Skip to main content
SpringerLink
Log in

Performance evaluation and optimization of convolutional neural network architectures for Tomato plant disease eleven classes based on augmented leaf images dataset

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

An efficient plant disease identification is prime important for Agricultural productivity. This paper proposes a method of automatic leaf extraction and classification with improved accuracy compared with existing techniques. Images acquired from natural fields are complex, with background information that needs to be accurately segmented to identify areas of interest. Grab cut is a semi-automatic approach to extract foreground objects, which might occasionally degrade an object’s properties. This paper suggests a better Grab cut for automatically extracting leaves from real-field images. The enhanced dataset of 23,617 images with eleven tomato leaf classes are created using the plant village dataset and actual field images. This paper addresses a wide range of issues related to convolutional neural network optimization, including how the number of layers affects the results of leaf disease detection and the creation of tiny models for portable devices. Five models, VGG 16, MobileNet, and custom architecture with input sizes of 98\(\times\)98, 160\(\times\)160, and 256\(\times\)256, have been optimized and evaluated through several trials. Training and testing involve varying the input size, the number of layers, the optimizer, the dropout, the batch normalization, and the transfer learning. This paper presents and analyzes the findings of thirty experiments on various architecture. The proposed modified sequential eleven-layer architecture achieves accuracy compared with MobileNet and a reduced model size of 3.9 MB for input size 160 and 4.5 MB for input size 98.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

The data availability statement

The data that support the findings of this study are available from the corresponding author,Shital Karande, upon reasonable request.

References

  1. Khattab D, Ebeid HM, Tolba MF, Hussein AS (2016) Clustering-based image segmentation using automatic grabcut. INFOS ’16, New York, NY. Association for Computing Machinery, pp 95–100

  2. Yann LC, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–44

    Article  Google Scholar 

  3. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. arXiv:abs/1604.03169

  4. Srdjan S, Marko A, Andras A, Darko S (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci, 1–11(06):2016

  5. Mamillapally N, Priyanka C (2020) Systematic review of deep learning techniques in plant disease detection. Int J Syst Assuran Eng Manage 11:05

    Google Scholar 

  6. Sabrol H, Satish K (2016) Tomato plant disease classification in digital images using classification tree. In: 2016 international conference on communication and signal processing (ICCSP), pp 1242–1246

  7. Petrellis N (2017) Mobile application for plant disease classification based on symptom signatures. In: Proceedings of the 21st Pan-Hellenic conference on informatics

  8. Vijai S, Misra AK (2016) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4:11

    Google Scholar 

  9. Xuebing B, Xinxing Li F, Zetian LX, Lingxian Z (2017) A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images. Comput Electron Agric 136(157–165):04

    Google Scholar 

  10. Pascual E, Plaza J, Tesorero J, Goma J (2019) Disease detection of Asian rice (Oryza sativa) in the Philippines using image processing, pp. 131–135, 10

  11. Chad DC, Tyr W-H, Siyuan C, Ethan S, Jason Y, Michael G, Rebecca N, Hod L (2017) Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107:1426–1432

    Article  Google Scholar 

  12. Mohammed B, Boukhalfa K, Abdelouahab M (2017) Deep learning for tomato diseases: Classification and symptoms visualization. Appl Artif Intell 31:1–17

    Google Scholar 

  13. Rocha E, Rodrigues Moreira L, Mari J (2019) Maize leaf disease classification using convolutional neural networks and hyperparameter optimization. 05

  14. Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8

  15. Fuentes AF, Yoon S, Lee J, Park DS (2018) High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank. Front Plant Sci 9

  16. Mohammed B, Boukhalfa K, Abdelouahab M (2017) Deep learning for tomato diseases: Classification and symptoms visualization. Appl Artif Intell 31:1–17

    Google Scholar 

  17. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861,

  18. Xihai Z, Yue Q, Fanfeng M, Chengguo F, Mingming Z (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370–30377

    Article  Google Scholar 

  19. Konstantinos F (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318

    Article  Google Scholar 

  20. Ramesh A (2019) Tomato crop disease classification using pre-trained deep learning algorithm. 03

  21. Shanwen Z, Zhu-Hong Y, Xiaowei W (2019) Plant disease leaf image segmentation based on superpixel clustering and em algorithm. Neural Comput Appl 31:02

