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Leaf images classification for the crops diseases detection

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Abstract

Crop production affects the economy of a specific region or country. To maintain the economic development of any territory crops disease detection is a leading factor in agriculture. There exists various techniques to detect the crops disease, but they offers lilimted accuracy. The proposed mulistage technique utilizes leaf region separation from background prior to the feature extraction for classification of images as healthy and diseased. Initially color transformation-based foreground separation is performed followed by the features extraction. The novelty of the method is attruibuts that are computed though adaptive analytic wavelet transform (AAWT). The AAWT decomposes the preprocessed images into various sub-band images as features. The attributes utilized for categorization are a combination of bag of visual word, Fisher vectors, and AAWT based-features extracted from leaf images. Performance of the proposal is analyzed through PlantVillage dataset. The simulation outcomes validate the superiority of the proferred classification system as compared with the existing techniques of the field. The proposed leaf classification method provides an average accuracy of 94.07% with the area under the characteristic curve 0.961.

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References

  1. Acharya UR, Bhat S, Koh JE, Bhandary SV, Adeli H (2017) A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images. Comput Biol Med 88:72–83

    Article  Google Scholar 

  2. Acharya UR, Dua S, Du X, Sree VS, Chua CK (2011) Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans Inf Technol Biomed 15(3):449–455

    Article  Google Scholar 

  3. Acharya UR, Mookiah MRK, Koh JE, Tan JH, Noronha K, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A (2016) Novel risk index for the identification of age-related macular degeneration using radon transform and dwt features. Comput Biol Med 73:131–140

    Article  Google Scholar 

  4. Aparajita R, Sharma A, Singh M, Dutta K, Riha K, Kriz P (2017) Image processing based automated identification of late blight disease from leaf images of potato crops. In: 2017 40th international conference on telecommunications and signal processing (TSP), pp 758–762

  5. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Patt Anal Mach Intell 33 (5):898–916

    Article  Google Scholar 

  6. Barbedo J (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springer Plus 2:660, 12

    Google Scholar 

  7. Bayram I (2013) An analytic wavelet transform with a flexible time-frequency covering. IEEE Trans Signal Process 61(5):1131–1142

    Article  MathSciNet  MATH  Google Scholar 

  8. Beucher S, Meyer F (1993) The morphological approach to segmentation. The Watershed Transformation 34:433–481

    Google Scholar 

  9. Biswas MK, Ghose T, Guha S, Biswas PK (1998) Fractal dimension estimation for texture images: a parallel approach. Pattern Recogn Lett 19(3):309–313

    Article  MATH  Google Scholar 

  10. Biswas S, Jagyasi B, Singh BP, Lal M (2014) Severity identification of potato late blight disease from crop images captured under uncontrolled environment. In: 2014 IEEE Canada international humanitarian technology conference - (IHTC), pp 1–5

  11. Chaurasia V, Chaurasia V (2016) Statistical feature extraction based technique for fast fractal image compression. J Vis Commun Image Represent 41:87–95

    Article  Google Scholar 

  12. Chen Y, Ma Y, Kim DH, Park S (2015) Region-based object recognition by color segmentation using a simplified pcnn. IEEE Trans Neur Netw Learn Syst 26(8):1682–1697

    Article  MathSciNet  Google Scholar 

  13. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  MATH  Google Scholar 

  14. Cuingnet R, Glaunès J. A., Chupin M, Benali H, Colliot O (2013) Spatial and anatomical regularization of svm: a general framework for neuroimaging data. IEEE Trans Pattern Anal Mach Intell 35(3):682–696

    Article  Google Scholar 

  15. Dean R, Van Kan JAL, Pretorius ZA, Hammond-Kosack KE, Di Pietro A, Spanu PD, Rudd JJ, Dickman M, Kahmann R, Ellis J, Foster GD (2012) The top 10 fungal pathogens in molecular plant pathology. Mol Plant Pathol 13(4):414–430

