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Detection and classification of diseased plant leaf images using hybrid algorithm

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Abstract

Plant disease reduces the quantity and quality of the agricultural product, so identification of plant disease in the early stages is very important. Early detection of disease in plants helps to reduce the overuse of pesticides as well as save plants from further damage. In this research work, we designed a plant disease identification system using image processing and machine learning techniques. Segmentation of the leaf image is one of the strategies for extracting the diseased part of a leaf, which will be given to an automated plant disease recognition system. The challenge of the K-means algorithm is the selection of the optimal cluster number and cluster centroid initialization. The image is segmented using a hybrid clustering (Genetic Algorithm+K-means) algorithm. This hybrid algorithm helps to overcome the drawback of the local optimization problem of k-means algorithm and it selects the number of clusters automatically. Disease is classified using the Artificial neural network (ANN). This proposed algorithm experimental results are compared with traditional k-means.

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Data availability

The datasets analyzed during the current study are available from the Corresponding Author and it can be shared on Reasonable Request.

References

  1. Al-Hiary H, Bani-Ahmad S, Reyalat M, Braik M, Alrahamneh Z (2011) Fast and accurate detection and classification of plant diseases. Mach Learn 14(5):31–38

    Google Scholar 

  2. Bashish A, Dheeb MB, Bani-Ahmad S (2011) Detection and classification of leaf diseases using K-means-based segmentation. Inf Technol J 10(2):267–275

    Article  Google Scholar 

  3. Bhanu B, Lee S (2012) Genetic learning for adaptive image segmentation. Springer Science & Business Media

    MATH  Google Scholar 

  4. Bhowmik, Santanu, viki Datta (2012) A survey on clustering based image segmentation. Int J Adv Res Comput Eng Technol:2278–1323

  5. Bora DJ, Gupta AK, Khan FA (2015) Comparing the performance of L* a* B* and HSV color spaces with respect to color image segmentation. Int J Emerg Technol Adv Eng 5(2):192–203

    Google Scholar 

  6. Cheng HD, Jiang XH, Sun Y, Wang J (2001) Color image segmentation: advances and prospects. Pattern Recogn 34(12):2259–2281. https://doi.org/10.1016/S0031-3203(00)00149-7

    Article  MATH  Google Scholar 

  7. Cheng HD, Jiang XH, Wang J (2002) Color image segmentation based on homogram thresholding and region merging. Pattern Recogn 35(2):373–393. https://doi.org/10.1016/S0031-3203(01)00054-1

    Article  MATH  Google Scholar 

  8. Chouhan P, Tiwari M (2015) Image retrieval using data mining and image processing techniques. Int J Innov Res Electr Electron Instrum Control Eng 3(12):53–58

    Google Scholar 

  9. Fu KS, Mui JK (1981) A survey on image segmentation. Pattern Recogn 13(1):3–16. https://doi.org/10.1016/0031-3203(81)90028-5

    Article  MathSciNet  Google Scholar 

  10. Gaikwad DS, Karande KJ (2016) Image processing approach for grading and identification of diseases on pomegranate fruit: an overview. Int J Comput Sci Inform Technol 7(2):519–522

    Google Scholar 

  11. Gonzalez RC, Woods RE (2007) Digital image processing, 3rd edition.

  12. Haldurai L (2016) A study on genetic algorithm and its applications. Int J Comput Sci Eng 4:139–143

    Google Scholar 

  13. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier

    MATH  Google Scholar 

  14. Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comput Vis Graph Image Process 29(1):100–132. https://doi.org/10.1016/S0734-189X(85)90153-7

    Article  Google Scholar 

  15. Hazlyna HN, Mashor MY, Mokhtar NR, Salihah AA, Hassan R, Raof RAA, Osman MK (2010) Comparison of acute leukemia image segmentation using HSI and RGB color space. International Conference on Inf. Sci. Signal Processing and their Appl. IEEE

