Skip to main content

Advertisement

SpringerLink
Log in

A novel approach for rice plant diseases classification with deep convolutional neural network

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Agriculture is one of the major revenue-producing fields and a source of livelihood in India. On the largest regions in India, rice is cultivated as an essential food. It is observed that rice crops are strongly affected by diseases, that causes major loses in agriculture sector. Plant pathologists are searching for an accurate and reliable diagnosis method for rice plant disease. The machine learning has been used effectively in various areas of crop remote sensing, particularly in the classification of crop diseases. At present time, deep learning is a hot research topic for crop disease identification. In this research, we developed an efficient rice plant disease detection method based on convolution neural network approach. This paper focuses mainly on three well known rice diseases, namely, leaf smut and brown spot caused by fungus and bacterial leaf blight caused by bacteria. This article proposes an effective approach for recognition and identification of rice plant disease based on size, shape and color of lesions in the leaf image. The proposed model applies Otsu’s global thresholding technique to perform image binarization to remove background noise of the image. The proposed method based on fully connected CNN was trained using 4000 image samples of each diseased leaves and 4000 image samples of healthy rice leaves, to detect the three rice diseases. The analysis of result shows that the proposed fully connected CNN is fast and effective approach, which provides an accuracy of 99.7% on the dataset. This accuracy is significantly higher than that for existing plant disease recognition and classification approaches.

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
Fig. 15

Similar content being viewed by others

References

  1. Agriculture economics and importance of agriculture in national economy. http://agriinfo.in/?page=topic&superid=9&topicid=185. Accessed 26 Dec 2020

  2. Agriculture Sector in India. http://www.ibef.org/industry/agriculture-india.aspx. Accessed 26 Dec 2020

  3. Gianessi LP (2014) Importance of pesticides for growing rice in South and South East Asia, International Pesticide Benefit Case Study 108 October, 2014, pp 30–33

  4. Rice production (peace corps): Chapter 14—diseases of rice. http://www.nzdl.org. Accessed 26 Dec 2020

  5. Al-Bashish D, Braik M, Bani-Ahmad S (2011) Detection and classification of leaf diseases using K-means-based segmentation and neural networks-based classification. Inform Technol J 10(267–275):2011. https://doi.org/10.3923/itj.2011.267.275

    Article  Google Scholar 

  6. Shah JP, Prajapati HB, Dabhi VK Rice leaf diseases data set. https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases. Accessed 26 Dec 2020

  7. Phadikar S, Sil J (2008) Rice disease identification using pattern recognition techniques. In: Proceedings of the IEEE international conference on computer and information technology (ICCIT), Khulna, Bangladesh, pp 420–423

  8. Khairnar K, Dagade R (2014) Disease detection and diagnosis on plant using image processing—a review. Int J Comput Appl 108(13):36–39

    Google Scholar 

  9. Barbedo JGA (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus 2(1):660–672

    Article  Google Scholar 

  10. Shruthi U, Nagaveni V, Raghvendra BK (2019) A review on machine learning classification techniques for plant disease detection. In: 5th international conference on advanced computing and communication systems (ICACCS). IEEE, Coimbatore

  11. Tadeusiewicz R, Tylek P, Adamczyk F, Kielbasa P, Jablonski M, Pawlik P, Pilat A, Walczyk J, Szczepaniak J, Juliszewski T, Szaroleta M (2017) Automation of the acorn scarification process as a contribution to sustainable forest management, case study: common oak. Sustainability 9(12):2276. https://doi.org/10.3390/su9122276

    Article  Google Scholar 

  12. Barbedo JGA (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus 2(1):1–12. https://doi.org/10.1186/2193-1801-2-660

    Article  Google Scholar 

  13. Dhingra G, Kumar V, Joshi HD (2018) Study of digital image processing techniques for leaf disease detection and classification. Multimed Tools Appl 77(15):19951–20000. https://doi.org/10.1007/s11042-017-5445-8

    Article  Google Scholar 

  14. Pydipati R, Burks TF, Lee WS (2005) Statistical and neural network classifiers for citrus disease detection using machine vision. Trans ASAE 48(5):2007–2014

    Article  Google Scholar 

  15. Huang KY (2007) Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Comput Electron Agric 57:3–11

    Article  Google Scholar 

  16. Youwen T, Tianlai L, Yan N (2008) The recognition of cucumber disease based on image processing and support vector machine. In: 2008 congress on image and signal processing. IEEE, Sanya, pp 262–267

  17. Yao Q, Guan Z, Zhou Y, Tang J, Hu Y, Yang B (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: 2009 international conference on engineering computation. IEEE, Hong Kong, pp 79–83

  18. Al Bashish D, Braik M, Bani-Ahmad S (2010) A framework for detection and classification of plant leaf and stem diseases. In: 2010 international conference on signal and image processing. IEEE, Chennai, pp 113–118

  19. Kulkarni A, Ashwin Patil RK (2012) Applying image processing technique to detect plant diseases. Int J Mod Eng Res (IJMER) 2:3661–3664

    Google Scholar 

  20. Dhaygude SB, Kumbhar NP (2013) Agricultural plant leaf disease detection using image processing. Int J Adv Res Electr Electron Instrum Eng 2:599–602

