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Dear Dr. Abdel-Sattar
Thank you for your submission to PeerJ.
I am writing to inform you that your manuscript - Pre-harvest mango yield Prediction using artificial neural networks based on leaf nutrient variability - has been Accepted for publication.
Congratulations!
[# PeerJ Staff Note - this decision was reviewed and approved by Julin Maloof, a PeerJ Section Editor covering this Section #]
Dear Dr. Abdel-Sattar
Thank you for your submission to PeerJ.
It is my opinion as the Academic Editor for your article - Pre-harvest Mango Yield Prediction Using Artificial Neural Networks Based on Leaf Nutrient Variability - that it requires a few Minor Revisions.
You are therefore requested to go through the review report and modify the manuscript as per suggestion.
the authors follow the required revision but figure 2 is very small to see it. please use bigger letter size and abbreviation of mineral such as P no phosphorous, Ca no Calcium .......etc
it is Ok
it is Ok
thanks
Dear Dr. Abdel-Sattar
Thank you for your submission to PeerJ.
It is my opinion as the Academic Editor for your article - Sustainability of mango crop using fruit yield modeling by an artificial neural network based on variability in leaf nutrient status - that it requires some revision.
You are advised to go through the reviewers comments and modify your manuscript keeping all the recommendations in view. Please place equal emphasis on each and every section of the manuscript. It is pertinent to mention that your revised manuscript will undergo additional peer review in order to ensure that it is suitable for publication.
Look forward to receiving your revised manuscript in due course.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
While yield prediction is valuable, early-stage prediction would be more beneficial. The use of nutrient indicators four weeks before harvest (Line 168) occurs too late, as fruit drop has not yet occurred, and yield can still be manually estimated. Future work should focus on identifying the earliest reliable time point for prediction to enhance the practical utility of the model.
Abstract
Line 27: Spell out the full term before using the abbreviation (ANN) at first mention.
Introduction
- ANN is first introduced in Line 27 but used again without context in Line 126—ensure consistency.
Line 49: Avoid repeating the Latin name unnecessarily.
Lines 54–55: Do not use nested brackets. Place “(bioactive compounds)” before the citation.
Line 80: Rephrase for clarity and correct punctuation around “Vision 2030’s objectives (Achard, 2023).”
Lines 94–95: Ensure proper formatting of citations.
Materials
- Clearly state the geographic coordinates (longitude and latitude) of each farm.
- Briefly describe or tabulate the climatic conditions of each orchard (e.g., monthly averages for temperature, rainfall, humidity).
Line 161: Confirm whether fruit ripening on April 24 is accurate.
- Specify the month in which leaf samples were collected.
Lines 181–186: Cite a reference for the digestion methods used.
Line 191: Indicate the wavelengths used for nitrogen and phosphorus determination.
Line 186: Define the abbreviation “TA.”
Lines 253–256: Clarify the MAPE threshold used—10–20% or less than 10%?
- Clarify whether samples from the 9 mango orchards were analyzed individually or pooled.
Lines 167–168: Reassess the suitability of sampling leaves from 6–7-month-old branches four weeks before harvest for prediction purposes. Discuss whether nutrient depletion due to heavy fruit load in “On” years may affect accuracy, and suggest this as a direction for future research.
Lines 449–450: Correct the citation format for “Khoshnood and Mohammadi Torkashvand, 2016.”
Results
Line 304: Remove the space in the range “1.44–1.75%.”
- Avoid repeating the Latin name; it appears redundantly in lines 284, 308, 319, 330, 338, and 351. Mention it only once where appropriate.
Tables
Ensure all tables follow the journal’s formatting guidelines.
Add a footnote to clarify whether the data represent one farm or nine.
In Table 3, reduce the width of the first two columns and expand the third (Reference) to improve layout.
In Table 5, include a footnote explaining all abbreviations (RMSE, MAE, MAPE, R²).
Figures
- Renumber figures sequentially and ensure captions are correctly matched.
Figure 3 → Figure 6
Figure 4 → Figure 7
Figure 5 → Figure 8
Figure 6 → Figure 9
Figure 7 → Figure 10
Figure 8 → Figure 11
Figure 9 → Figure 12
- Add a footnote to each figure indicating whether the data represent one or nine farms.
- Correct the vertical axis label in Figure 10 (currently “pedicted”; should be “predicted”).
References
- Ensure all references follow the journal’s style, including punctuation between author names and initials.
