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Capsule network-based disease classification for Vitis Vinifera leaves

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Abstract

The primary source of food is extracted from the plant. Take care of and maintain the plants in real-time to enhance human survival. Diseases in plants can directly lead to a reduction in the industrial economy. Automatic detection of plant diseases is crucial for effective disease control. Vitis vinifera (Grapes) is an essential crop with rich vitamin C nutrients. This paper targets to classify the major diseases in Vitis vinifera leaves using Capsule Network (CapsNet), that prevails over the significant CNN limitations by discarding the pooling layers and adding capsule layers. Dynamic routing techniques of CapsNet make are more robust for the affine transformation of the leaves dataset. It is capable of learning large datasets effectively with vital image transformations such as rotations and transitions. Implementing CapsNet for Vitis vinifera leaf disease classification, which utilizes dynamic routing between capsules, is a novel method. The proposed CapsNet for disease classification is trained with augmented and non-augmented datasets. The performance metrics highlight that the proposed method can effectively classify the Vitis vinifera plant leaves with 98.7% of validation accuracy. The results highlight that the proposed model performs well in detecting and classifying the diseases of Vitis vinifera plant leaves on two benchmark datasets.

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References

  1. Hayit T, Erbay H, Varçın F et al (2021) Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks. J Plant Pathol 103:923–934. https://doi.org/10.1007/s42161-021-00886-2

    Article  Google Scholar 

  2. Harvey CA, Rakotobe ZL, Rao NS, Dave R, Razafimahatratra H, Rabarijohn RH, Rajaofara H, MacKinnon JL (2014) Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar. Philos Trans R Soc B Biol Sci 369(1639):20130089. https://doi.org/10.1098/rstb.2013.008

    Article  Google Scholar 

  3. Akkem Y, Biswas SK, Varanasi A (2023) Smart farming using artificial intelligence: a review. Eng Appl Artif Intell 120:105899

    Article  Google Scholar 

  4. Andrushia AD, Patricia AT (2020) Artificial bee colony optimization (ABC) for grape leaves disease detection. Evolv Syst. https://doi.org/10.1007/s12530-019-09289-2

    Article  Google Scholar 

  5. 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

    Book  Google Scholar 

  6. Ali H, Lali MI, Nawaz MZ, Sharif M, Saleem BA (2017) Symptom-based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92–104. https://doi.org/10.1016/j.compag.2017.04.008

    Article  Google Scholar 

  7. Krishnaswamy Rangarajan A, Purushothaman R (2020) Disease classification in eggplant using pre-trained VGG16 and MSVM. Sci Rep 10(1):2322

    Article  Google Scholar 

  8. Abdulridha J, Ampatzidis Y, Ehsani R, de Castro A (2018) Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado. Comput Electron Agric 155:203–2011. https://doi.org/10.1016/j.compag.2018.10.016

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Pramanik S, Joardar S, Jena O P and Obaid A J (2021) “An analysis of the operations and confrontations of using green IT in sustainable farming”. In: AIP conference proceedings (ISSN: 0094-243X, 1551-7616), Iraq, MAICT

  11. Babu BSR MSP (2007) Leaves recognition using back propagation neural network advice for pest and disease control on crops. IndiaKisan 13.

  12. Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72(1):1–13. https://doi.org/10.1016/j.compag.2010.02.007

    Article  Google Scholar 

  13. Kaur S, Pandey S, Goel S (2018) A semi-automatic leaf disease detection and classification system for soybean culture. IET Image Proces 12(6):1038

    Article  Google Scholar 

  14. Sengar N, Dutta MK, Travieso CM (2018) Computer vision-based technique for identification and quantification of powdery mildew disease in cherry leaves. Computing. https://doi.org/10.1007/s00607-018-0638-1

    Article  MathSciNet  Google Scholar 

  15. Sharif M, Khana MA, Iqbala Z, Azama MF, Lalib MIU, Javedc MY (2018) Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220–234

    Article  Google Scholar 

  16. 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) (pp. 513-518). IEEE

  17. Pramanik S (2023) A novel data hiding locating approach in image steganography, multimedia tools and applications. https://doi.org/10.1007/s11042-023-16762-3

  18. Zhang S, Wu X, You Z, Zhang L (2017) Leaf image-based cucumber disease recognition using sparse representation classification. Comput Electron Agr 134:135–141. https://doi.org/10.1016/j.compag.2017.01.014

    Article  Google Scholar 

  19. Ji M, Zhang L, Wu Q (2020) Automatic grape leaf diseases identification via united model based on multiple convolutional neural networks. Inform Process Agric 7(3):418–26

