotton was also measured.

 

Comment 7: L 168: citation of the formula is missing.

Respond: It has been changed. Qi, W.; Wang, H.; Xue, H.; et al. Effects of Irrigation Lower Limit on Photosynthetic Physiology and Yield of Cotton in Northern Xinjiang. Water Sav. Irrig. 2024, 12, 95-101+110, https://doi.org/10.12396/jsgg.2024198.

 

Comment 8: Do you think that the R² values of Table 4 are appropriate for adopting the linear equations?

Respond: It has been changed. Suitable

 

Comment 9: Maybe leaf "color" is not the appropriate word, as it refers to the color of the leaf. Maybe wavelength is the proper word that should be used throughout the text (and the title).

Respond: It has been changed. I'm sorry that I didn't follow your suggestion for the revision, because this paper is based on the diagnosis of cotton water status through the changes in cotton leaf color values (red, green, and blue).

 

Comment 10: In conclusions, you should mention the limitations of your study. Is it global, or are the results limited to a certain type of plant and certain spatiotemporal and climatic conditions?

Respond: It has been changed. However, this method faces practical limitations including soil type variations affecting irrigation effectiveness, weather conditions impacting diagnosis accuracy, and uncertain applicability across different cotton varieties. Comprehensive validation across multiple cultivars and environmental conditions is essential to develop universally applicable equations for optimal irrigation decision-making.

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript presents an investiigation into the potential of using cotton leaf color as an indicator of plant water content, with the broader aim of establishing an efficient and non invasive method for real time irrigation management. The authors have addressed arelevant and practical challenge in precision agriculture, particularly in cotton production, by proposing a novel approach based on the relationship between RGB leaf color values and plant water content.

The manuscript offers a valuable contribution to the field of precision irrigation. Its emphasis on real-time, low-cost, and non-destructive assessment techniques aligns well with current trends in sustainable agriculture. However, several important issues should be addressed to improve the clarity, rigor, and scientific value of the work:

1) Abstract: the abstract lacks an clearly stated research hypothesis—even a general one. Additionally, it is rather technical and dense, making it somewhat difficult to follow. It could benefit from being shortened, and sentence structures simplified to make it more accessible to readers.

2) Keywords: keywords should not repeat terms already included in the title. Please revise the list accordingly and arrange the keywords in alphabetical order.

3) Introduction: terminology used in describing the RGB-chlorophyll-water relationships should be more consistent and technically accurate. For instance, the phrase “leaf red light (R) and blue light (B)” is misleading if referring to RGB values extracted from images. A more appropriate formulation would be “R (red), G (green), and B (blue) color  ” Furthermore, the Introduction section lacks an explicitly formulated research hypothesis, although the objectives are outlined and the rationale is presented.

4) Materials and Methods:

- Line 102: please include the scientific name of the cotton species studied. The cultivar name should be placed in single quotation marks (‘...’).

- The logical relationship between Experiment 1 and Experiment 2 is unclear. Were they conducted in paralleil, independently, or are they complementary? This should be explained explicitly.

- The use of units is inconsistent and in some cases non standard (e.g., kg·hm⁻², m³·hm⁻², plants·hm⁻²). These should be unified and converted to SI compliant forms such as kg·ha⁻¹, m³·ha⁻¹, and plants·ha⁻¹. Note that “hm²” refers to square hectometers, which is rarely used and should be replaced with “ha” (hectares).

- The description of the “Cotton Moisture Determination” method is overly general. Essential details are missing, such as drying temperature and duration, initial and final sample mass, and weighing procedure.

- The section on morphological measurements lacks detail. Units are missing, and there is no information onthe timing and frequency of measurements, number of plants analyzed, plant growth stages, or methodology (e.g., how stem diameter was measured).

- The description of the colorimeter is insufficient. Please provide the device’s model, measurement range, accuracy, calibration method, and measurement conditions.

- The manuscript lacks information on the number of biological and technical replicates. How many plants were sampled per treatment? Are RGB value based on averaged readings? Please specify sample sizes for water content, yield, and other measured traits.

- The statistical analysis section is underdeveloped. Aside from correlation analysis, was statistical significance tested between groups (e.g., ANOVA, LSD, Tukey’s test)? How was significance defined (e.g., p < 0.05)? Were data checked for normality?

5) Results:

- Line 222: Text appears in Chinese and should be translated.