Line 544: Check punctuation and formatting for “Al-Dosary NMN, Alnajjar FM, Aboukarima AEWM. 2023.”
Line 547: Confirm correct formatting for “Alebidi A, Abdel-Sattar M, Mostafa LY, Hamad ASA, Rihan HZ. 2023.”
Line 556: Correct the names of the last two authors: “A elmoniem, M” should be revised.
Line 621–622: Confirm whether the journal name “Journal of the Brazilian Association of Agricultural Engineering” should be abbreviated.
Line 581: Verify the correct abbreviation for the first author “Bharti Das P.”
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Clarify whether permission was obtained to collect data from the different orchards, as this is essential for ethical and legal compliance. Specify whether information on climate management practices, soil nutrient status, and irrigation was available for these orchards, as these factors significantly influence yield prediction and must be accounted for in the analysis.
Language and Grammar:
Numerous grammatical, typographical, and syntactical errors make reading cumbersome.
Example: Line 19 – “enhancing food security, and supporting the livelihoods of farmers.” → Replace with: “enhancing food security and supporting farmer livelihoods.”
Suggestion: A thorough English language editing is mandatory for clarity and fluency.
Redundancy and Repetition:
Terms like “chlorophyll content a, chlorophyll content b” and “total carbohydrates fraction” are repeated excessively (e.g., Lines 23, 32, 266, 480).
Suggested Action: Consolidate repetitive mentions to improve readability.
Inconsistent Terminology:
Variability in using “chlorophyll content a” and “Chlorophyll a” or “chlorophyll-a”.
Recommendation: Use a consistent scientific notation, e.g., Chlorophyll a, throughout.
Figure/Table Captions Missing or Misplaced:
E.g., Figures 1 to 9 and Tables 1 to 5 are referenced but not clearly embedded or described.
Action: Ensure all figures/tables are numbered, captioned, and placed appropriately in the manuscript with self-contained legends.
Sampling Strategy Needs Clarity:
The manuscript mentions 9 orchards × 9 trees = 81 trees. Were the same trees monitored in both seasons? Clarify if repeated measures were used.
Suggestion: Explain replication structure and randomization clearly.
Model Development Details:
The use of Qnet v2000 is novel, but the choice over standard tools like Python (Keras/TensorFlow) should be justified.
Action: Briefly describe why Qnet v2000 was preferred and its limitations.
Input Parameter Justification:
Why were only 8 nutrients selected as ANN inputs? No rationale is provided.
Suggestion: Include a brief discussion on selection criteria and potential exclusion of environmental/management variables.
Preprocessing Explanation Inadequate:
Normalization (between 0.15 and 0.85) is mentioned, but the implications or rationale are not discussed.
Add a sub-section explaining the preprocessing steps more clearly.
Overfitting Risk Not Addressed:
Very high training accuracy (R² = 0.989) and low error rates may indicate overfitting.
Suggestion: Include cross-validation results or early stopping mechanisms to validate robustness.
Limited Explanatory Power:
Only leaf nutrient data were used. Factors like climate, irrigation, pruning, and biotic stress significantly influence mango yield.
Recommendation: Acknowledge this limitation explicitly and propose the inclusion of such variables in future models.
Conflicting Data Interpretations:
Example: Line 282 mentions “potassium” twice. Also, in Tables 2 and 3, values overlap and sometimes contradict the text.
Action: Carefully recheck nutrient values across tables and narrative for accuracy and consistency.
Pearson Correlation Interpretation:
Line 448–449: Correlation values are described as positive but low, yet no discussion on statistical significance is included.
Suggestion: Add p-values or significance indicators to clarify relevance.
Areas for Improvement:
Title Improvement:
The current title is long and redundant. Suggestion:
“Pre-harvest Mango Yield Prediction Using Artificial Neural Networks Based on Leaf Nutrient Variability”
Abstract:
Lacks clarity in presenting methods and findings. Rewrite for better flow and impact.
Include quantitative results clearly (e.g., “ANN model predicted yield with R² = 0.975, MAPE = 3.02%”).
Conclusion Section:
Too generalized and speculative.
Suggestion: Focus on summarizing key findings, model accuracy, main contributors (chlorophyll a/b and carbohydrate), and practical implications.
References:
Many references are solid, but several are outdated (pre-2010). Include more recent literature on ANN applications in horticulture (post-2020).
Correct formatting inconsistencies (some with full journal name, others abbreviated).
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