Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The author provides new insights into NIR applications using machine learning.

Lines 66-69: include references to breast cancer tissue irrelevant to the research; Look for other research related to seed origin. Likewise, there are examples of cases of GAF in Parkinson's disease and Progressive Supranuclear Palsy. The use of the terms corn and maize should be consistent In the method, what is the cup size and sample volume for each measurement?

Put lines 247-256 in the method instead

be consistent in using wavelength (Figure 4,5, etc) with Line 130.  

Author Response

Please see the attachment.Thank you.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript (agriculture-2886735), utilising near-infrared spectroscopy and the GAF-VGG network model, innovatively identifies corn seed origins with high accuracy (96.81%) and efficiency, surpassing traditional methods and streamlining the complexity of NIR spectral modelling for traceability in the seed industry. The manuscript is intriguing but lacks significant novelty. The concept is muddled, with missing elements and definitions in the introduction and the materials and methods section. Figures require extensive revisions for improved readability. The results and discussion section needs to be more direct, with necessary references and a clear indication of the advancements made. Finally, the conclusion section, while focused, must summarise and direct future perspectives. As it is written, including in bullet points, the flow seems inadequate.

L89-103 needs bibliographic references. This requirement has been extended throughout the entire manuscript.

All figure and table captions must be reformulated to describe all elements present, including different colours, letters (A), (B), (C)…, sample numbers, and statistics used.

The materials and methods section should detail the manufacturer, city, state, and country.

The meaning of each applied algorithm needs clarification.

Figure 2: What do the yellow arrows signify? And the colour maps? What do the blue elements represent, and why is there no standard deviation or error associated?

What does Figure 3 signify?

Figures 4 and 5 need to describe what each line represents. The description within the images should be removed. If each sample represents a colour, why not group them by mean and standard deviation? As presented, it is impossible to identify.

Figure 6 is inadequate and needs a complete overhaul, perhaps to show what the red and blue dots mean? And the 1:1 line?

Table 2: Reduce the number of decimal places where possible.

Graphs need revising, for example, increase the font size, remove the grey background.

The results and discussion section must include bibliographic references.

Comments on the Quality of English Language

Moderate editing of English language required

Author Response

Please see the attachment. Thank you.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Accept in present form.

Comments on the Quality of English Language

Minor editing of English language required.