- Tables 1–3: Instead of “* P<0.05; ** P<0.01,” use the clearer formulation: “* significant at p < 0.05; ** significant at p < 0.01.”

- Tables 4–5: Define all abbreviations used (R, G, P, R²).

- Figures 7–11: Explain all treatment codes (CK, T1, T2, T3), and clarify the meaning of letters (a, b, c) above the bars.

- Figure 12: The unit of measurement should be included on the Y axis.

6) Discussion:

- The discussion lacks any reference to environmental conditions such as weather (e.g., humidity, wind) and soil type, which can significantly influence transpiration and irrigation effectiveness. A brief comment on the generalizability of the model under different environmental conditions would strengthen the manuscript.

- The potential applicability of the method to other crops could be discussed. While this study focuses on cotton, the same RGB vs. water content concept might be adaptable to other species (e.g., maize, soybean or vegetables), and this would broaden the impact of the research.

7) Conclusion:

- Please clarify what is novel about the proposed method.

- The conclusions are overly positive and do not acknowledge any limitations or challenges associated with the approach. A brief mention of such limitations would add scientific balance.

8) Sections from Lines 407 to 423 should be revised and completed to conform with the journal’s template.

9) Formatting and References: the manuscript, particularly the References section, must be revised to fully comply with the journals formatting guidelines and template.

Comments on the Quality of English Language

The manuscript contains numerous grammatical errors, informal expressions, and imprecise phrasing. A thorough language revision by a native English speaker or a professsional scientific editor is strongly recommended to ensure clarity, and readability throughout the entire text.

Author Response

Comment 1: Abstract: the abstract lacks an clearly stated research hypothesis—even a general one. Additionally, it is rather technical and dense, making it somewhat difficult to follow. It could benefit from being shortened, and sentence structures simplified to make it more accessible to readers.

Respond: It has been changed. The position and color values (R, G, B values) of different functional leaves may influence the relationship between leaf color and plant water content, and the relationship between leaf color and plant water content varies across different time periods.

Comment 2: Keywords: keywords should not repeat terms already included in the title. Please revise the list accordingly and arrange the keywords in alphabetical order.

Respond: It has been changed. Irrigation Water Use Efficiency; Moisture monitoring; Precision irrigation; RGB color analysis

Comment 3: Introduction: terminology used in describing the RGB-chlorophyll-water relationships should be more consistent and technically accurate. For instance, the phrase “leaf red light (R) and blue light (B)” is misleading if referring to RGB values extracted from images. A more appropriate formulation would be “R (red), G (green), and B (blue) color  ” Furthermore, the Introduction section lacks an explicitly formulated research hypothesis, although the objectives are outlined and the rationale is presented.

Respond: It has been changed.The leaf red light (R) and leaf blue light (B) have been changed to R (red), G (green), and B (blue).

The study hypothesized that leaf RGB color values would exhibit significant correlations with plant water content, with potential variations among different color components (R, G, and B values); that functional leaves at different positions might influence the relationship between leaf color values and moisture content; and that different time periods throughout the day could affect the correlations between cotton leaf color and water status.The experiment was conducted in two phases.

Comment 4-1:  Line 102: please include the scientific name of the cotton species studied. The cultivar name should be placed in single quotation marks (‘...’).

Respond: It has been changed. ‘Zhongmian 125’ 

Comment 4-2: The logical relationship between Experiment 1 and Experiment 2 is unclear. Were they conducted in paralleil, independently, or are they complementary? This should be explained explicitly.

Respond: It has been changed. Experiment 1

The experiment was conducted in 2023 to investigate the daily and periodic variations in cotton morphology and water content.

Daily variation: Previous studies have collected chlorophyll data by establishing different time points [22,23]. In this experiment, based on the changes in plant moisture and ambient temperature, five time periods were established: 9:30-12:00, 12:00-14:30, 14:30-17:00, 17:00-19:00, and 19:00-21:00. Color values of functional leaves from 10 cotton plants with uniform and continuous growth were collected during these five time periods each day, and the plant water content of the cotton was also measured.

Periodic variation: Within each irrigation cycle, the relationship between the changes in color values of functional leaves and the water content of cotton at different time periods was analyzed.

