Skip to main content

Advertisement

SpringerLink
Log in

Comparison of various deep convolutional neural network models to discriminate apple leaf diseases using transfer learning

  • Original Article
  • Published:
Journal of Plant Diseases and Protection Aims and scope Submit manuscript

Abstract

Plant diseases are the major factor behind production loss in agriculture. The traditional manual methods for disease detection in plants involve expert knowledge that may be biased. The modern computing techniques and image processing can assist non-experts in plant disease management. Recently, deep learning techniques have observed remarkable success in image-based health assessment of plants. In this paper, the state-of-the-art pre-trained convolutional neural network (CNN) models are fine-tuned to detect and diagnose the diseases in apple crop using digital images. The experiments are performed on a publicly available dataset PlantVillage. The dataset consists of three classes of apple diseases including Scab, Black Rot, and Cedar Rust, and one class of Healthy leaves. The experimental results on ten well-known CNN models DenseNet201, DenseNet169, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, ResNet50, VGG16, VGG19, and Xception showed that deep learning techniques can accurately discriminate the apple diseases. DenseNet201 outperformed the other models with an accuracy of 98.75%. The high accuracy shows that CNN-based methods could be a useful alternative to the conventional methods.

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

Similar content being viewed by others

References

  • Afonso M, Blok PM, Polder G, van der Wolf JM, Kamp J (2019) Blackleg detection in potato plants using convolutional neural networks. IFAC-PapersOnLine 52(30):6–11

    Article  Google Scholar 

  • Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S (2020) ToLeD: tomato leaf disease detection using convolution neural network. Procedia Comput Sci 167:293–301

    Article  Google Scholar 

  • Ali D, Frimpong S (2020) Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector. Artif Intell Rev 53(8):6025–6042

    Article  Google Scholar 

  • Anagnostis A, Asiminari G, Papageorgiou E, Bochtis D (2020) A convolutional neural networks based method for anthracnose infected walnut tree leaves identification. Appl Sci 10(2):469

    Article  Google Scholar 

  • Bowen JK, Mesarich CH, Bus VG, Beresford RM, Plummer KM, Templeton MD (2011) Venturia inaequalis: the causal agent of apple scab. Mol Plant Pathol 12(2):105–122

    Article  Google Scholar 

  • Bracino AA, Concepcion RS, Bedruz RAR, Dadios EP, Vicerra RRP (2020) Development of a hybrid machine learning model for apple (Malus domestica) health detection and disease classification. In: 2020 IEEE 12th International conference on humanoid, nanotechnology, information technology, communication and control, environment, and management (HNICEM). IEEE, pp 1–6

  • Chakraborty S, Paul S, Rahat-uz Zaman M (2021) Prediction of apple leaf diseases using multiclass support vector machine. In: 2021 2nd International conference on robotics, electrical and signal processing techniques (ICREST). IEEE, pp 147–151

  • Chandel AK, Khot LR, Sallato B (2021) Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique. Sci Hortic 287:110228

    Article  Google Scholar 

  • Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258

  • Crespo M, Moral J, Michailides T, Trouillas F (2018) First report of black rot on apple fruit caused by Diplodia seriata in California. Plant Disease 102(4):824

    Article  Google Scholar 

  • Crowell IH (1934) The hosts, life history and control of the cedar-apple rust fungus Gymnosporangium juniperi-virginianae schw. J Arnold Arboretum 15(3):163–232

    Article  Google Scholar 

  • Darwish A, Ezzat D, Hassanien AE (2020) An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm Evol Comput 52:100616

    Article  Google Scholar 

  • Dutot M, Nelson L, Tyson R (2013) Predicting the spread of postharvest disease in stored fruit, with application to apples. Postharvest Biol Technol 85:45–56

    Article  Google Scholar 

  • Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  • 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 preprint arXiv:1704.04861

  • Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  • Jadhav SB, Udupi VR, Patil SB (2020) Identification of plant diseases using convolutional neural networks. Int J Inf Technol 13:1–10

    Google Scholar 

  • Jiang P, Chen Y, Liu B, He D, Liang C (2019) Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7:59069–59080. https://doi.org/10.1109/ACCESS.2019.2914929

    Article  Google Scholar 

  • Kaur S, Pandey S, Goel S (2019) Plants disease identification and classification through leaf images: a survey. Arch Comput Methods Eng 26:507–530

    Article  Google Scholar 

  • Khan MA, Akram T, Sharif M, Awais M, Javed K, Ali H, Saba T (2018) CCDF: automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep cnn features. Comput Electron Agric 155:220–236

    Article  Google Scholar 

  • Khan MA, Lali MIU, Sharif M, Javed K, Aurangzeb K, Haider SI, Altamrah AS, Akram T (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 

  • Kolhalkar NR, Krishnan V (2020) Mechatronics system for diagnosis and treatment of major diseases in grape vineyards based on image processing. Mater Today Proc 23:549–556