    Article  Google Scholar 

  16. Dua S, Acharya UR, Chowriappa P, Sree SV (2012) Wavelet-based energy features for glaucomatous image classification. IEEE Trans Inf Technol Biomed 16(1):80–87

    Article  Google Scholar 

  17. Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874

    MATH  Google Scholar 

  18. Fang Y, Ramasamy R (2015) Current and prospective methods for plant disease detection. Biosensors 5:537–61, 08

    Article  Google Scholar 

  19. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. ROC Analysis in Pattern Recognition

    Article  MathSciNet  Google Scholar 

  20. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2):179–188. [Online]. Available: https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

    Article  Google Scholar 

  21. Gonzalez REW, Rafael C, Eddins SL (2004) Digital Image Processing Using MATLAB, ser. International series of monographs on physics. Upper Saddle River, Prentice Hall

    Google Scholar 

  22. Grand-Brochier M, Vacavant A, Cerutti G, Kurtz C, Weber J, Tougne L (2015) Tree leaves extraction in natural images: comparative study of preprocessing tools and segmentation methods. IEEE Trans Image Process 24(5):1549–1560

    Article  MathSciNet  MATH  Google Scholar 

  23. Gupta V, Chopda MD, Pachori RB (2019) Cross-subject emotion recognition using flexible analytic wavelet transform from eeg signals. IEEE Sensors J 19(6):2266–2274

    Article  Google Scholar 

  24. Hančinský R, Mihálik D, Mrkvová M, Candresse T, Glasa M (2020) Plant viruses infecting solanaceae family members in the cultivated and wild environments: a review, Plants. Basel Switzerland 9(5):1–17

    Google Scholar 

  25. Hojjatoleslami SA, Kittler J (1998) Region growing: a new approach. IEEE Trans Image Process 7(7):1079–1084

    Article  Google Scholar 

  26. Iftekharuddin K, Jia W, Marsh R (2003) Fractal analysis of tumor in brain mr images. Mach Vis Appl 13:352–362, 03

    Article  Google Scholar 

  27. Islam M, Dinh Anh, Wahid K, Bhowmik P (2017) Detection of potato diseases using image segmentation and multiclass support vector machine. In: 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE), pp 1–4

  28. Kaur S, Pandey S, Goel S (2018) Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Process 12 (6):1038–1048

    Article  Google Scholar 

  29. Keras (2018) Keras Documentation, https://keras.io. Accessed 2 Feb 2018

  30. 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 

  31. Kim J, Han D, Tai Y, Kim J (2016) Salient region detection via high-dimensional color transform and local spatial support. IEEE Trans Image Process 25(1):9–23

    Article  MathSciNet  MATH  Google Scholar 

  32. Klein A, Falkner S, Bartels S, Hennig P, Hutter F (2017) Fast Bayesian optimization of machine learning hyperparameters on large datasets. In: Proceedings of the 20th international conference on artificial intelligence and statistics (AISTATS 2017), ser. Proceedings of Machine Learning Research, vol 54. PMLR, pp 528–536

  33. Kurmi Y, Chaurasia V (2018) Multifeature-based medical image segmentation. IET Image Process 12(8):1491–1498

    Article  Google Scholar 

  34. Kurmi Y, Chaurasia V (2020) Classification of magnetic resonance images for brain tumor detection. IET Image Process 1–13

  35. Kurmi Y, Chaurasia V, Ganesh N (2019) Tumor malignancy detection using histopathology imaging. Journal of Medical Imaging and Radiation Sciences

  36. Kurmi Y, Chaurasia V, Ganesh N, Kesharwani A (2020) Microscopic images classification for cancer diagnosis. Signal Image and Video Processing 14 (4):665–673

    Article  Google Scholar 

  37. Kurmi Y, Gangwar S, Agrawal D, Kumar S, Saxena D, Saxena M, Shrivastava H (2020) An algorithm for various crop diseases detection and classification using leaves images. In: 2nd international conference on data engineering and applications (IDEA), pp 1–5