 Experiment 2

Based on the fitting equation between leaf color and water content established in Experiment 1, the plant water status was determined using leaf color values measured during the optimal time period, with the experiment conducted in 2024. A field experimental design was adopted with four irrigation treatments. Each plot covered an area of 353.4 m², measuring 38 m in length and 9.24 m in width, with irrigation amount uniformly set at 215 m³·ha⁻¹. Control (CK): conventional field cultivation method with irrigation applied once every 8 days. Three soil moisture lower limits were established: irrigation was conducted when the whole-plant water content of cotton decreased to 72% (T1), 70% (T2), and 68% (T3), respectively.Investigate the agronomic traits, yield, and irrigation water use efficiency of cotton under each treatment

Comment 4-3: The use of units is inconsistent and in some cases non standard (e.g., kg·hm⁻², m³·hm⁻², plants·hm⁻²). These should be unified and converted to SI compliant forms such as kg·ha⁻¹, m³·ha⁻¹, and plants·ha⁻¹. Note that “hm²” refers to square hectometers, which is rarely used and should be replaced with “ha” (hectares).

Respond: It has been changed. The diammonium phosphate (18-46-0) was applied as base fertilizer at 375kg·ha^-2 before seeding. The urea (N 46%), potassium sulfate (K₂O 50%), and mono ammonium phosphate (12-61-0) were applied at 600 kg·ha^-2, 375 kg·ha^-2, and 450 kg·ha^-2 separately with irrigation during the growth period.

Comment 4-4: The description of the “Cotton Moisture Determination” method is overly general. Essential details are missing, such as drying temperature and duration, initial and final sample mass, and weighing procedure.

Respond: It has been changed. During the irrigation cycle from mid-June to mid-July, two cotton plants with uniform growth near the designated observation sites were collected at five different time periods each day, with three replications. The water content of the entire cotton plant was determined using the oven-drying method. Before weighing, attached impurities or soil were removed from the collected cotton samples. After weighing, the whole cotton plant samples were cut into 3-5 cm pieces and immediately blanched at 105°C for 0.5 hours, then dried at a constant temperature of 80°C for 12 hours until reaching constant weight. The dry weight of the entire cotton plant was then measured.

Comment 4-5: The section on morphological measurements lacks detail. Units are missing, and there is no information onthe timing and frequency of measurements, number of plants analyzed, plant growth stages, or methodology (e.g., how stem diameter was measured).

Respond: It has been changed.During yield measurement on August 25, five consecutive cotton plants with uniform growth were selected for each treatment, with three replications. The following parameters were investigated: plant height (cm): distance from cotyledon node to the main stem apex; stem diameter (mm): measured at the position between the cotyledon node and the first main stem leaf using vernier calipers, with three measurements taken and the mean value presented; and the number of leaves on the main stem was counted.

Comment 4-6: The description of the colorimeter is insufficient. Please provide the device’s model, measurement range, accuracy, calibration method, and measurement conditions.

Respond: It has been changed. Respond: It has been changed. The LScolor170 has deviation values between 0.1-0.5, calibrated once per irrigation cycle using white and black reference boards. Under light-shielded conditions, leaf RGB tri-color values were measured using the LScolor170 instrument's built-in light source, with RGB tri-color value measurement ranges all between 0-255. (Shenzhen Linshang Technology Co., Ltd.)

Comment 4-7: The manuscript lacks information on the number of biological and technical replicates. How many plants were sampled per treatment? Are RGB value based on averaged readings? Please specify sample sizes for water content, yield, and other measured traits.

Respond: It has been changed. 

1. During the irrigation cycle from mid-June to mid-July, two cotton plants with uniform growth near the designated observation sites were collected at five different time periods each day, with three replications.
2. Color values of functional leaves from 10 cotton plants with uniform and continuous growth were collected during these five time periods each day, and the plant water content of the cotton was also measured.
3. During yield measurement on August 25, five consecutive cotton plants with uniform growth were selected for each treatment, with three replications. The following parameters were investigated: plant height (cm): distance from cotyledon node to the main stem apex; stem diameter (mm): measured at the position between the cotyledon node and the first main stem leaf using vernier calipers, with three measurements taken and the mean value presented; and the number of leaves on the main stem was counted.

Comment 4-8: The statistical analysis section is underdeveloped. Aside from correlation analysis, was statistical significance tested between groups (e.g., ANOVA, LSD, Tukey’s test)? How was significance defined (e.g., p < 0.05)? Were data checked for normality?