  16. Hinz J (2013) Clustering the web: comparing clustering methods in Swedish

  17. Issad HA, Aoudjit R, Rodrigues JJ (2019) A comprehensive review of data mining techniques in smart agriculture. Eng Agric Environ Food 12(4):511–525. https://doi.org/10.1016/j.eaef.2019.11.003

    Article  Google Scholar 

  18. Jacquet F, Jeuffroy MH, Jouan J, Le Cadre E, Litrico I, Malausa T, Huyghe C (2022) Pesticide-free agriculture as a new paradigm for research. Agron Sustain Dev 42(1):1–24. https://doi.org/10.1007/s13593-021-00742-8

    Article  Google Scholar 

  19. Jain S, Laxmi V (2018) Color image segmentation techniques: a survey. In: Proceedings of the international conference on microelectronics, Computing & Communication Systems. Springer, Singapore. pp. 189-197

  20. Jung YG, Kang MS, Heo J (2014) Clustering performance comparison using K-means and expectation maximization algorithms. Biotechnol Biotechnol Equip 28(sup1):S44–S48. https://doi.org/10.1080/13102818.2014.949045

    Article  Google Scholar 

  21. Kaushik B, Amit S, Shukla KK, Rupankar B (2016) Application and scope of data mining in agriculture. Int J Adv Eng Res Sci 3(7):66–69

    Google Scholar 

  22. Khirade SD, Patil AB (2015) Plant disease detection using image processing, Computing communication control and automation. IEEE. pp. 768-771

  23. Madhavan MV, Thanh DNH, Khamparia A, Pande S, Malik R, Gupta D (2021) Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques. Comput Mater Contin 66(3):2939–2955

    Google Scholar 

  24. Mallikarjuna B, Jagadeesh Babu B, Imran D, Chandrashekhar K, Rajasekhar D (2020) Detection of leaf diseases using image segmentation. Int J Creat Res Thoughts 8(5):521–527

    Google Scholar 

  25. Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33(9):1455–1465. https://doi.org/10.1016/S0031-3203(99)00137-5

    Article  Google Scholar 

  26. Meyer F (1992) Color image segmentation. International conference on image processing and its Appls. IET. pp. 303-306

  27. Mitchell M (1998) An introduction to genetic algorithms. MIT press

    Book  MATH  Google Scholar 

  28. Mohanta RK, Sethi B (2011) A review of genetic algorithm application for image segmentation. Int J Comput Technol Appl 3(2):720–723

    Google Scholar 

  29. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https://doi.org/10.3389/fpls.2016.01419

    Article  Google Scholar 

  30. Muthukannan K, Latha P (2018) A GA_FFNN algorithm applied for classification in diseased plant leaf system. Multimed Tools Appl 77(18):24387–24403

    Article  Google Scholar 

  31. Prasad Babu MS, Srinivasa Rao B (2007) Leaves recognition using Back propagation neural network-advice for Pest and disease control on crops. IndiaKisan.Net: Expert Advissory System

  32. Pratheba R, Sivasangari A, Saraswady D (2014) Performance analysis of pest detection for agricultural field using clustering techniques. International conference on circuits, power and computing technologies. IEEE. pp. 1426-1431

  33. Rathod AN, Tanawal B, Shah V (2013) Image processing techniques for detection of leaf disease. Int J Adv Res Comput Sci Softw Eng 3:11

    Google Scholar 

  34. Rawat S (2020) Global volatility of public agricultural R&D expenditure. In: Advances in food security and sustainability, vol 5, pp 119–143. https://doi.org/10.1016/bs.af2s.2020.08.001

    Chapter  Google Scholar 

  35. 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 

  36. Selim SZ, Ismail MA (1984) K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. IEEE Trans Pattern Anal Mach Intell PAMI-6(1):81–87

    Article  MATH  Google Scholar 

  37. Senthilkumaran N, Rajesh R (2009) Image segmentation-a survey of soft computing approaches. In: 2009 international conference on advances in recent Technologies in Communication and Computing, IEEE. pp. 844-846