    Google Scholar 

  20. Jayasingh R, Kumar J, R.J.S, Telagathoti DB, Sagayam KM, Pramanik S (2022) Speckle noise removal by SORAMA segmentation in digital image processing to facilitate precise robotic surgery. Int J Reliable Qual E-Healthc. https://doi.org/10.4018/IJRQEH.295083

  21. Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017:1–8

    Google Scholar 

  22. Guo Y et al (2016) Deep learning for visual understanding: a review. NeuroComput 187:27–48. https://doi.org/10.1016/j.NetCom.2015.09.116

    Article  Google Scholar 

  23. Ma J et al (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 154:18–24. https://doi.org/10.1016/j.compag.2018.08.048

  24. Liang WJ, Zhang H, Zhang GF, Cao HX (2019) Rice blast disease recognition using a deep convolutional neural network. Sci Rep 9(1):2869. https://doi.org/10.1038/s41598-019-38966-0

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. 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 1(157):63–76

    Article  Google Scholar 

  27. Huang W, Zhou F (2020) DA-CapsNet: dual attention mechanism capsule network. Sci Rep 10(1):1–13

    MathSciNet  Google Scholar 

  28. Agrawal N, Singhai J, Agarwal DK. Grape leaf disease detection and classification using multi-class support vector machine. In: 2017 International conference on recent innovations in signal processing and embedded systems (RISE) (pp. 238-244). IEEE

  29. Samanta D, Dutta S, Galety MG, Pramanik S (2021) A novel approach for web mining taxonomy for high-performance computing. In: The 4th international conference of computer science and renewable energies (ICCSRE’2021). https://doi.org/10.1051/e3sconf/202129701073

  30. 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 

  31. 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 

  32. Diana Andrushia A, Mary Neebha T, Trephena Patricia A, Umadevi S, Anand N, Varshney A (2023) Image-based disease classification in grape leaves using convolutional capsule network. Soft Comput 27(3):1457–1470

  33. Lu X, Yang R, Zhou J, Jiao J, Liu F, Liu Y, Su B, Gu P (2022) A hybrid model of ghost-convolution enlightened transformer for effective diagnosis of grape leaf disease and pest. J King Saud Univ-Comput Inf Sci 34(5):1755–1767

    Google Scholar 

  34. Hinton GE, Sabour S, Frosst N (2018) Matrix capsules with EM routing. Proc Int Conf Learn Represent 6:3859–3869

    Google Scholar 

  35. Sara Sabour, Nicholas Frosst, Hinton Geoffrey E (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst 30:3856–66

    Google Scholar 

  36. 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 

  37. Du YP, Zhao XZ, He M, Guo WY (2019) A novel capsule-based hybrid neural network for sentiment classification. IEEE Access 7:39321–39328

    Article  Google Scholar 

  38. 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 cybernetics (Cybernetics). IEEE, 870–877

  39. Koresh HJD, Chacko S (2020) Classification of noiseless corneal image using capsule networks. Soft Comput 24(21):16201–16211

    Article  Google Scholar 

  40. Kruthika KR, Maheshappa HD (2019) Alzheimer’s disease neuroimaging initiative CBIR system using capsule networks and 3D CNN for alzheimer’s disease diagnosis. Inform Med Unlocked 14:59–68

    Article  Google Scholar 

  41. Mohanty SP, Hughes D, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419

    Article  Google Scholar 

  42. VidyaChellam V, Veeraiah V, Khanna A, Sheikh TH, Pramanik S, Dhabliya D (2023) A machine vision-based approach for tuberculosis identification in chest X-Rays images of patients, ICICC, Springer. https://doi.org/10.1007/978-981-99-3315-0_3

  43. Aravind KR, Raja P, Ashiwin R (2019) Disease classification in Solanum melongena using deep learning. Span J Agric Res 17(3):e0204

    Article  Google Scholar 

  44. Li Y, Qian M, Liu P et al (2019) The recognition of rice images by UAV based on capsule network. Cluster Comput 22:9515–9524. https://doi.org/10.1007/s10586-018-2482-7

    Article  Google Scholar 

  45. Kurup R V, Anupama M A, Vinayakumar R, Sowmya V, Soman K P (2020) Capsule network for plant disease and plant species classification. In: Smys S, Tavares J, Balas V, Iliyasu A (eds) Computational vision and bio-inspired computing. ICCVBIC 2019. Advances in intelligent systems and computing

  46. Touafria M, Yang Q (2019) SAR image classification via capsule networksCSAE 2019: Proceedings of the 3rd International conference on computer science and application engineering, Oct https://doi.org/10.1145/3331453.3361286