  38. Kurmi Y, Gangwar S, Agrawal D, Kumar S, Srivastava HS (2020) Leaf image analysis-based crop diseases classification. Signal Image and Video Processing 1–9

  39. Li M, Yuan B (2005) 2D-lda: a statistical linear discriminant analysis for image matrix. Pattern Recogn Lett 26(5):527–532

    Article  Google Scholar 

  40. Liu L, Wang P, Shen C, Wang L, Hengel AVD, Wang C, Shen HT (2017) Compositional model based fisher vector coding for image classification. IEEE Trans Pattern Anal Mach Intell 39(12):2335–2348

    Article  Google Scholar 

  41. Mansfield J, Genin S, Magori S, Citovsky V, Sriariyanum M, Ronald P, Dow M, Verdier V, Beer SV, Machado MA, Toth I, Salmond G, Foster GD (2012) Top 10 plant pathogenic bacteria in molecular plant pathology. Mol Plant Pathol 13(6):614–629

    Article  Google Scholar 

  42. Martis RJ, Acharya UR, Min LC (2013) Ecg beat classification using pca, lda, ica and discrete wavelet transform. Biomed Signal Process Cont 8 (5):437–448

    Article  Google Scholar 

  43. Mercan E, Aksoy S, Shapiro LG, Weaver DL, Brunye T, Elmore JG (2014) Localization of diagnostically relevant regions of interest in whole slide images. In: 2014 22nd international conference on pattern recognition, pp 1179–1184

  44. Mu H, Ni H, Zhang M, Yang Y, Qi D (2019) Tree leaf feature extraction and recognition based on geometric features and haar wavelet theory. Engineering in Agriculture, Environment and Food

  45. Neto JC, Meyer GE, Jones DD (2006) Individual leaf extractions from young canopy images using gustafson–kessel clustering and a genetic algorithm. Comput Electron Agric 51(1):66–85

    Article  Google Scholar 

  46. Patil P, Yaligar N, M. S. M (2017) Comparision of performance of classifiers - svm, rf and ann in potato blight disease detection using leaf images. In: 2017 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–5

  47. Pedregosa F, Varoquaux G, Gramfort A. e. a. (2011) Scikit-learn: machine learning in python. J Mach Learn Res 384(12):2825–2830

    MathSciNet  MATH  Google Scholar 

  48. Qin F, Liu D, Sun B, Ruan L, Ma Z, Wang H (2016) Identification of alfalfa leaf diseases using image recognition technology. PLOS ONE 11 (12):1–26, 12

    Article  Google Scholar 

  49. Raji CG, Vinod Chandra SS (2017) Long-term forecasting the survival in liver transplantation using multilayer perceptron networks. IEEE Trans Syst Man Cybern Syst 47(8):2318–2329

    Article  Google Scholar 

  50. Rumpf T, Mahlein A-K, Steiner U, Oerke E-C, Dehne H-W, Plümer L (2010) Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric 74(1):91–99

    Article  Google Scholar 

  51. Sabrol H, Satish K (2016) Tomato plant disease classification in digital images using classification tree. In: 2016 international conference on communication and signal processing (ICCSP), pp 1242–1246

  52. 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

    Article  Google Scholar 

  53. Scharr H, Minervini M, French AP, Klukas C, Kramer DM, Liu X, Luengo I, Pape J-M, Polder G, Vukadinovic D, Yin X, Tsaftaris SA (2016) Leaf segmentation in plant phenotyping: a collation study. Mach Vis Appl 27(4):585–606

    Article  Google Scholar 

  54. Schor N, Bechar A, Ignat T, Dombrovsky A, Elad Y, Berman S (2016) Robotic disease detection in greenhouses: Combined detection of powdery mildew and tomato spotted wilt virus. IEEE Robot Autom Lett 1(1):354–360