    Google Scholar 

  21. Prajapati HB, Shah JP, Dabhi VK (2017) Detection and classification of rice plant diseases. J Intell Decis Technol 11(3):357–373

    Google Scholar 

  22. Jayanthi G, Archana KS, Saritha A (2019) Analysis of automatic rice disease classification using image processing techniques. Int J Eng Adv Technol (IJEAT) 8(3S):2249–8958

    Google Scholar 

  23. Atabay HA (2017) Deep residual learning for tomato plant leaf disease identification. J Theor Appl Inf Technol 95(24):6800–6808

    Google Scholar 

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

    Article  Google Scholar 

  25. Kumbhar S, Nilawar A, Patil S, Mahalakshmi B, Nipane M (2019) Farmer buddy-web based cotton leaf disease detection using CNN. Int J Appl Eng Res 14(11):2662–2666

    Google Scholar 

  26. Jayasripriyanka K, Gaayathri S, Vinmathi MS, Jayashri C (2019) Semi-automatic leaf disease detection and classification system for soybean culture. Int Res J Eng Technol (IRJET) 06:1721–1724

    Google Scholar 

  27. Durmuú H, Güneú EO, Kõrc ÕM (2017) Disease detection on the leaves of the tomato plants by using deep learning. In: Proceedings of conference 6th international conference on agro-geoinformatics, Fairfax, VA. IEEE

  28. Patidar S, Pandey A, Shirish B, Sriram A (2020). Rice plant disease detection and classification using deep residual learning. https://doi.org/10.1007/978-981-15-6315-7_23

  29. Jadhav SB, Udupi VR, Patil SB (2020) Identification of plant diseases using convolutional neural networks. Int J Inf Technol. https://doi.org/10.1007/s41870-020-00437-5

    Article  Google Scholar 

  30. Pujari JD, Yakkundimath R, Byadgi AS (2013) Classification of fungal disease symptoms affected on cereals using color texture features. Int J Signal Process 6(6):321–330

    Google Scholar 

  31. Yue Y, Cheng X, Zhang D, Wu YZ, Zhao Y, Chen YQ, Fan GH, Zhang YH (2018) Deep recursive super resolution network with Laplacian pyramid for better agricultural pest surveillance and detection. Comput Electron Agric 150:26–32. https://doi.org/10.1016/j.compag.2018.04.004

    Article  Google Scholar 

  32. Mathulaprangsan S, Lanthong K, Jetpipattanapong D, Sateanpattanakul S, Patarapuwadol S (2020) Rice diseases recognition using effective deep learning models. 386–389. https://doi.org/10.1109/ECTIDAMTNCON48261.2020.9090709

  33. Verma T, Dubey S (2021) Prediction of diseased rice plant using video processing and LSTM-simple recurrent neural network with comparative study. Multimed Tools Appl 80:29267–29298. https://doi.org/10.1007/s11042-021-10889-x

    Article  Google Scholar 

  34. Deng R, Tao M, Xing H, Yang X, Liu C, Liao K, Qi L (2021) Automatic diagnosis of rice diseases using deep learning. Front Plant Sci 12:701038. https://doi.org/10.3389/fpls.2021.701038

    Article  Google Scholar 

  35. Islam M, Shuvo M, Shamsojjaman M, Hasan S, Hossain M, Khatun T (2021) An automated convolutional neural network based approach for paddy leaf disease detection. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2021.0120134

    Article  Google Scholar 

  36. https://www.kaggle.com/bahribahri/riceleaf. Accessed on 26 Dec 2020

  37. Bodapati JD, Veeranjaneyulu N (2019) Feature extraction and classification using deep convolutional neural networks. J Cyber Secur Mobil 8(2):261–276. https://doi.org/10.13052/jcsm2245-1439.825

    Article  Google Scholar 

  38. Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  39. Liu J, Yang S, Cheng Y, Song Z (2018) Plant leaf classification based on deep learning. In: 2018 Chinese Automation Congress (CAC). https://doi.org/10.1109/cac.2018.8623427

  40. Phung VH, Rhee EJ (2018) A deep learning approach for classification of cloud image patches on small datasets. J Inf Commun Converg Eng 16:173–178. https://doi.org/10.6109/jicce.2018.16.3.173

    Article  Google Scholar 

  41. Liu B, Zhang Y, He DJ, Li Y (2017) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(1):11. https://doi.org/10.3390/sym10010011

    Article  Google Scholar 

  42. Zhu L, Li Z, Li C, Wu J, Yue J (2018) High performance vegetable classification from images based on AlexNet deep learning model. Int J Agric Biol Eng 11(4):190–196. https://doi.org/10.25165/j.ijabe.20181104.2690

    Article  Google Scholar 

Download references

Author information

Author notes

  1. Santosh Kumar Upadhyay

    Present address: Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, India

Authors and Affiliations

  1. School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Santosh Kumar Upadhyay & Avadhesh Kumar

Authors
  1. Santosh Kumar Upadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Avadhesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santosh Kumar Upadhyay.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Upadhyay, S.K., Kumar, A. A novel approach for rice plant diseases classification with deep convolutional neural network. Int. j. inf. tecnol. 14, 185–199 (2022). https://doi.org/10.1007/s41870-021-00817-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-021-00817-5

Keywords

Search

Navigation