  47. Panigrahi S, Das J, Swarnkar T (2022) Capsule network based analysis of histopathological images of oral squamous cell carcinoma. J King Saud Univ Comput Inform Sci 34(7):4546–53

    Google Scholar 

  48. Sabour S, Frosst N, Hinton G E (2017) Dynamic routing between capsules. In: Proceedings of the 31st conference on neural information processing systems(NIPS). 3859–3869

  49. Thakur A, Chakraborty S (2023) Deep capsule encoder–decoder network for surrogate modelling and uncertainty quantification. Int J Numer Meth Eng 124(12):2783–2800

  50. El Alaoui-Elfels O, & Gadi T (2021) From auto-encoders to capsule networks: a survey. In: E3S Web of conferences (Vol. 229, p. 01003). EDP sciences

  51. Hassan SM, Maji AK, Jasiński M, Leonowicz Z, Jasińska E (2021) Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics 10(12):1388

    Article  Google Scholar 

  52. Lauguico S, Concepcion R, Tobias R R, Bandala A, Becerra R R, Dadios E (2020) Grape leaf multi-disease detection with confidence value using transfer learning integrated to regions with convolutional neural networks. In: Proceedings of the 2020 IEEE region 10 conference (TENCON), Osaka, Japan, 16–19: pp. 767–772

  53. Hasan MA, Riana D, Swasono S, Priyatna A, Pudjiarti E, Prahartiwi LI (2020) Identification of grape leaf diseases using convolutional neural network. J Phys Conf Ser 1641:012007

    Article  Google Scholar 

  54. Al-Saffar AAM, Tao H, Talab MA (2017) Review of deep convolution neural network in image classification. In: IEEE 2017, International conference on radar, antenna, microwave, electronics, and telecommunications. ICRAMET pp. 26–31

  55. Qiao K, Zhang C, Wang L, Chen J, Zeng L, Tong L, Yan B (2018) Accurate reconstruction of image stimuli from human functional magnetic resonance imaging based on the decoding model with capsule network architecture. Front Neuroinform 12:62

    Article  Google Scholar 

  56. Afshar P, Mohammadi A, & Plataniotis K N (2018) Brain tumour type classification via capsule networks. In: 2018 25th IEEE International conference on image processing (ICIP) (pp. 3129–3133). IEEE

  57. Kumar A D (2018) Novel deep learning model for traffic sign detection using capsule networks. arXiv preprint arXiv:1805.04424. doi: arXiv:1805.04424v1

  58. Ghoury S, Sungur C, & Durdu A (2019) Real-time disease detection of grape and grape leaves using faster r-cnn and ssd mobile net architectures. In: International conference on advanced technologies, computer engineering and science (ICATCES 2019)

  59. Li Y, Nie J, Chao X (2020) Do we really need deep CNN for plant diseases identification? Comput Electron Agric 178:105803. https://doi.org/10.1016/j.compag.2020.105803

    Article  Google Scholar 

  60. Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:105393. https://doi.org/10.1016/j.compag.2020.105393

    Article  Google Scholar 

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

    Google Scholar 

  62. 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 

  63. 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. Global Trans Proceed 2(2):535–544

    Article  Google Scholar 

  64. Alsubai S, Dutta AK, Alkhayyat AH, Jaber MM, Abbas AH, Kumar A (2023) Hybrid deep learning with improved Salp swarm optimization based multi-class grape disease classification model. Comput Electr Eng 108:108733

    Article  Google Scholar 

  65. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318. https://doi.org/10.1016/j.compag.2018.01.009

    Article  Google Scholar 

  66. Wang Q, Liu S, Chanussot J, Li X (2018) Scene classification with recurrent attention of VHR remote sensing images. IEEE Trans Geosci Remote Sens 57(2):1155–1167

    Article  Google Scholar 

  67. Omrani E, Khoshnevisan B, Shamshirband S et al (2014) Potential of radial basis function-based support vector regression for apple disease detection. Measurement 55(9):512–519

    Article  Google Scholar 

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Authors and Affiliations

  1. Karunya University, Coimbatore, Tamil Nadu, India

    A. Diana Andrushia, T. Mary Neebha & K. Martin Sagayam

  2. Anna University, Chennai, India

    A. Trephena Patricia

  3. Haldia Institute of Technology, Haldia, India

    Sabyasachi Pramanik

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  1. A. Diana Andrushia
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Andrushia, A.D., Neebha, T.M., Patricia, A.T. et al. Capsule network-based disease classification for Vitis Vinifera leaves. Neural Comput & Applic 36, 757–772 (2024). https://doi.org/10.1007/s00521-023-09058-y

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