    Article  Google Scholar 

  55. Sharma M, Pachori RB, Acharya UR (2017) A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn Lett 94:172–179

    Article  Google Scholar 

  56. Silva L, Koga M, Cugnasca C, Costa A (2013) Comparative assessment of feature selection and classification techniques for visual inspection of pot plant seedlings. Comput Electron Agric 97:47–55

    Article  Google Scholar 

  57. Singh V, Misra A (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inform Process Agricult 4(1):41–49

    Article  Google Scholar 

  58. Soares JAV, Jacobs DW (2013) Efficient segmentation of leaves in semi-controlled conditions. Mach Vision Appl 24(8):1623–1643. [Online]. Available: https://doi.org/10.1007/s00138-013-0530-0

    Article  Google Scholar 

  59. Team GB (2018) TensorFlow, https://www.tensorflow.org/. Accessed 2 Feb 2018

  60. Teng C-H, Kuo Y-T, Chen Y-S (2011) Leaf segmentation, classification, and three-dimensional recovery from a few images with close viewpoints. Opt Eng 50(3):1–13

    Google Scholar 

  61. Wang J, He J, Han Y, Ouyang C, Li D (2013) An adaptive thresholding algorithm of field leaf image. Comput Electron Agric 96:23–39

    Article  Google Scholar 

  62. Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Computational Intelligence and Neuroscience 1–8, 07 2017

  63. Wang S, Yan W, Li X, Zhao G, Zhou C, Fu X, Yang M, Tao J (2015) Micro-expression recognition using color spaces. IEEE Trans Image Process 24(12):6034–6047

    Article  MathSciNet  MATH  Google Scholar 

  64. Wu J, Zhang B, Zhou J, Xiong Y, Gu B, Yang X (2019) Automatic recognition of ripening tomatoes by combining multi-feature fusion with a bi-layer classification strategy for harvesting robots in. Sensors

  65. Xu D, Erdogmuns D (2010) Renyi’s entropy, divergence and their nonparametric estimators. Springer, New York, pp 47–102

    Google Scholar 

  66. Xu G, Zhang F, Shah SG, Ye Y, Mao H (2011) Use of leaf color images to identify nitrogen and potassium deficient tomatoes. Pattern Recogn Lett 32(11):1584–1590

    Article  Google Scholar 

  67. Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  68. Yanikoglu B, Aptoula E, Tirkaz C (2014) Automatic plant identification from photographs. Mach Vision Appl 25(6):1369–1383

    Article  Google Scholar 

  69. Zhang X, Hu B, Ma X, Xu L (2015) Resting-state whole-brain functional connectivity networks for MCI classification using l2-regularized logistic regression. IEEE Transactions on NanoBioscience 14(2):237–247

    Article  Google Scholar 

  70. Zhang C, Li B, Chen B, Cao H, Zi Y, He Z (2015) Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. Mech Syst Signal Process 64-65:162–187

    Article  Google Scholar 

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Authors and Affiliations

  1. ECE Department, Madhyanchal Processional Univerity Bhopal, Ratibad, India

    Yashwant Kurmi

  2. Radiology, Mayo Clinic, Rochester, MN, USA

    Yashwant Kurmi

  3. Agriculture Department, RKDF University, Bhopal, India

    Suchi Gangwar

  4. ECE Department, Maulana Azad National Institute of Technology, Bhopal, India

    Vijayshri Chaurasia & Aditya Goel

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  1. Yashwant Kurmi
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  2. Suchi Gangwar
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  3. Vijayshri Chaurasia
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  4. Aditya Goel
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Correspondence to Yashwant Kurmi.

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Kurmi, Y., Gangwar, S., Chaurasia, V. et al. Leaf images classification for the crops diseases detection. Multimed Tools Appl 81, 8155–8178 (2022). https://doi.org/10.1007/s11042-022-11910-7

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