[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Automatic method for classification of groundnut diseases using deep convolutional neural network

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. The oil obtained from the groundnut is widely used for cooking and losing body weight, and its fats are widely used for making soaps. The groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of deep convolutional neural network (DCNN) because it automatically detects the important features without any human supervision. The DCNN procedure can deeply detect plant disease by using a deep learning process. Moreover, the DCNN training and testing process demonstrate an accurate groundnut disease determination and classification result. The number of groundnut leaf disease images is chosen from the plant village dataset, and it is used for the training and testing process. The stochastic gradient decent momentum method is used for dataset training, and it has shown the better performance of proposed DCNN. From the comparison analysis, the 6th combined layer of proposed DCNN delivers a 95.28% accuracy value. Ultimately, the groundnut disease classification with its overall performance of proposed DCNN provides 99.88% accuracy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Bakker PA, Bakker AW, Marugg JD, Weisbeek PJ, Schippers B (2001) Bioassay for studying the role of siderophores in potato growth stimulation by Pseudomonas in short potato rotations. Soil Biol Biochem 19(4):443–449

    Article  Google Scholar 

  • Bharate AA, Shirdhonkar MS (2017) A review on plant disease detection using image processing. In: International conference on intelligent sustainable systems, pp 103–109.

  • Brodatz P (1996) Textures: a photographic album for artists and designers. Dover Publications, New York

    Google Scholar 

  • Dankelman I, Davidson J (2013) Woman and environment in the third world: alliance for the future. Routledge, Abingdon

    Book  Google Scholar 

  • El Houby EM (2018) A survey on applying machine learning techniques for management of diseases. J Appl Biomed 16(3):165–174

    Article  Google Scholar 

  • Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48

    Article  Google Scholar 

  • Kamilaris A, Prenafeta-Boldú FX (2018) A review of the use of convolutional neural networks in agriculture. J Agric Sci 156(3):312–322

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  • Kylberg G (2011) Kylberg texture dataset v.1.0 centre for image analysis. Swedish University of Agricultural Sciences and Uppsala University, Uppsala

    Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):536

    Article  Google Scholar 

  • Li H, Ji R, Zhang J, Yuan X, Hu K, Qi L (2010) WEB-based intelligent diagnosis system for cotton disease control. In: International conference on computer and computing technologies in agricultural on computer and computing technologies in agricultural, pp 483–490

  • Lichtenthaler HK (1996) Vegetation stress: an introduction to the stress concept in plants. J Plants Physiol 148(1–2):4–14

    Article  Google Scholar 

  • Lin M, Chen Q, Yan S (2013) Network in network. ArXiv preprint 1312:4400

    Google Scholar 

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

    Article  Google Scholar 

  • Ojala T, Maenpaa T, Pietikainen M, Viertola J, Kyllonen J, Huovinen S (2002) Outex-new framework for empirical evaluation of texture analysis algorithm. Object recognition supported by user interaction for service robots 1:701–706

    Article  Google Scholar 

  • Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Expert Syst Appl 79:164–180

    Article  Google Scholar 

  • Özçift A (2011) Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis. Comput Biol Med 41(5):265–271

    Article  Google Scholar 

  • Pimentel D, Burgess M (2014) Environmental and economic costs of the applications of pesticides primarily in the United States. In: Integrated pest management, pp 47–71

  • Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tool Appl 78(16):22691–22710.

  • Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC (2015) Imagent large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  • Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: International neural networks, pp 92–101

  • Singh KK (2018) An artificial intelligence and cloud-based collaborative platform for plant disease identification, tracking and forecasting for farmers. In: Emerging markets, pp 49–56

  • Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126

    Google Scholar 

  • Sundararaj V (2019a) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel Pers Commun 104(1):173–197

    Article  Google Scholar 

  • Sundararaj V (2019b) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325

    Article  Google Scholar 

  • Sundararaj V, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288

    Article  Google Scholar 

  • Toseef M, Khan MJ (2018) An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system. Comput Electron Agric 153:1–11

    Article  Google Scholar 

  • Traore BB, Kamsu-Foguem B, Tangara F (2018) Deep convolution neural network for image recognition. Ecol Inform 48:257–268

    Article  Google Scholar 

  • Tuzun S (2001) The relationship between pathogen-induced systemic resistance (ISRR) and multigenic (horizontal) resistance in plants. Eur J Plant Pathol 107(1):85–93

    Article  Google Scholar 

  • Wan F, Hong L, Xiao A, Jiang T, Zeng J (2019) Neo DTI: neural integration of neighbour from a heterogeneous network for discovering new drug-target interactions. Bioinformatics 35(1):104–111

    Article  Google Scholar 

  • Wang S, Zhang Y, Ji G, Yang J, Wu J, Wei L (2015) Fruit classification by wavelet-entropy and feed forward neural network trained by fitness scaled chaotic ABC and biogeography based optimization. Entropy 17(8):5711–5728

    Article  Google Scholar 

  • Wang S, Lu Z, Yang J, Zhang Y, Liu J, Wei L, Chen S, Phillips P, Dong Z (2016) Fractional Fourier entropy increases the recognition rate of fruit type detection. BMC Plant Biol 16:85

    Article  Google Scholar 

  • Wang S, Yang M, Li J, Wu X, Wang H, Liu B, Dong Z, Zhang Y (2017) Texture analysis method based on fractional Fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sen-sorineural hearing loss. Fundam Inform 151(1–4):505–521

    Article  Google Scholar 

  • Woodward JE, Brenneman TB, Kemerait RC Jr, Culbreath AK, Clark JR (2006) First report of Sclerotinia blight caused by Sclerotinia sclerotiorum on peanut in Georgia. Plant Dis 90(1):111

    Article  Google Scholar 

  • Yang CC, Prasher SO, Enright P, Madramootoo C, Burgess M, Goel PK, Callum I (2003) Application of decision tree technology for image classification using remote sensing data. Agric Syst 72(3):1101–1117

    Article  Google Scholar 

  • Zhang Y, Wu L (2012) Classification of fruits using computer vision and multiclass support vector machine. Sensors 12(9):12489–12505

    Article  Google Scholar 

  • Zhang Y, Wang S, Ji G, Phillips P (2014) Fruit classification using computer vision and feed forward neural network. J Food Eng 143:167–177

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, University College of Engineering, Villupuram, India

    M. P. Vaishnnave

  2. Department of CSE, National Institute of Technology Silchar, Cachar, Assam, India

    K. Suganya Devi

  3. Department of IT, Anna University Regional Campus, Coimbatore, India

    P. Ganeshkumar

Authors
  1. M. P. Vaishnnave
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Suganya Devi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Ganeshkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to M. P. Vaishnnave or K. Suganya Devi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

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

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by V. Loia.

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vaishnnave, M.P., Suganya Devi, K. & Ganeshkumar, P. Automatic method for classification of groundnut diseases using deep convolutional neural network. Soft Comput 24, 16347–16360 (2020). https://doi.org/10.1007/s00500-020-04946-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04946-0

Keywords

Search

Navigation