Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences

Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

Series Vol. 2 , 22 March 2023

Open Access | Article

Tomato leaf diseases classification using alexnet

D. Deepa * 1 , R. Yaswanth 2 , K. Vasantha Kumar 3
1 Department of Computer Science and Engineering College, Tamilnadu,
2 Department of Computer Science and Engineering College, Tamilnadu,
3 Department of Computer Science and Engineering College, Tamilnadu,

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 2, 635-642
Published 22 March 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation D. Deepa, R. Yaswanth, K. Vasantha Kumar. Tomato leaf diseases classification using alexnet. ACE (2023) Vol. 2: 635-642. DOI: 10.54254/2755-2721/2/20220627.


The economy is largely reliant on agricultural productivity. Plant diseases and pests are also a serious issue in agriculture. It is necessary to recognise them early on in order to eliminate all infections as rapidly as possible and avoid crop devastation. To protect the plants against illness, a variety of insecticides have been utilised. Despite these precautions, the illness continues to spread throughout the field. Because we don't always know what kind of sickness we're dealing with, a bad pesticide may have been applied instead. As a result, it's all futile.Tomato leaf infections, on the other hand, are a severe problem for many farmers, thus mastering the severity of diseases in a fast and precise manner is critical to assisting staff in taking additional intervention steps to prevent plants from becoming more afflicted. An Alexnet model for detecting tomato leaf disease is proposed in this study, which may minimise the number of training processes while enhancing computation accuracy and gradient flow. This indicates that categorization of diseases is equally important. An improved classification model is presented in this paper for identifying and categorising tomato leaf disease. Before CNN is used to identify the pictures, a training dataset containing a large number of images is utilised, and visual characteristics are extracted using several methods. The Alexnet model achieves the highest classification accuracy when compared to other models.


Extraction, Data Augmentation, Alexnet, Detection, Convolutional Neural Network


1. Fumio Okura Yosuke Toda, “How convolutional neural networks diagnose plant disease.Plant Phenomics”, 2019.

2. Abdelouahab Moussaoui Mohammed Brahimi, Kamel Boukhalfa, “Deep learning for tomato diseases: Classification and symptoms visualization”, Applied Artificial Intelligence, 2019.

3. Marcel Salath Sharada P. Mohanty, David P. Hughes, “Using deep learning for imagebased plant disease detection”, Frontiers Plant Science, 2016.

4. Siyuan Chen Ethan L. Stewart Jason Yosinski Michael A. Gore Rebecca J. Nelson Hod Lipson Chad DeChant, TyrWiesner-Hanks, “Automated identification of northern leaf blightinfected maize plants from field imagery using deep learning”, Phytopathology, 2017.

5. Loreto Susperregi Carlos Tub-o Ivan RankiT Libor LenDa Aitor Gutierrez, Ander Ansuategi, ”A benchmarking of learning strategies for pest detection and identification on tomato plants for autonomous scouting robots using internal databases”, Journal of Sensors, 2018.

6. Andras Anderla Dubravko Culibrk-Darko Stefanovic Srdjan Sladojevic, Marko Arsenovic, ”Deep neural networks based recognition of plant diseases by leaf image classification”, Computational Intelligence and Neuroscience, 2016.

7. Hiroyuki Uga Satoshi Kagiwada-Hitoshi Iyatomi Erika Fujita, Yusuke Kawasaki, ”Basic investigation on a robust and practical plant diagnostic system”, IEEE, 2016.

8. Aboul Ella Hassenian-E. Emary Mahmoud A. Mahmoud Hesham Hefny Mohamed F. Tolba Usama Mokhtar, NashwaEl-Bendary, ”Svm-based detectionof tomato leaves diseases”, Advances in Intelligent Systems and Computing, 2014.

9. Satoshi Kagiwada Hitoshi Iyatomi Yusuke Kawasaki, Hiroyuki Uga, ”Basic study of automated diagnosis of viral plant diseases using convolutional neural networks”, ISVC, 2015.

10. Shoab A. Khan Arslan Shaukat Asma Akhtar, Aasia Khanum, ”Automated plant disease analysis (apda): Performance comparison of machine learning techniques.

11. Khattak et al., "Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model," in IEEE Access, vol. 9, pp. 112942-112954, 2021.

12. W. Pan, J. Qin, X. Xiang, Y. Wu, Y. Tan and L. Xiang, "A smart mobile diagnosis system for citrus diseases based on densely connected convolutional networks", IEEE Access, vol. 7, pp. 87534-87542, 2019.

13. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city”, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.

14. Sathishkumar V E, Youngyun Cho, “A rule-based model for Seoul Bike sharing demand prediction using Weather data”, European Journal of Remote Sensing, Vol. 52, no. 1, pp. 166-183, 2020.

15. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Seoul Bike Trip duration prediction using data mining techniques”, IET Intelligent Transport Systems, Vol. 14, no. 11, pp. 1465-1474, 2020.

16. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Using data mining techniques for bike sharing demand prediction in Metropolitan city”, Computer Communications, Vol. 153, pp. 353-366, 2020.

17. Sathishkumar V E, Yongyun Cho, “Season wise bike sharing demand analysis using random forest algorithm”, Computational Intelligence, pp. 1-26, 2020.

18. Sathishkumar, V. E., Wesam Atef Hatamleh, Abeer Ali Alnuaim, Mohamed Abdelhady, B. Venkatesh, and S. Santhoshkumar. "Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment." Arabian Journal for Science and Engineering (2021): 1-9.

19. Chen, J., Shi, W., Wang, X., Pandian, S., & Sathishkumar, V. E. (2021). Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model. International Journal of Shipping and Transport Logistics, 13(5), 538-553.

20. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E., Visiting Indian Hospitals Before, During and After Covid. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30 (1), 111-123, 2022.

21. Easwaramoorthy, S., Moorthy, U., Kumar, C. A., Bhushan, S. B., & Sadagopan, V. (2017, January). Content based image retrieval with enhanced privacy in cloud using apache spark. In International Conference on Data Science Analytics and Applications (pp. 114-128). Springer, Singapore.

22. Sathishkumar, V. E., Agrawal, P., Park, J., & Cho, Y. (2020, April). Bike Sharing Demand Prediction Using Multiheaded Convolution Neural Networks. In Basic & Clinical Pharmacology & Toxicology (Vol. 126, pp. 264-265). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

23. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2021). The role of contemporary digital tools and technologies in Covid‐19 crisis: An exploratory analysis. Expert systems.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
ISBN (Print)
ISBN (Online)
Published Date
22 March 2023
Applied and Computational Engineering
ISSN (Print)
ISSN (Online)
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated