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Fractional mega trend diffusion function-based feature extraction for plant disease prediction

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Abstract

Plant diseases can severely degrade the quality and productivity of any crop. Hence, an automated forecasting model can be developed to help the farmers and agricultural experts for early detection and on-time treatment of plant diseases. However, precise identification and classification of plant diseases becomes tedious when the dataset is small-sized. This motivated us to design a feature extraction technique that can produce more relevant features for small-sized datasets by performing some operations on the original features. Thus, the current study contributes towards an accurate and speedy detection of plant diseases by proposing an innovative technique, namely, Fractional Mega Trend Diffusion (FMTD) function-based feature extraction technique. The proposed feature extraction technique, i.e., FMTD Function-based Fuzzy Transformation (FFFT) extends a small dataset into a high dimensional feature space by computing new features using a novel FMTD function. In this research, two small plant diseases datasets, namely Tomato Early Blight Disease (TomEBD) and Tomato Powdery Mildew Disease (TPMD) have been used to validate the proposed approach. Resampling techniques have also been implemented in this paper to balance the imbalanced datasets and afterwards, Optimized Kernel Extreme Learning Machine (OKELM) algorithm has been used for the classification purpose. A genetic algorithm has also been used for parameter optimization while performing feature extraction and classification. The results of this study indicate that the proposed approach has achieved the accuracy ranging between 70 and 89.47% for the TomEBD dataset and between 92.27 and 100% for the TPMD dataset. The performance of the proposed approach is also tested for its efficiency using three benchmarking datasets. Conclusively, the proposed approach performed remarkably well for all the three datasets.

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Data Availability Statement

The TomEBD dataset belongs to the multiple agencies of the Govt. of India and needs approval before making it public. Therefore, with the approval of appropriate agency, data will be made public in later stage. However, the representative TomEBD dataset can be found at the following link: https://bit.ly/3BC6XwR. Moreover, the TPMD dataset can be accessed through the following link: https://bit.ly/3oSXZoF and benchmarking datasets are available at the following publicly-accessible sites: https://www.kaggle.com/uciml/pima-indians-diabetes-database. https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29. https://archive.ics.uci.edu/ml/datasets/glass+identification.

References

  1. Strange RN, Scott PR (2005) Plant disease: a threat to global food security. Annu Rev Phytopathol 43:83–116. https://doi.org/10.1146/annurev.phyto.43.113004.133839

    Article  Google Scholar 

  2. Golhani K (2018) Early Detection of orange spotting disease in oil palm using red edge parameters. Dr thesis, Univ Putra Malaysia

  3. Khirade SD, Patil AB (2015) Plant disease detection using image processing. In: 2015 International conference on computing communication control and automation. pp 768–771

  4. Verma S, Bhatia A, Chug A, Singh AP (2020) Recent advancements in multimedia big data computing for IoT applications in precision agriculture: opportunities, issues, and challenges. In: Multimedia Big Data Computing for IoT Applications. Springer, pp 391–416

  5. Bhatia A, Chug A, Singh AP (2020) Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant. In: 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN). pp 218–223

  6. Bhatia A, Chug A, Prakash SA (2020) Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. J Stat Manag Syst 23:1059–1068. https://doi.org/10.1080/09720510.2020.1799504

    Article  Google Scholar 

  7. Verma S, Chug A, Singh AP (2020) Exploring capsule networks for disease classification in plants. J Stat Manag Syst 23:307–315

    Google Scholar 

  8. Verma S, Chug A, Singh AP, et al (2019) Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant. In: Applications of Image Processing and Soft Computing Systems in Agriculture. IGI Global, pp 242–271

  9. Bhatia A, Chug A, Singh AP (2020) Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach. Int J Futur Gen Commun Netw 13:71–78

    Google Scholar 

  10. Bhatia A, Chug A, Singh AP (2021) Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants. Int J Intell Eng Inform 9:24–58

    Google Scholar 

  11. Bhatia A, Chug A, Singh AP, et al (2022) A Forecasting Technique for Powdery Mildew Disease Prediction in Tomato Plants. In: Proceedings of Second Doctoral Symposium on Computational Intelligence. pp 509–520

  12. Bhatia A, Chug A, Singh AP et al (2021) A machine learning-based spray prediction model for tomato powdery mildew disease. Indian Phytopathol. https://doi.org/10.1007/s42360-021-00430-3

    Article  Google Scholar 

  13. Zhao S, Peng Y, Liu J, Wu S (2021) Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 11:651

    Article  Google Scholar 

  14. Jones WB, Thomson SV et al (1987) Source of inoculum, yield, and quality of tomato as affected by Leveillula taurica. Plant Dis 71:266–268

    Article  Google Scholar 

  15. **do K, Evenhuis A, Kempenaar C, et al Holistic pest management against early blight disease towards sustainable agriculture. Pest Manag Sci. https://doi.org/10.1002/ps.6320

  16. Aegerter BJ, Stoddard CS, Miyao EM, et al (2014) Impact of powdery mildew (Leveillula taurica) on yield and fruit quality of processing tomatoes in California. In: XIII International Symposium on Processing Tomato 1081, pp 153–158

  17. Bakeer ART, Abdel-Latef MAE, Afifi MA, Barakat ME (2013) Validation of tomato powdery mildew forecasting model using meteorological data in Egypt. Int J Agric Sci 5:372

    Article  Google Scholar 

  18. Avenot HF, Michailides TJ (2010) Progress in understanding molecular mechanisms and evolution of resistance to succinate dehydrogenase inhibiting (SDHI) fungicides in phytopathogenic fungi. Crop Prot 29:643–651

    Article  Google Scholar 

  19. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Article  MATH  Google Scholar 

  20. Zhang J, Chen L (2019) Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis. Comput Assist Surg 24:62–72

    Google Scholar 

  21. Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl 6:20–29

    Article  Google Scholar 

  22. Branco P, Ribeiro RP, Torgo L (2016) UBL: an R package for utility-based learning. ar**v Prepr: ar**v160408079

  23. Dalal S, Vishwakarma VP (2020) A novel approach of face recognition using optimized adaptive illumination-normalization and KELM. Arab J Sci Eng 45:9977–9996. https://doi.org/10.1007/s13369-020-04566-8

    Article  Google Scholar 

  24. Dalal S, Vishwakarma VP, Sisaudia V (2018) ECG classification using kernel extreme learning machine. In: 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). pp 988–992

  25. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. 3:95–99. https://doi.org/10.1023/A:1022602019183

  26. Kaur M, Kumar V (2018) Beta chaotic map based image encryption using genetic algorithm. Int J Bifurc Chaos 28:1850132–1850816. https://doi.org/10.1142/S0218127418501328

    Article  MATH  Google Scholar 

  27. Kaur M, Kumar V (2018) Parallel non-dominated sorting genetic algorithm-II-based image encryption technique. Imaging Sci J 66:453–462. https://doi.org/10.1080/13682199.2018.1505327

    Article  Google Scholar 

  28. Sahu M, Bhurchandi KM (2016) Article: color image segmentation using genetic algorithm. Int J Comput Appl 140:15–20

    Google Scholar 

  29. Kavitha AR, Chellamuthu C (2016) Brain tumour segmentation from MRI image using genetic algorithm with fuzzy initialisation and seeded modified region growing (GFSMRG) method. Imaging Sci J 64:285–297. https://doi.org/10.1080/13682199.2016.1178412

    Article  Google Scholar 

  30. Nagarajan G, Minu RI, Muthukumar B et al (2016) Hybrid genetic algorithm for medical image feature extraction and selection. Procedia Comput Sci 85:455–462

    Article  Google Scholar 

  31. Kim EY, Jung K (2005) Genetic algorithms for video segmentation. Pattern Recognit 38:59–73

    Article  Google Scholar 

  32. Kim EY, Park SH (2006) Automatic video segmentation using genetic algorithms. Pattern Recognit Lett 27:1252–1265

    Article  Google Scholar 

  33. Peerlinck A, Sheppard J, Pastorino J, Maxwell B (2019) Optimal Design of Experiments for precision agriculture using a genetic algorithm. In: 2019 IEEE Congress on Evolutionary Computation (CEC). pp 1838–1845

  34. Pachepsky Y, Acock B (1998) Stochastic imaging of soil parameters to assess variability and uncertainty of crop yield estimates. Geoderma 85:213–229

    Article  Google Scholar 

  35. Wang J, Huang L (2014) Evolving Gomoku solver by genetic algorithm. In: 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA). pp 1064–1067

  36. Huo P, Shiu SCK, Wang H, Niu B (2009) Application and comparison of particle swarm optimization and genetic algorithm in strategy defense game. In: 2009 Fifth International Conference on Natural Computation. pp 387–392

  37. Li H, Yuan D, Ma X et al (2017) Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Sci Rep 7:1–12

    Google Scholar 

  38. Dalal S, Vishwakarma VP (2021) Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier. Sci Rep 11:1–25

    Article  Google Scholar 

  39. Wen T, Zhang Z (2017) Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification. Medicine (Baltimore) 96:1–11. https://doi.org/10.1097/MD.0000000000006879

    Article  Google Scholar 

  40. Choubey DK, Paul S, Kumar S, Kumar S (2017) Classification of Pima indian diabetes dataset using naive bayes with genetic algorithm as an attribute selection. In: Communication and Computing Systems: Proceedings of the International Conference on Communication and Computing System (ICCCS 2016). pp 451–455

  41. Lavanya D, Rani DKU (2011) Analysis of feature selection with classification: breast cancer datasets. Indian J Comput Sci Eng 2:756–763

    Google Scholar 

  42. Aldayel MS (2012) K-Nearest Neighbor classification for glass identification problem. In: 2012 International Conference on Computer Systems and Industrial Informatics. pp 1–5

  43. Dua D, Graff C (2017) {UCI} Machine Learning Repository. Absenteeism Work dataset was donated by Andrea Martiniano, Ricardo Pinto Ferreira, Renato Jose Sassi

  44. Steddom K, Heidel G, Jones D, Rush CM (2003) Remote detection of rhizomania in sugar beets. Phytopathology 93:720–726

    Article  Google Scholar 

  45. Kaundal R, Kapoor AS, Raghava GPS (2006) Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinform 7:485

    Article  Google Scholar 

  46. Yao Q, Guan Z, Zhou Y, et al (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: 2009 international conference on engineering computation. pp 79–83

  47. Rumpf T, Mahlein A-K, Steiner U et al (2010) Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric 74:91–99

    Article  Google Scholar 

  48. Römer C, Bürling K, Hunsche M et al (2011) Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with support vector machines. Comput Electron Agric 79:180–188

    Article  Google Scholar 

  49. Bauer SD, Korč F, Förstner W (2011) The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precis Agric 12:361–377

    Article  Google Scholar 

  50. Sankaran S, Mishra A, Maja JM, Ehsani R (2011) Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Comput Electron Agric 77:127–134

    Article  Google Scholar 

  51. zhong Liu L, Zhang W, bao Shu S, ** X (2013) Image Recognition of Wheat Disease Based on RBF Support Vector Machine. In: 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)

  52. Patil SP, Zambre RS (2014) Classification of cotton leaf spot disease using support vector machine. Int J Eng Res 3:1511–1514

    Google Scholar 

  53. Pujari JD, Yakkundimath R, Byadgi AS (2015) Image processing based detection of fungal diseases in plants. Procedia Comput Sci 46:1802–1808

    Article  Google Scholar 

  54. Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: 2016 Conference on advances in signal processing (CASP). pp 175–179

  55. Sabrol H, Kumar S (2016) Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int J Comput Sci Inf Secur 14:622

    Google Scholar 

  56. Chung C-L, Huang K-J, Chen S-Y et al (2016) Detecting Bakanae disease in rice seedlings by machine vision. Comput Electron Agric 121:404–411

    Article  Google Scholar 

  57. Pujari D, Yakkundimath R, Byadgi AS (2016) SVM and ANN based classification of plant diseases using feature reduction technique. Int J Interact Multimed Artif Intell 3:6–14

    Google Scholar 

  58. Naik HS, Zhang J, Lofquist A et al (2017) A real-time phenoty** framework using machine learning for plant stress severity rating in soybean. Plant Methods 13:23

    Article  Google Scholar 

  59. Fuentes A, Yoon S, Kim S, Park D (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17:2022. https://doi.org/10.3390/s17092022

    Article  Google Scholar 

  60. Verma S, Chug A, Singh AP (2018) Prediction Models for Identification and Diagnosis of Tomato Plant Diseases. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp 1557–1563

  61. Verma S, Chug A, Singh AP (2020) Application of convolutional neural networks for evaluation of disease severity in tomato plant. J Discret Math Sci Cryptogr 23:273–282

    Article  Google Scholar 

  62. Sanyal P, Patel SC (2008) Pattern recognition method to detect two diseases in rice plants. Imaging Sci J 56:319–325

    Article  Google Scholar 

  63. Kim DG, Burks TF, Qin J, Bulanon DM (2009) Classification of grapefruit peel diseases using color texture feature analysis. Int J Agric Biol Eng 2:41–50

    Google Scholar 

  64. Li G, Ma Z, Wang H (2012) Image recognition of grape downy mildew and grape powdery mildew based on support vector machine. In: Li D, Chen Y (eds) Computer and computing technologies in agriculture. Springer, Berlin Heidelberg, pp 151–162

    Google Scholar 

  65. Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15:211–217

    Google Scholar 

  66. Ramakrishnan M et al. (2015) Groundnut leaf disease detection and classification by using back probagation algorithm. In: 2015 International Conference on Communications and Signal Processing (ICCSP). pp 964–968

  67. Aravind KR, Raja P, Mukesh K V, et al (2018) Disease classification in maize crop using bag of features and multiclass support vector machine. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC). pp 1191–1196

  68. Chen J, Yin H, Zhang D (2020) A self-adaptive classification method for plant disease detection using GMDH-Logistic model. Sustain Comput Inform Syst 28:100415

    Google Scholar 

  69. Bharti R, Khamparia A, Shabaz M et al (2021) Prediction of heart disease using a combination of machine learning and deep learning. Comput Intell Neurosci. https://doi.org/10.1155/2021/8387680

    Article  Google Scholar 

  70. Pima Indians Diabetes Database. https://www.kaggle.com/uciml/pima-indians-diabetes-database

  71. Breast Cancer Wisconsin (Original) Dataset. https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29

  72. Buuren S van, Groothuis-Oudshoorn K (2010) mice: Multivariate imputation by chained equations in R. J Stat Softw 1–68

  73. Glass Identification Dataset. https://archive.ics.uci.edu/ml/datasets/glass+identification

  74. Li D-C, Wu C-S, Tsai T-I, Lina Y-S (2007) Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Comput Oper Res 34:966–982

    Article  MATH  Google Scholar 

  75. Li D-C, Liu C-W, Hu SC (2011) A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artif Intell Med 52:45–52. https://doi.org/10.1016/j.artmed.2011.02.001

    Article  Google Scholar 

  76. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  77. Huang G-B, Zhu Q-Y, Siew C-K et al (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw 2:985–990

    Google Scholar 

  78. Cao W, Wang X, Ming Z, Gao J (2018) A review on neural networks with random weights. Neurocomputing 275:278–287

    Article  Google Scholar 

  79. Cao W, Hu L, Gao J, et al (2020) A study on the relationship between the rank of input data and the performance of random weight neural network. Neural Comput Appl 1–12

  80. Cao W, **e Z, Li J et al (2021) Bidirectional stochastic configuration network for regression problems. Neural Netw 140:237–246

    Article  Google Scholar 

  81. Dalal S, Vishwakarma VP (2020) GA based KELM optimization for ECG classification. Procedia Comput Sci 167:580–588

    Article  Google Scholar 

  82. Vishwakarma VP, Dalal S (2018) A Novel Approach for Compensation of Light Variation Effects with KELM Classification for Efficient Face Recognition. In: International Conference on VLSI, Communication and Signal Processing (VCAS 2018)

