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Integration of nondecimated quaternion wavelet transform and neighborhood texture patterns for disease classification in banana (Musa spp.) foliage

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Abstract

Fungal diseases of banana is a global threat to banana production and cultivation. Diagnosis of these diseases using an identification system limited by human visual capabilities often lead to misinterpretation of diseases, causing severe yield losses. An automated fungal disease identification method in banana plants using nondecimated quaternion wavelet Transform (NDQWT) and texture features is being detailed in this paper. The discoloration pattern appearing on banana leaf blade, at the early stages of infection, are used for classifying three major fungal diseases namely, Sigatoka, Cordana and Deightoneilla. The captured leaf images are enhanced and segmentation algorithms are applied to identify the infected regions. The segmented images are converted to transform domain using spatial-frequency transforms (discrete wavelet transforms (DWT), dual tree complex wavelet transforms (DTCWT) and NDQWT). Texture features are extracted from transform domain images using local neighborhood patterns. The feature vectors are applied to five popular classifiers and performance metrics are estimated. A comparative analysis of these methods shows best classification performance (accuracy, precision, sensitivity, specificity, F-score) for gradient directional pattern (GDP) and noise resistant local binary pattern (NRLBP) based feature vectors extracted from combined magnitude and phase components of NDQWT transformed images. To the best of our knowledge, this methodology of feature extraction from combined phase and magnitude components of NDQWT transformed images is novel and efficient when compared with traditional methods.

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Data Availability

The dataset generated during the current study are available from the corresponding author on reasonable request.

References

  1. Agarwal M, Gupta SK, Biswas K (2020) Development of efficient cnn model for tomato crop disease identification. Sustain Comput Inform Syst 28:100407

    Google Scholar 

  2. Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. Lect Notes Inform (LNI), Proc - Ser Ges fur Inform (GI) 266:79–88

    Google Scholar 

  3. Aruraj A, Alex A, Subathra MSP, Sairamya NJ, George ST, Ewards SEV (2019) Detection and classification of diseases of banana plant using local binary pattern and support vector machine. In: 2nd Int conf signal process commun ICSPC 2019 - proc, pp 231–235

  4. Basavaiah J, Arlene Anthony A (2020) Tomato leaf disease classification using multiple feature extraction techniques. Wirel Pers Commun 115(1):633–651

    Google Scholar 

  5. Bharath R, Mishra PK, Rajalakshmi P (2018) Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD. Biocybern Biomed Eng 38(1):145–157

    Google Scholar 

  6. Bulow T (1999) Hypercomplex Spectral Signal Representations for the Processing and Analysis of Images. PhD thesis, Christian-Albrechts-Universitat zu Kiel

    MATH  Google Scholar 

  7. 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

    Google Scholar 

  8. Dhingra D, Kumar V, Joshi HD (2019) A novel computer vision based neutrosophic approach for leaf disease identification and classification. J Int Meas Confed 135:782–794. https://doi.org/10.1016/j.measurement.2018.12.027

    Google Scholar 

  9. Dhingra G, Kumar V, Joshi HD (2019) A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement 135:782–794

    Google Scholar 

  10. Ding S, Zhao H, Zhang Y, Xu X, Nie R (2015) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44(1):103–115. https://doi.org/10.1007/s10462-013-9405-z

    Google Scholar 

  11. Diwakar M, Kumar P, Singh AK (2020) Ct image denoising using nlm and its method noise thresholding. Multimed Tools Appl 79(21):14449–14464

    Google Scholar 

  12. Diwakar M, Singh P (2020) Ct image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain. Biomed Signal Process Control 57:101754

    Google Scholar 

  13. Diwakar M, Verma A, Lamba S, Gupta H (2019) Inter-and intra-scale dependencies-based ct image denoising in curvelet domain. In: Soft comput Theories Appl, pp 343–350

  14. FAO (2021) Banana market review 2020. Banan Mark Rev, vol 20. fao.org/3/cb6639en/cb6639en.pdf

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

    Google Scholar 

  16. Gajalakshmi K, Palanivel S, Nalini NJ, Saravanan S (2018) Automatic classification of cast iron grades using support vector machine. Optik (Stuttg) 157:724–732. https://doi.org/10.1016/j.ijleo.2017.11.183

