Recognition of Tomato Leaf Diseases Based on DIMPCNET
Round 1
Reviewer 1 Report
The reviewed paper, "Recognition of tomato leaf diseases based on DIMPCNET," is novel. It deals with modern image analysis techniques used to recognize four diseases of tomato leaf diseases. Such work is important for agricultural practice makes an important contribution to the development of new decision support systems in crop protection.
But the work concerns neural network modification and image analysis improvement analysis. The tangible result of the work is the developed DIMPCNET model. The experiment itself dealt with learning procedures of the neural network in disease recognition and comparing it with other modules (Table 4), in addition, the usefulness of the DIMPCNET model was checked on grape leaf disease images.
The presented work is done carefully and methodologically correct, although I would expect validation in agricultural practice.
In my opinion, the specificity of this work does not fit the profile of the journal Agronomy.
The work is interdisciplinary focused on the method of analyzing photographic data and should be published in AgriEngineeringmna, for example.
Author Response
Point 1: The reviewed paper, "Recognition of tomato leaf diseases based on DIMPCNET," is novel. It deals with modern image analysis techniques used to recognize four diseases of tomato leaf diseases. Such work is important for agricultural practice makes an important contribution to the development of new decision support systems in crop protection. But the work concerns neural network modification and image analysis improvement analysis. The tangible result of the work is the developed DIMPCNET model. The experiment itself dealt with learning procedures of the neural network in disease recognition
and comparing it with other modules (Table 4), in addition, the usefulness of the DIMPCNET model was checked on grape leaf disease images. The presented work is done carefully and methodologically correct, although I would expect
validation in agricultural practice. In my opinion, the specificity of this work does not fit the profile of the journal Agronomy. The work is interdisciplinary focused on the method of analyzing photographic data and should be published in AgriEngineeringmna, for example.
Response: Thank you for your comments. Thank you for taking the time out of your busy schedule to follow our manuscript. Thank you again for your recognition and useful suggestions. Tomatoes, as an important vegetable crop, are susceptible to various diseases and pests, seriously affecting their quality and yield, resulting in economic losses. We are researching an automatic recognition method to help identify tomato leaf diseases, which is necessary for the development of modern smart agriculture. Agronomy is an international
interdisciplinary academic journal on agriculture and agricultural ecology. As far as we know, many excellent articles on using deep learning to help identify diseases have been published in the journal. The help our work can bring to agriculture is obvious. For example, there are the following latest published articles:
Alzahrani M S, Alsaade F W. Transform and Deep Learning Algorithms for the Early Detection and Recognition of Tomato Leaf Disease[J]. Agronomy, 2023, 13(5): 1184.
Liu W, Zhai Y, Xia Y. Tomato Leaf Disease Identification Method Based on Improved YOLOX[J]. Agronomy, 2023, 13(6):1455.
Hassan SM, Jasinski M, Leonowicz Z, Jasinska E, Maji AK. Plant Disease Identification Using Shallow Convolutional Neural Network. Agronomy, 2021, 11(12): 2388.
Bi C, Xu S, Hu N, Zhang S, Zhu Z, Yu H. Identification Method of Corn Leaf Disease Based on Improved Mobilenetv3 Model. Agronomy. 2023; 13(2):300.
Shao M, He P, Zhang Y, Zhou S, Zhang N, Zhang J. Identification Method of Cotton Leaf Diseases Based on Bilinear Coordinate Attention Enhancement Module. Agronomy. 2023; 13(1):88.
Gao Y, Cao Z, Cai W, Gong G, Zhou G, Li L. Apple Leaf Disease Identification in Complex Background Based on BAM-Net. Agronomy. 2023; 13(5):1240.
Peng Y, Zhao S, Liu J. Fused-Deep-Features Based Grape Leaf Disease Diagnosis. Agronomy. 2021; 11(11):2234.
Ma L, Yu Q, Yu H, Zhang J. Maize Leaf Disease Identification Based on YOLOv5n Algorithm Incorporating Attention Mechanism. Agronomy. 2023; 13(2):521.
Dai Q, Guo Y, Li Z, Song S, Lyu S, Sun D, Wang Y, Chen Z. Citrus Disease Image Generation and Classification Based on Improved FastGAN and EfficientNet-B5. Agronomy. 2023; 13(4):988
Author Response File: 
Author Response.pdf
Reviewer 2 Report
see the attachment
Comments for author File: Comments.pdf
Moderate editing of the English language required
Author Response
We responded to your comments one by one and revised them into the manuscript. Please refer to the attachment for details.
Author Response File: Author Response.pdf
Reviewer 3 Report
Dear authors,
I have reviewer the work entitled Recognition of tomato leaf diseases based on DIMPCNET, and I found some remarks included in the .PDF file.
Best regards!
Comments for author File: Comments.pdf
English should be improved, particularly in some subject-verb agreement, typography, and grammar. Please, it is essential to review citing in the Introduction Section.
Author Response
We responded to your comments one by one and revised them into the manuscript. Please refer to the attachment for details.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Author has incorporated all the comments but the last comment I guess author didn't understand. There is a problem when text is copied from pdf to word. It doesn't copy properly.
The last comment is: the practical application of the proposed work is missing in introduction as well as in conclusion
Minor editing
Author Response
Thank you for your comments. Thank you again for your kind reminder to us and sorry for our wrong understanding. We reconsidered this comment and revised it in the manuscript. The practical application of this work is added in the introduction and conclusion. The details are as follows:
In the introduction, lines 201 through 206:
- The method proposed in this paper achieved recognition accuracy of 94.44% and F1 value of 0.9475 for the identification of five types of tomato leaf dis-eases. It is effective to identify tomato leaf diseases with complex background and high similarity between classes. This allows agricultural experts and scholars to better apply this technology to the prevention and control of to-mato diseases, so as to effectively alleviate food production problems to a certain extent.
In the conclusion, lines 746 through 751:
- Compared with the current popular classification method of tomato leaf disease, our recognition accuracy was 94.44%, and F1 value was 0.9475. This method achieves better performance. It has obvious advantages in that it can effectively remove image noise and reduce the negative impact of complex background envi-ronment and disease similarity on disease recognition, so as to effectively prevent and control tomato diseases and improve tomato yield.
Author Response File: Author Response.pdf
