This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.

1. Mohammad Shakirul Islam
Department of Computer Science and Engineering
Daffodil International University, Dhaka, Bangladesh.
A Novel Approach for Tomato Diseases Classification
Based on Deep Convolutional Neural Networks
International Joint Conference on
Computational Intelligence (IJCCI 2018)

2.  Motivation
 Literature Review
 Proposed Methodology
 Result discussion
 Future Work & Conclusion
 References
Table of Contents

3. Motivation
Sun Online Desk | 6th January, 2018
http://www.daily-sun.com/post/280158/Tomato-cultivation-changes-farmers-lifestyle-in-Rajshahi

4. Literature Review
Tomato: Septoria leaf spot
Now a days scientists and researchers are
working with early detection of crops and
plant diseases.
In 2012, M Hanssen et al. Published
their work on major tomato viruses.
In 2013 a Pakistani research group
worked on automated plant diseases
analysis (APDA).
There are some works done on Rise,
Potato, Cabbage etc.

5. Proposed Methodology:
Tomato Diseases Classification

6. Dataset
Fig: Sample data from Our Dataset

7. Data Processing
 6 Class
 Total data: 3000
 Train and Validation data: 80% (2400)
 Test Data: 20% (600)
 Resized Image size: 100 x 100 pixels.
 Converted to grayscale.

8. Our Proposed Model
Fig: Our Proposed Model
Our model have following number of layers:
5 convolution, 5 max pooling, 2 dropout, 1 flatten, 2 dense

9. Training the Model
 Our model was compiled by Adam Optimization Algorithm.
 Keras fit( ) function was used to train our model.
 Our model was trained for 40 epochs and batch size was 64.
 The loss type we have used is known as `Categorical cross
entropy‘.

10. Performance Evaluation
Fig. (a) Training and validation accuracy, (b) Training and validation loss.

11. Result Discussion

12. Result Discussion
Fig: Confusion Matrix

13. Future Work
 Enrich the dataset.
 Develop real life application (Android, IOS)
 Comparing with new approach for Better accuracy.
Image from Google

14. [1] Inge M. Hanssen, Moshe Lapidot, “Major Tomato Viruses in the Mediterranean Basin” ,Advances in Virus Research, Volume 84, 2012,
Pages 31-66.
[2] Mohammed Brahimi, Kamel Boukhalfa and Abdelouahab Moussaoui (2017) “Deep Learning for Tomato Diseases: Classification and
Symptoms Visualization”, Applied Artificial Intelligence, 31:4, 299-315, DOI: 10.1080/08839514.2017.1315516
[3] Y. Lu, Shujuan Yi, N. Zeng, Y. Liu, Yong Zhang, “Identification of rice diseases using deep convolutional neural
Networks.”,Neurocomputing Volume 267, 6 December 2017, Pages 378-384.
[4] Asma Akhtar, Aasia Khanum, Shoab A. Khan, Arslan Shaukat, 2013. “Automated Plant Disease Analysis (APDA): Performance
comparison of machine learning techniques.’ Proceedings of the 11th International Conference on Frontiers of Information Technology,
60–65.
[5] Hanssen, I. M., and M. Lapidot. 2012. “Major tomato viruses in the Mediterranean basin.” In Advances in virus research, volume 84
of advances in virus research, ed. G. Loebenstein and H. Lecoq, 31–66. Academic Press: San Diego, California, USA.
[6] Blancard, D. 2012. Tomato diseases. The Netherlands: Academic Press. Breitenreiter, A. H.Poppinga, T. U. Berlin, and F. N. Technik.
2015. Deep learning. Nature 521:2015.
[7] James H. Blake, Anthony P. Keinath, Marjan Kluepfel and Joey Williamson “Tomato Diseases & Disorders” by Clemson University
Home and Garden Information Center, Jun 28, 2018 [online]: https://hgic.clemson.edu/factsheet/tomato-diseases-disorders.
[8] J. Amara, B. Bouaziz, and A. Algergawy, “A Deep Learning-based Approach for Banana Leaf Diseases Classification”, B. Mitschang et
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References (Cont.)

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[15] Koike, S. T., P. Gladders, and A. O. Paulus. 2007. Vegetable diseases: A color handbook. Ed Academic Press: San Diego, California, USA.
[16] Le, T.-L., N.-D. Duong, V. T. Nguyen, and H. Vu. 2015. Complex background leaf-based plant identification method based on interactive
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References

16. Affiliation

17. Thank You!