  38. Shedthi BS, Shetty S, Siddappa M (2017) Implementation and comparison of K-means and fuzzy C-means algorithms for agricultural data. In: International conference on inventive communication and computational technologies (ICICCT-2017) (pp. 105-108). IEEE. https://doi.org/10.1109/ICICCT.2017.7975168

  39. Shen C, Wang D, Tang S, Cao H, Liu J (2017) Hybrid image noise reduction algorithm based on genetic ant colony and PCNN. Vis Comput 33(11):1373–1384. https://doi.org/10.1007/s00371-016-1325-x

    Article  Google Scholar 

  40. Shrutika I, Baru VB (2019) Plant leaf disease detection recognition using machine learning. Int J Eng Res Technol 8(6):1179–1182

    Google Scholar 

  41. Singh V, Misra AK (2015) Detection of unhealthy region of plant leaves using image processing and genetic algorithm. International conference on advances in computer engineering and applications. IEEE. pp. 1028-1032

  42. Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4(1):41–49. https://doi.org/10.1016/j.inpa.2016.10.005

    Article  Google Scholar 

  43. Srinivasa Reddy A, Malleswari G (2016) Significance of genetic algorithm in image segmentation. Int J Signal Process Image Process Pattern Recognit 9(4):177–184

    Google Scholar 

  44. Suarez AJB, Singh B, Almukhtar FH, Kler R, Vyas S, Kaliyaperumal K (2022) Identifying smart strategies for effective agriculture solution using data mining techniques. J Food Qual 2022:1–9. https://doi.org/10.1155/2022/6600049

    Article  Google Scholar 

  45. Tuba E, Jovanovic R, Tuba M (2017) Plant diseases detection based on color features and Kapur’s metho. WSEAS Trans Inf Sci Appl 14:31–39

    Google Scholar 

  46. Vibhute A, Bodhe SK (2012) Applications of image processing in agriculture: a survey. Int J Comput Appl 52(2):34–40

    Google Scholar 

  47. Wang XF, Wang Z, Zhang SW (2019) Segmenting crop disease leaf image by modified fully-convolutional networks. In: International conference on intelligent computing. Springer, Cham. pp. 646–652

  48. Woods K (2007) Genetic Algorithms: Colour Image Segmentation Literature Review

  49. World Health Organization & United Nations Environment Programme (1990) Public health impact of pesticides used in agriculture. World Health Organization

    Google Scholar 

  50. Yimyam P, Clark AF (2012) Agricultural produce grading by computer vision using genetic programming. International conference on robotics and biomimetics (ROBIO). IEEE. pp 458–463. https://doi.org/10.1109/ROBIO.2012.6491009

  51. Zhang S, You Z, Wu X (2019) Plant disease leaf image segmentation based on super pixel clustering and EM algorithm. Neural Comput & Applic 31(2):1225–1232. https://doi.org/10.1007/s00521-017-3067-8

    Article  Google Scholar 

  52. Zhi-Gang C, Toshifumi K, Kenji Y, Kenichi H (2002) Image segmentation considering intensity roughness and color purity. J Softw 13(5):907–912

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, NMAM Institute of Technology (Nitte Deemed to be University), Nitte, 574110, India

    Shabari Shedthi B

  2. Department of Computer Science Engineering, Sri Siddhartha Institute of Technolgy, Tumkur, India

    M. Siddappa

  3. Department of Masters in Computer Application, NMAM Institute of Technology (Nitte Deemed to be University), Nitte, 574110, India

    Surendra Shetty

  4. Department of Machanical Engineering, NMAM Institute of Technology (Nitte Deemed to be University), Nitte, 574110, India

    Vidyasagar Shetty

  5. Department of Mechanical and Manufacturing Engineering, M S Ramaiah University of Applied Sciences, Bangaloree, 560058, India

    R. Suresh

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  1. Shabari Shedthi B
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Shedthi B, S., Siddappa, M., Shetty, S. et al. Detection and classification of diseased plant leaf images using hybrid algorithm. Multimed Tools Appl 82, 32349–32372 (2023). https://doi.org/10.1007/s11042-023-14751-0

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