    Article  CAS  Google Scholar 

  • Kumar B, Dikshit O (2015) Spectral-spatial classification of hyperspectral imagery based on moment invariants. IEEE J Sel Top Appl Earth Obs Remote Sensing 8(6):2457–2464

    Article  Google Scholar 

  • Kumar B, Dikshit O, Gupta A, Singh MK (2020) Feature extraction for hyperspectral image classification: a review. Int J Remote Sensing 41(16):6248–6287

    Article  Google Scholar 

  • Lee SH, Goëau H, Bonnet P, Joly A (2020) New perspectives on plant disease characterization based on deep learning. Comput Electron Agric 170:105220

    Article  Google Scholar 

  • Liu Y, Chen X, Wang Z, Wang ZJ, Ward RK, Wang X (2018) Deep learning for pixel-level image fusion: recent advances and future prospects. Inf Fusion 42:158–173

    Article  Google Scholar 

  • Nevavuori P, Narra N, Lipping T (2019) Crop yield prediction with deep convolutional neural networks. Comput Electron Agric 163:104895

    Article  Google Scholar 

  • Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345–1359

    Article  Google Scholar 

  • Paoletti ME, Haut JM, Plaza J, Plaza A (2019) Deep learning classifiers for hyperspectral imaging: a review. ISPRS J Photogramm Remote Sensing 158:279–317

    Article  Google Scholar 

  • Saba L, Dey N, Ashour AS, Samanta S, Nath SS, Chakraborty S, Sanches J, Kumar D, Marinho R, Suri JS (2016) Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm. Comput Methods Prog Biomed 130:118–134

    Article  Google Scholar 

  • Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520

  • Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed 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 

  • Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034

  • Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4:41–49

    Google Scholar 

  • Sun Y, Jiang Z, Zhang L, Dong W, Rao Y (2019) Slic_svm based leaf diseases saliency map extraction of tea plant. Comput Electron Agric 157:102–109

    Article  Google Scholar 

  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  • Thangaraj R, Anandamurugan S, Kaliappan VK (2020) Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. J Plant Dis Prot 128:1–14

    Google Scholar 

  • Toda Y, Okura F (2019) How convolutional neural networks diagnose plant disease. Plant Phenomics 2019:9237136

    Article  Google Scholar 

  • Vishnoi VK, Kumar K, Kumar B (2020) Plant disease detection using computational intelligence and image processing. J Plant Dis Prot 128:1–35

    Google Scholar 

  • Vishnoi VK, Kumar K, Kumar B (2021) A comprehensive study of feature extraction techniques for plant leaf disease detection. Multimed Tools Appl 81:367–419. https://doi.org/10.1007/s11042-021-11375-0

    Article  Google Scholar 

  • Wang Z, Li H, Zhu Y, Xu T (2017) Review of plant identification based on image processing. Arch Comput Methods Eng 24:637–654

    Article  Google Scholar 

  • Waris MA, Iosifidis A, Gabbouj M (2017) CNN-based edge filtering for object proposals. Neurocomputing 266:631–640

    Article  Google Scholar 

  • Yadav D, Yadav AK et al (2020) A novel convolutional neural network based model for recognition and classification of apple leaf diseases. Traitement du Signal 37(6):1093–1101

    Article  Google Scholar 

  • Yan Q, Yang B, Wang W, Wang B, Chen P, Zhang J (2020) Apple leaf diseases recognition based on an improved convolutional neural network. Sensors 20(12):3535

    Article  Google Scholar 

  • Yigit E, Sabanci K, Toktas A, Kayabasi A (2019) A study on visual features of leaves in plant identification using artificial intelligence techniques. Comput Electron Agric 156:369–377

    Article  Google Scholar 

  • Zeiler MD, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv:1301.3557

Download references

Acknowledgements

Priyanka Pradhan and Brajesh Kumar are thankful to the Government of Uttar Pradesh, India, for providing the financial support for this research through Grant Reference Number-47/2021/606/Seventy-4-2021-4(56)/2020.

Author information

Authors and Affiliations

  1. Department of Computer Science and IT, MJP Rohilkhand University, Bareilly, UP, 243006, India

    Priyanka Pradhan & Brajesh Kumar

  2. Atal Center for Artificial Intelligence, MJP Rohilkhand University, Bareilly, UP, 243006, India

    Brajesh Kumar

  3. Department of Biosystems and Agriculture Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Shashank Mohan

Authors
  1. Priyanka Pradhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brajesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shashank Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brajesh Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Pradhan, P., Kumar, B. & Mohan, S. Comparison of various deep convolutional neural network models to discriminate apple leaf diseases using transfer learning. J Plant Dis Prot 129, 1461–1473 (2022). https://doi.org/10.1007/s41348-022-00660-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41348-022-00660-1

Keywords

Search

Navigation