    Google Scholar 

  17. Gross MH, Koch R, Lippert L, Dreger A (1994) Multiscale image texture analysis in wavelet spaces. In: Proc. Int conf image process, vol 3, pp 412–416. https://doi.org/10.1109/ICIP.1994.413816

  18. Ilhan HO, Serbes G, Aydin N (2018) Dual tree complex wavelet transform based sperm abnormality classification. In: Int Conf Tele Signal Process (TSP), pp 1–5. https://doi.org/10.1109/TSP.2018.8441431

  19. Iqbal Z, Khana MA, Sharif M, Shah JH, Rehman MH, Javed K (2018) An automated detection and classification of citrus plant diseases using image processing techniques: a review. Comput Electron Agric 153:12–32

    Google Scholar 

  20. Islam ST, Mazumder B (2019) Wavelet based feature extraction for rice plant disease detection and classification. In: 2019 3rd Int conf electr comput telecommun eng, pp 53–56

  21. Johannes A, Picon A, Alvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S, Navajas A, Ortiz-Barredo A (2017) Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput Electron Agric 138:200–209. https://doi.org/10.1016/j.compag.2017.04.013

    Google Scholar 

  22. Jones DR (2000) Diseases of banana abaca and enset

  23. Jose S, Thomas George S, Roopchand P (2020) Dwt-based electromyogram signal classification using maximum likelihood-estimated features for neurodiagnostic applications. Signal Image Video Process 14(3):601–608

    Google Scholar 

  24. Kamal K, Yin Z, Wu M, Wu Z (2019) Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric 165:104948

    Google Scholar 

  25. 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

    Google Scholar 

  26. Kaur S, Pandey S, Goel S (2019) Plants disease identification and classification through leaf images: a survey. Arch Comput Methods Eng 26(1):507–530. https://doi.org/10.1007/s11831-018-9255-6

    Google Scholar 

  27. Koh JEW, Hagiwara Y, Oh SL, Tan JH, Ciaccio EJ, Green PH, Lewis SK, Acharya UR (2019) Automated diagnosis of celiac disease using dwt and nonlinear features with video capsule endoscopy images. Future Gener Comput Syst 90:86–93

    Google Scholar 

  28. Kong T, Vidakovic B (2019) Non-decimated quaternion wavelet spectral tools with applications. arXiv:1903.00790

  29. Kumar P, Diwakar M (2021) A novel approach for multimodality medical image fusion over secure environment. Trans Emerg Telecommun Technol 32 (2):3985

    Google Scholar 

  30. Kumar V, Gokulpriya DA, Subharatha R, Dinesh V (2018) Banana tall plant disease detection and classification using image processing and artificial neural network. Int J Adv Eng Res Sci 3:452–459

    Google Scholar 

  31. Kumari CU, Prasad SJ, Mounika G (2019) Leaf disease detection: feature extraction with k-means clustering and classification with ann. In: Proc 3rd int conf comput methodol commun ICCMC, vol 2019, pp 1095–1098

  32. Liu L, Lao S, Fieguth PW, Guo Y, Wang X, Pietikäinen M (2016) Median robust extended local binary pattern for texture classification. IEEE Trans Image Process 25(3):1368–1381

    MathSciNet  MATH  Google Scholar 

  33. Mary AB, Singh AR, Athisayamani S (2020) Banana leaf diseased image classification using novel heap auto encoder (hae) deep learning. Multimed Tools Appl 79(41):30601–30613

    Google Scholar 

  34. Mathew D, Kumar CS, Cherian A (2020) Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain. Inf Process Agric. 8(4):581–592. https://doi.org/10.1016/j.inpa.2020.11.002

    Google Scholar 

  35. Mustafa MS, Husin Z, Tan WK, Mavi MF, Farook RSM (2020) Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Comput Appl 32(15):11419–11441. https://doi.org/10.1007/s00521-019-04634-7

    Google Scholar 

  36. Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J, Johannes A (2019) Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric 161:280–290

