Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network

S.No

Query

Response

1

·         Please rearrange the position of tables and figures in order to increase the readability of the paper.

Rearranged the position of tables and figures.

2

·         Several sentences are unclear or meaningless, see introduction, lines 41-43, 55-57, 60-62 and 65, for examples.

We have changed the meaningful sentence

S.No

Query

Response

1

Insert correct punctuation in the text

Corrected

2

Use carefully capital and small letters

Corrected

3

Detach some words that are attached

Incorporated

4

Give a space after the period at the end of each sentence in the text: in many cases the period is attached to the first letter of the following sentence

Corrected

5

Use correct English grammar and form: a native English speaker should read all the text

Updated correct English grammar

6

Paragraphs should be rewritten, formatting them properly

Formatted properly

7

Row 213 reports the term <<table…”>>: what table?

Figure 1 updated

8

Row 238 reports: << The size of the max pooling filter is 2X2. With strides 2. As shown in table 1. >>:what does this mean?

Meaning of 2 updated.

9

Row 244 reports: << The total number of extracted parameters are 1060138 >>: I think is best "is", not

"are"

Is updated

10

Row 294 reports: << The result shown in fig… >>: what figure?

Figure number included.

11

Row 291 reports: << The model obtained the testing accuracy and loss are 0.4944 and 1.0017

respectively >>: clarify the meaning of this sentence

Clarified the meaning of the sentence

12

In the text, the accurate definition of terms "accuracy" and "loss" is missing

Included

13

In Table 2, the value <<19s 289ms>> should be written as 19,289 s or 19,289 ms, and so on

Updated as per reviewer suggestion

14

1.           Emre ozbIlge,Mehtap Köse Ulukok,onsen Toygar, And Ebru OzbIlge," Tomato Disease Recognition Using a Compact Convolutional Neural Network",IEEE Access, 2022, 10, pp. 77213-77224

2.           Piyush Juyal and Sachin Sharma, "Detecting the Infectious area along with Disease using Deep Learning in tomato Plant Leaves ",Proc. of 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020, doi: 10.1109/ICISS49785.2020.9316108

3.           Prajwala TM, Alla Pranathi, Kandiraju Sai Ashritha, Nagaratna B. Chittaragi*, Shashidhar G. Koolagudi “Tomato Leaf Disease Detection using Convolutional Neural Networks”, in: Proc. of Innovations in Intelligent Systems and Applications Conference (ASYU), 2020, pp. 1-5,doi: 10.1109/ASYU50717.2020.9259832

These three paper refer and included in reference section.

15

Supplemented the conclusions with more experimental information

Conclusion updated .

S.No

Query

Response

1

What's new?

10 different diseases are detected and CNN model is used to identify the diseases

2

Very few references that don't.

Updated

round 1

S.No

Query

Response

 Several sentences are unclear or meaningless, see introduction, lines 41-43, 55-57, 60-62 and 65, for examples.

Lines 41-43, 55-57, 60-62 and 65  have been removed from the article as per reviewer commands and  it’s  not relevant to this work

2

The State of the art section is very poor, I recommend the author to consider reviews about deep learning in clinical applications, some examples are listed below:

We added author suggested article.

3

R. Zemouri, N. Zerhouni, and D. Racoceanu, “Deep learning in the biomedical applications: Recent and future status,” Applied Sciences, vol. 9, no. 8, 2019.

Referred this article and added.

4

28. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. Adiyoso Setio, F. Ciompi, M. Ghafoorian, J. A.W.M. van der Laak, B. van Ginneken, and C. I. S´anchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.https://doi.org/10.1016/j.media.2017.07.005

Referred this article and added.

5

Most of the figures and graphs do not add any important information about the proposed framework (Figs. 3,4 and 6, for example).

 Figure 3 show the Confusion matrix of validation and training in different epochs, figure 4 shown the performance of CNN model in graphical representation and Figure 6. Graphical representation of transfer learning techniques.

This figure helps to understand different epochs, CNN performance and transfer learning techniques.

6

Most of the figures present results of both training and validation tests. There is no need of include training losses and accuracies, typically just performances of test set are sufficient.

For detailed explanation, information on training and losses is included

7

·        Please rearrange the position of tables and figures in order to increase the readability of the paper.

Rearranged the position of tables and figures appropriate  places

8

·         The paper requires a complete revision to be presented at an English conference or published in an English journal. It is recommended that the authors find a local professional or academic who shares their first language to assist them.

We have revised the English version

round 2

S.No

Query

Response

1

Most of the criticism listed in the first round revision have not been addressed, therefore I remain of the same opinion

Above all the first round comments revised.

2

·         Most of the figures and graphs do not add any important information about the proposed framework (Figs. 3,4 and 6, for example).

Figure 3 show the Confusion matrix of validation and training in different epochs, figure 4 shown the performance of CNN model in graphical representation and Figure 6. Graphical representation of transfer learning techniques.

This figure helps to understand different epochs, CNN performance and transfer learning techniques.

·         Most of the figures present results of both training and validation tests. There is no need of include training losses and accuracies, typically just performances of test set are sufficient.

For detailed explanation, information on training and losses is included

Please rearrange the position of tables and figures in order to increase the readability of the paper (typically figures and tables are placed in the top part of a page).

Rearranged the position of tables and figures appropriate  places

·         Be careful about figures resolutions. For example, in Fig. 4 the bottom graph present different resolution compared to the upper two, the same occurs for Figs. 5(a) and 5(b).

Resolution is improved