Skip to main content

Advertisement

SpringerLink
Log in

Image-based disease classification in grape leaves using convolutional capsule network

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Crop protection is the prime hindrance to food security. Plant diseases destroy the overall quality and quantity of agricultural products. Grape is an important fruit and a major source of vitamin C nutrients. The automatic decision-making system plays a paramount role in agricultural informatics. This paper aims to detect the diseases in grape leaves using convolutional capsule networks. The capsule network is a promising neural network in deep learning. This network uses a group of neurons as capsules and effectively represents spatial information of features. The novelty of the proposed work relies on the addition of convolutional layers before the primary caps layer, which indirectly decreases the number of capsules and speeds up the dynamic routing process. The proposed method has experimented with augmented and non-augmented datasets. It effectively detects the diseases of grape leaves with an accuracy of 99.12%. The method's performance is compared with state-of-the-art deep learning methods and produces reliable results.

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

Similar content being viewed by others

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A (2020) Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. Pattern Recogn Lett 138:638–643

    Article  Google Scholar 

  • Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classification via capsule networks. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, 3129–3133

  • Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband.

  • Ampatzidis Y, De Bellis L, Luvisi A (2017) iPathology: robotic applications and management of plants and plant diseases. Sustainability 9(6):1010. https://doi.org/10.3390/su9061010

    Article  Google Scholar 

  • Andrushia AD, Patricia AT (2019) Artificial bee colony based feature selection for automatic skin disease identification of mango fruit. In: Nature Inspired Optimization Techniques for Image Processing Applications. Springer, Cham, 215–233

  • Andrushia AD, Patricia AT (2020) Artificial bee colony optimization (ABC) for grape leaves disease detection. Evol Syst 11(1):105–117

    Article  Google Scholar 

  • Arnal Barbedo JG (2019) Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 2180:96–107

    Article  Google Scholar 

  • Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D (2019) Solving current limitations of deep learning based approaches for plant disease detection. Symmetry 11(7):939

    Article  Google Scholar 

  • Chen J, Liu Q, Gao L (2019) Visual tea leaf disease recognition using a convolutional neural network model. Symmetry 11(3):343

    Article  Google Scholar 

  • Cruz A, Ampatzidis Y, Pierro R, Materazzi A, Panattoni A, De Bellis L, Luvisi A (2019) Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Comput Electron Agric 157:63–76

    Article  Google Scholar 

  • Deng F, Pu S, Chen X, Shi Y, Yuan T, Pu S (2018) Hyperspectral image classification with capsule network using limited training samples. Sensors 18(9):3153

    Article  Google Scholar 

  • Fuentes A, Yoon S, Kim S, Park DA (2017) Robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17:2022

    Article  Google Scholar 

  • 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 9:1162. https://doi.org/10.3389/fpls.2018.01162

    Article  Google Scholar 

  • Gandhi R, Nimbalkar S, Yelamanchili N, Ponkshe S (2018) Plant disease detection using CNNs and GANs as an augmentative approach. In: 2018 IEEE International Conference on Innovative Research and Development (ICIRD). IEEE 1–5

  • Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327(5967):828–831

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ghoury S, Sungur C, Durdu A (2019) Real-time diseases detection of grape and grape leaves using faster r-cnn and ssd mobilenet architectures. In: International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2019).

  • Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew SL (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27

    Article  Google Scholar 

  • Huang Z, Qin A, Lu J, Menon A, Gao J (2020) Grape leaf disease detection and classification using machine learning. In: 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). IEEE, 870–877

  • Hughes DP, Salathé M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. CoRR abs/1511.08060.

  • Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167. arXiv:1502.03167v3

  • Jaisakthi SM, Mirunalini P, Thenmozhi D (2019) Grape leaf disease identification using machine learning techniques. In: 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1–6). IEEE.

  • Ji M, Wu Z (2022) Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic. Comput Electron Agric 193:106718

    Article  Google Scholar 

  • Ji M, Zhang L, Wu Q (2020) Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Inf Proc Agricult 7(3):418–426

    Google Scholar 

  • Kerkech M, Hafiane A, Canals R (2018) Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Comput Electron Agric 155:237–243

    Article  Google Scholar 

  • Koklu M, Unlersen MF, Ozkan IA, Aslan MF, Sabanci K (2022) A CNN-SVM study based on selected deep features for grapevine leaves classification. Measurement 188:110425

    Article  Google Scholar 

  • Krishnaswamy Rangarajan A, Purushothaman R (2020) Disease classification in eggplant using pre-trained VGG16 and MSVM. Sci Rep 10(1):1–11

    Article  Google Scholar 

  • Kumar AD (2018) Novel deep learning model for traffic sign detection using capsule networks. arXiv preprint arXiv:1805.04424.