    Google Scholar 

  37. Pridham G, Oladosu O, Zhang Y (2020) Evaluation of discrete orthogonal versus polar stockwell transform for local multi-resolution texture analysis using brain mri of multiple sclerosis patients. Magn Reson Imaging 72:150–158

    Google Scholar 

  38. Ren J, Jiang X, Yuan J (2013) Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans Image Process 22 (10):4049–4060. https://doi.org/10.1109/TIP.2013.2268976

    MathSciNet  MATH  Google Scholar 

  39. Salman A, Semwal A, Bhatt U, Thakkar VM (2017) Leaf classification and identification using canny edge detector and svm classifier. In: Proc Int Conf Inven Syst Control(ICISC 2017), pp 1–4. https://doi.org/10.1109/ICISC.2017.8068597

  40. Selvaraj MG, Vergara A, Ruiz H, Safari N, Elayabalan S, Ocimati W, Blomme G (2019) Ai-powered banana diseases and pest detection. Plant Methods 15(1):1–11. https://doi.org/10.1186/s13007-019-0475-z

    Google Scholar 

  41. 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. https://doi.org/10.1016/j.compag.2018.04.023

    Google Scholar 

  42. Sharma M, Sharma P, Pachori RB, Acharya UR (2018) Dual-tree complex wavelet transform-based features for automated alcoholism identification. Int J Fuzzy Syst 20(4):1297–1308. https://doi.org/10.1007/s40815-018-0455-x

    Google Scholar 

  43. Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4(1):41–49

    Google Scholar 

  44. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437. https://doi.org/10.1016/j.ipm.2009.03.002

    Google Scholar 

  45. Sood M, Singh PK, et al. (2020) Hybrid system for detection and classification of plant disease using qualitative texture features analysis. Proced Comput Sci 167:1056–1065

    Google Scholar 

  46. Srinivas J, Qyser AM, Reddy BE (2018) Classification of textures based on circular and elliptical weighted symmetric texture matrix. Period Eng Nat Sci 7(3.27):593–600

    Google Scholar 

  47. 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. https://doi.org/10.1016/j.compag.2018.12.042

    Google Scholar 

  48. Tigadi B, Sharma B (2016) Banana plant disease detection and grading using image processing. Int J Eng Sci Comput 6(6):6512–6516

    Google Scholar 

  49. Turan C, Lam K (2018) Histogram-based local descriptors for facial expression recognition (fer): a comprehensive study. J Vis Commun Image Represent 55:331–341

    Google Scholar 

  50. Vipindas MJ, Thamizharasi A (2016) Banana leaf disease identification technique. Int J Adv Eng Res Sci 3(6):120–124

    Google Scholar 

  51. Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci, vol 2017. https://doi.org/10.1155/2017/2917536

  52. Xie C, Yang C, He Y (2017) Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities. Comput Electron Agric 135:154–162. https://doi.org/10.1016/j.compag.2016.12.015

    Google Scholar 

  53. Yang P, Yang G (2020) Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix. Neurocomputing 197:212–220. https://doi.org/10.1016/j.neucom.2016.02.061

    Google Scholar 

  54. Yepez J, Ko SB (2018) Improved license plate localisation algorithm based on morphological operations. IET Intell Transp Syst 6(12):542–549. https://doi.org/10.1049/iet-its.2017.0224

    Google Scholar 

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Funding

This work has received funding from Center for Engineering Research and Development, Government of Kerala, India, vide grant no. KTU/Research/2743/2017.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Rajiv Gandhi Institute of Technology (Affiliated to APJ Abdul Kalam Technological University), Pampady, Kottayam, 686501, Kerala, India

    Deepthy Mathew & C. Sathish Kumar

  2. Department of Plant Pathology, College of Agriculture, Kerala Agricultural University, Thrissur, 680656, Kerala, India

    K. Anita Cherian

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  1. Deepthy Mathew
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Correspondence to C. Sathish Kumar.

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Mathew, D., Kumar, C.S. & Cherian, K.A. Integration of nondecimated quaternion wavelet transform and neighborhood texture patterns for disease classification in banana (Musa spp.) foliage. Multimed Tools Appl 82, 37327–37349 (2023). https://doi.org/10.1007/s11042-023-14869-1

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