Respond: It has been changed. Data processing was conducted using Microsoft Excel. Analysis of variance (ANOVA) was performed in Origin to examine the continuous temporal trends of plant water content and leaf color RGB values throughout each irrigation cycle. Correlation analysis was carried out using SPSS to investigate the relationships between color values and plant moisture content, with significance levels set at P<0.05 and high significance at P<0.01. Both simple and multiple linear regression analyses were performed in Origin to model the relationships between plant water content and leaf color R and G values. Cotton yield, agronomic characteristics, and water use efficiency for all treatments were visualized through graphical representations generated in Origin software.

Comment 5-1: Results:- Line 222: Text appears in Chinese and should be translated.

Respond: It has been changed. The correlation analysis indicated that there was no significant correlation between plant water content and the leaf R and G color values from 09:30 to 14:30 (except for the third and fourth leaves during the boll stage). However, a significant positive relationship between plant water content and the R and G color values was observed from 14:30 to 21:00, with correlation coefficients ranging from 0.71 to 0.90 (except for the R color of the second leaf at 19:00 during the bud stage). Therefore, it is more appropriate to use the leaf R color value to diagnose plant water content during the period from 14:30 to 21:00.

Comment 5-2: Tables 1–3: Instead of “* P<0.05; ** P<0.01,” use the clearer formulation: “* significant at p < 0.05; ** significant at p < 0.01.”

Respond: It has been changed. * significant at p < 0.05; ** significant at p < 0.01

Comment 5-3: Tables 4–5: Define all abbreviations used (R, G, P, R²).

Respond: It has been changed.Note: R represents the red color value of leaves; G represents the green color value of leaves; P represents significance level, p<0.05 indicates significant correlation between treatments, and p<0.01 indicates highly significant correlation between treatment; R² represents the coefficient of determination.

Comment 5-4: Figures 7–11: Explain all treatment codes (CK, T1, T2, T3), and clarify the meaning of letters (a, b, c) above the bars.

Respond: It has been changed. Note: CK represents conventional field irrigation, while T1, T2, and T3 represent different irrigation lower limit treatments, corresponding to irrigation when plant water content drops to 72%, 70%, and 68%, respectively. In the figures, treatments sharing the same letter designation indicate no statistically significant differences between groups, whereas different letter designations denote statistically significant differences among treatments (a, b, c).

Comment 5-5: Figure 12: The unit of measurement should be included on the Y axis.

Respond: I apologize for not being able to make changes according to your suggestions. Based on the book 'Plant Physiology and Development' that I consulted, Chapter 7 in the section on Key Experiments in Understanding Photosynthesis does not indicate units.

Comment 6-1: Discussion:- The discussion lacks any reference to environmental conditions such as weather (e.g., humidity, wind) and soil type, which can significantly influence transpiration and irrigation effectiveness. A brief comment on the generalizability of the model under different environmental conditions would strengthen the manuscript.

Respond: It has been changed.Soil type significantly influences irrigation effectiveness, with each type presenting distinct characteristics that affect water management strategies. Clay soils exhibit exceptional water retention capacity but are susceptible to waterlogging, which compromises root respiration and plant health. In contrast, sandy soils provide excellent aeration but demonstrate poor water retention, resulting in excessive percolation that necessitates more frequent irrigation applications with higher volumes. Loam soils represent the optimal compromise, offering a balanced combination of water retention and aeration properties that create ideal conditions for robust plant growth. Water stress diagnosis through leaf color assessment, grounded in crop water requirement principles, serves as an effective tool for preventing water resource waste associated with excessive irrigation practices. Nevertheless, this approach cannot entirely eliminate the variable effects that different soil types exert on irrigation water effectiveness, highlighting the need for soil-specific calibration. Leaf color-based plant water status diagnosis demonstrates considerable sensitivity to prevailing weather conditions, as meteorological parameters substantially influence plant transpiration dynamics. During overcast and rainy periods, elevated atmospheric humidity suppresses leaf transpiration, minimizing water loss from cotton plants. Under these circumstances, even when cotton experiences significant water stress, visual symptoms remain subdued and difficult to detect, thereby limiting the reliability of leaf color diagnosis for accurate water status assessment. Conversely, hot and sunny conditions intensify evapotranspiration from both soil surfaces and plant tissues, accelerating water depletion and causing cotton to display pronounced water stress symptoms characterized by intensified leaf coloration. These enhanced visual indicators facilitate precise water status diagnosis through systematic leaf color observation. Wind velocity emerges as another critical factor influencing plant transpiration rates. Moderate wind speeds enhance water vapor dispersion around leaf surfaces, promoting increased transpiration and resulting in more pronounced leaf coloration changes that improve diagnostic accuracy. Furthermore, genetic variability among cotton cultivars may produce divergent leaf color responses under identical plant water content conditions, raising questions about the universal applicability of current diagnostic models across different varieties. This variability underscores the necessity for comprehensive validation studies across multiple cotton cultivars. Future research endeavors should prioritize the integration of environmental factors as auxiliary variables, incorporating temperature, humidity, and wind speed parameters to refine existing models and enhance both the accuracy and broader applicability of leaf color-based plant water diagnosis systems. Through systematic investigation of diverse cotton varieties under varied environmental conditions, researchers can develop universally applicable predictive equations that reliably correlate cotton leaf color characteristics with plant water status. This comprehensive approach aims to optimize irrigation decision-making through refined leaf color water diagnosis methodology, ultimately advancing agricultural water conservation objectives while maintaining crop productivity and quality standards.