  • Liu B, Zhang Y, He D, Li Y (2017) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry. https://doi.org/10.3390/sym10010011

    Article  Google Scholar 

  • Liu B, Zhang Y, He D, Li Y (2018) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10:11

    Article  Google Scholar 

  • Liu B, Ding Z, Tian L, He D, Li S, Wang H (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11:1082

    Article  Google Scholar 

  • Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017a) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384

    Article  Google Scholar 

  • Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017b) Identification of rice disease using deep convolutional neural networks. Neuro Comput 267:378–384. https://doi.org/10.1016/j.neucom.2017.06.023

    Article  Google Scholar 

  • Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 154:18–24

    Article  Google Scholar 

  • Marino S, Beauseroy P, Smolarz A (2019) Deep learning-based method for classifying and localizing potato blemishes. In: ICPRAM (pp. 107–117), https://doi.org/10.5220/0007350101070117.

  • Paoletti ME, Haut JM, Fernandez-Beltran R, Plaza J, Plaza A, Li J, Pla F (2018) Capsule networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(4):2145–2160

    Article  Google Scholar 

  • Paymode AS, Malode VB (2022) Transfer learning for multi-crop leaf disease image classification using convolutional neural networks VGG. Artif Intell Agricult

  • Polder G, Blok PM, De Villiers HA, Van der Wolf JM, Kamp J (2019) Potato virus detection in seed potatoes using deep learning on hyperspectral images. Front Plant Sci 10:209

    Article  Google Scholar 

  • Qin F, Liu D, Sun B, Ruan L, Ma Z, Wang H (2016) Identification of Alfalfa leaf diseases using image recognition technology. PLoS ONE 11(12):e0168274

    Article  Google Scholar 

  • Rao US, Swathi R, Sanjana V, Arpitha L, Chandrasekhar K, Naik PK (2021) Deep learning precision farming: grapes and mango leaf disease detection by transfer learning. Glob Trans Proc 2(2):535–544

    Article  Google Scholar 

  • Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst 30

  • Savary S, Ficke A, Aubertot JN, Hollier C (2012) Crop losses due to diseases and their implications for global food production losses and food security. Food Secur 4(4):519–537

    Article  Google Scholar 

  • Sezer A, Sezer HB (2019) Capsule network-based classification of rotator cuff pathologies from MRI. Comput Electr Eng 80:106480

    Article  Google Scholar 

  • Verma S, Chug A, Singh AP (2020) Exploring capsule networks for disease classification in plants. J Stat Manag Syst 23(2):307–315

    Google Scholar 

  • Waghmare H, Kokare R, Dandawate Y (2016) Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. In: 2016 3rd international conference on signal processing and integrated networks (SPIN). IEEE, 513–518

  • Wang Y, Huang L, Jiang S, Wang Y, Zou J, Fu H, Yang S (2020) Capsule networks showed excellent performance in the classification of hERG blockers/non blockers. Front Pharmacol 10:1631

    Article  Google Scholar 

  • Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017

  • Xie X, Ma Y, Liu B, He J, Li S, Wang H (2020) A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Front Plant Sci 11:751

    Article  Google Scholar 

  • Yin J, Li S, Zhu H, Luo X (2019) Hyperspectral image classification using CapsNet with well-initialized shallow layers. IEEE Geosci Remote Sens Lett 16(7):1095–1099

    Article  Google Scholar 

  • Yuan H, Zhu J, Wang Q, Cheng M, Cai Z (2022) An Improved DeepLab v3+ deep learning network applied to the segmentation of grape leaf black rot spots. Front Plant Sci 13:795410–795410

    Article  Google Scholar 

  • Zhang W, Tang P, Zhao L (2019) Remote sensing image scene classification using CNN-CapsNet. Remote Sens 11(5):494

    Article  Google Scholar 

  • Zhu J, Cheng M, Wang Q, Yuan H, Cai Z (2021) Grape leaf black rot detection based on super-resolution image enhancement and deep learning. Front Plant Sci 12

  • Zilvan V, Ramdan A, Suryawati E, Kusumo RBS, Krisnandi D, Pardede HF (2019) Denoising convolutional variational autoencoders-based feature learning for automatic detection of plant diseases. In: 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) (pp. 1–6). IEEE, https://doi.org/10.1109/ICICoS48119.2019.8982494.

Download references

Funding

This study was not funded by any other organization.

Author information

Authors and Affiliations

  1. Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    A. Diana Andrushia, T. Mary Neebha & N. Anand

  2. Anna University, Chennai, Tamil Nadu, India

    A. Trephena Patricia

  3. Centre for Nano Electronics and VLSI Design, SENSE, VIT University, Chennai campus, Tamil Nadu, India

    S. Umadevi

  4. Gurukula Kangri University, Haridwar, Uttarakhand, India

    Atul Varshney

Authors
  1. A. Diana Andrushia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Mary Neebha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Trephena Patricia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Umadevi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. N. Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Atul Varshney
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Diana Andrushia.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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 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

Diana Andrushia, A., Mary Neebha, T., Trephena Patricia, A. et al. Image-based disease classification in grape leaves using convolutional capsule network. Soft Comput 27, 1457–1470 (2023). https://doi.org/10.1007/s00500-022-07446-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07446-5

Keywords

Search

Navigation