    Google Scholar 

  22. Hlaing CS, Maung Zaw SM (2018) Tomato plant diseases classification using statistical texture feature and color feature. In: 2018 IEEE/ACIS 17th international conference on computer and information science (ICIS), pp 439–444

  23. Attique KM, Ikram LM, Ullah SM, Kashif J, Khursheed A, Irtaza HS, Saud AA, Talha A (2019) An optimized method for segmentation and classification of apple diseases based on strong correlation and genetic algorithm based feature selection. IEEE Access 7:46261–46277

    Article  Google Scholar 

  24. Sinan U, Nese U (2020) Classification of olive leaf diseases using deep convolutional neural networks. Neural Comput Appl 33:07

    Google Scholar 

  25. Jakjoud F, Anas H, Bouaaddi A (2019) Deep learning application for plant diseases detection, vol 10, pp 1–6,

  26. Selvaraj MG, Vergara A, Ruiz Guzman H, Safari N, Elayabalan S, Ocimati W, Blomme G (2019) Ai-powered banana diseases and pest detection. Plant Methods 15:08

    Article  Google Scholar 

  27. Marko A, Mirjana K, Srdjan S, Andras A, Darko S (2019) Solving current limitations of deep learning based approaches for plant disease detection. Symmetry 11(21):07

    Google Scholar 

  28. Geetharamani G, Arun Pandian J (2019) Identification of plant leaf diseases using a nine layer deep convolutional neural network. Comput Electr Eng 76:323–338

    Article  Google Scholar 

  29. Belal A, Samy A-N (2019) Image-based tomato leaves diseases detection using deep learning. Int J Eng Res 2:10–16

    Google Scholar 

  30. Belal A, Samy A-N (2019) Image-based tomato leaves diseases detection using deep learning. Int J Eng Res 2:10–16

    Google Scholar 

  31. Sembiring A, Away Y, Arnia F, Muharar R (1845) Development of concise convolutional neural network for tomato plant disease classification based on leaf images. J Phys Confer Ser 012009(03):(2021)

  32. Sambasivam G, Geoffrey O (2020) A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egypt Inf J 22:03

    Google Scholar 

  33. Hang Yu-Xiu Zhang, Guanhua C, Wang B (2019) Classification of plant leaf diseases based on improved convolutional neural network. Sensors 19:4161

    Article  Google Scholar 

  34. Muhammad S, Potgieter J, Khalid A (2019) Plant disease detection and classification by deep learning. Plants 8(468):10

    Google Scholar 

  35. Hasanul K, Redwan S, Bakhtiar H, Tasnim A, Sabbir A (2022) Less is more: Lighter and faster deep neural architecture for tomato leaf disease classification. IEEE Access 10:07

    Google Scholar 

  36. Yashwant K, Suchi G (2022) A leaf image localization based algorithm for different crops disease classification. Inf Process Agric 9(3):456–474

    Google Scholar 

  37. Yonghua X, Longfei L, Lin W, Jinhua S, Min W (2020) Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Comput Electron Agric 177:105712

    Article  Google Scholar 

  38. Jadhav Shital GB (2022) Comparative analysis of image segmentation techniques for real field crop images, pp 1–9. 09

  39. Bindu G, Shital J (2022) Comprehensive review on machine learning for plant disease identification and classification with image processing. In: Proceedings of international conference on intelligent cyber-physical systems. algorithms for intelligent systems. Springer, Singapore, pp 247–262. 01

  40. Pintelas E, Liaskos M, Livieris I, Kotsiantis S, Pintelas P (2021) A novel explainable image classification framework: case study on skin cancer and plant disease prediction. Neural Comput Appl 33:11

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Computer Engieering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, Maharashtra, 411046, India

    Shital Karande & Bindu Garg

Authors
  1. Shital Karande
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bindu Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shital Karande.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a Conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karande, S., Garg, B. Performance evaluation and optimization of convolutional neural network architectures for Tomato plant disease eleven classes based on augmented leaf images dataset. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09670-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00521-024-09670-6

Keywords

Search

Navigation