Comment 6-2: The potential applicability of the method to other crops could be discussed. While this study focuses on cotton, the same RGB vs. water content concept might be adaptable to other species (e.g., maize, soybean or vegetables), and this would broaden the impact of the research.

Respond: It has been changed. The leaf color-based plant water status diagnosis method demonstrates considerable feasibility for crops such as corn and soybeans, as these crops exhibit distinct leaf color changes under water stress conditions. However, different crops vary in their leaf characteristics, water requirements across growth stages, and environmental sensitivity, necessitating the establishment of independent color-water relationship models and diagnostic thresholds for each specific crop. Therefore, while this method holds significant potential for broader application, crop-specific experimental validation must be conducted to ensure the accuracy and practicality of this diagnostic approach.

Comment 7-1: Conclusion:- Please clarify what is novel about the proposed method.

Respond: It has been changed. By combining the leaf R-G color values with the 72% plant water content threshold, this method enables real-time, non-destructive, and rapid diagnosis of cotton water status, avoiding traditional, time-consuming, and destructive moisture measurement methods, reducing interference with the plants, and providing a scientific basis for irrigation scheduling in cotton fields.

Comment 7-2: The conclusions are overly positive and do not acknowledge any limitations or challenges associated with the approach. A brief mention of such limitations would add scientific balance.

Respond: It has been changed. However, this method faces practical limitations including soil type variations affecting irrigation effectiveness, weather conditions impacting diagnosis accuracy, and uncertain applicability across different cotton varieties. Comprehensive validation across multiple cultivars and environmental conditions is essential to develop universally applicable equations for optimal irrigation decision-making.

Comment 8: Sections from Lines 407 to 423 should be revised and completed to conform with the journal’s template.

Respond: It has been changed.

1. Author Contributions:Binbin Mao: Study design, Methodology, Data collection, Writing – original; Draft, Visualization. Lulu Wang: Methodology, Study design, Data analysis, Data interpretation, Visualization. Junhui Cheng: Data collection, Formal analysis, Writing – review & editing. Bing Chen: Validation, Writing – original draft, Visualization. Jiandong Wang: Project administration, Resource management, Supervision. Kai Zhang: Supervision, Project administration, Funding acquisition, Writing – original draft, Writing – review. Xiaowei Liu: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review. The contributions of each author accurately reflect the specific work and contributions made by each researcher.
2. Data Availability Statement: I apologize for not being able to share my research data in the journal as required, because it involves my supervisor's research content and direction, which cannot be publicly disclosed.

Comment 9: Formatting and References: the manuscript, particularly the References section, must be revised to fully comply with the journals formatting guidelines and template.

Respond: It has been changed.

Comment 10: The manuscript contains numerous grammatical errors, informal expressions, and imprecise phrasing. A thorough language revision by a native English speaker or a professsional scientific editor is strongly recommended to ensure clarity, and readability throughout the entire text.

Respond: Further modifications have been made to address the existing grammatical issues.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript "Study on the Response of Cotton Leaf Color to Plant Water Content Changes and Optimal Irrigation Thresholds" has been significantly improved after the revision, thus I believe that it is now appropriate for publication.