  83. Dalal S, Vishwakarma VP (2020) PHT and KELM Based Face Recognition. In: Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Springer, pp 157–167

  84. Vishwakarma VP, Dalal S (2020) Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations. EAI Endorsed Trans Scalable Inf Syst Online First. https://doi.org/10.4108/eai.13-7-2018.163575

    Article  Google Scholar 

  85. Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468

    Article  Google Scholar 

  86. Luukka P (2011) Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst Appl 38:4600–4607

    Article  Google Scholar 

  87. Choubey DK, Paul S (2015) GA_J48graft DT: a hybrid intelligent system for diabetes disease diagnosis. Int J Bio-Sci Bio-Technol 7:135–150

    Article  Google Scholar 

  88. Choubey DK, Paul S (2016) GA_MLP NN: a hybrid intelligent system for diabetes disease diagnosis. Int J Intell Syst Appl 8:49

    Google Scholar 

  89. Bani-Hani D, Patel P, Alshaikh T (2019) An optimized recursive general regression neural network oracle for the prediction and diagnosis of diabetes. Glob J Comput Sci Technol 19:1–12

    Google Scholar 

  90. Sheth PD, Patil ST, Dhore ML (2020) Evolutionary computing for clinical dataset classification using a novel feature selection algorithm. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.12.012

    Article  Google Scholar 

  91. Kumar KS (2021) Evolutionary computation technique combined with ensemble model for classification of diabetes. Afr J Diabetes Med 29:1–14

    Google Scholar 

  92. Chen H-L, Yang B, Liu J, Liu D-Y (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38:9014–9022

    Article  Google Scholar 

  93. Lavanya D, Rani KU (2012) Ensemble decision tree classifier for breast cancer data. Int J Inf Technol Converg Serv 2:17

    Google Scholar 

  94. Aličković E, Subasi A (2017) Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Comput Appl 28:753–763

    Article  Google Scholar 

  95. Prince MSM, Hasan A, Shah FM (2019) An Efficient Ensemble Method for Cancer Detection. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). pp 1–6

  96. Yu J, Li H, Liu D (2020) Modified immune evolutionary algorithm for medical data clustering and feature extraction under cloud computing environment. J Healthc Eng 2020:

  97. Pickens A, Sengupta S (2021) Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks. Mach Learn Knowl Extr 3:695–719

    Article  Google Scholar 

  98. Panthong R, Srivihok A (2015) Wrapper feature subset selection for dimension reduction based on ensemble learning algorithm. Procedia Comput Sci 72:162–169

    Article  Google Scholar 

  99. Akbulut Y, Sengur A, Guo Y, Smarandache F (2017) Ns-k-nn: Neutrosophic set-based k-nearest neighbors classifier. Symmetry (Basel) 9:179

    Article  Google Scholar 

  100. Rao H, Shi X, Rodrigue AK et al (2019) Feature selection based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput 74:634–642

    Article  Google Scholar 

  101. Syaliman K, Labellapansa A, Yulianti A (2020) Improving the Accuracy of Features Weighted k-Nearest Neighbor using Distance Weight. In: Journal of Physics: Conference Series. pp 1–6

  102. Kaur A, Kumar Y (2021) Water Wave Optimization Based Data Clustering Model. In: Journal of Physics: Conference Series. p 12054

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Acknowledgements

The work was funded by the Department of Science and Technology under a project with reference number "DST/Reference.No.T-319/2018-19". We are greatly appreciative of their help. This work would not be possible without their generous support. We are also thankful to the Department of Plant Pathology of Indian Agricultural Research Institute (IARI) for their immense support to conduct this study.

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  1. University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Sector 16-C, Dwarka, New Delhi, India

    Anshul Bhatia, Anuradha Chug & Amit Prakash Singh

  2. Division of Plant Pathology, Indian Agricultural Research Institute (IARI), New Delhi, India

    Dinesh Singh

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  1. Anshul Bhatia
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Bhatia, A., Chug, A., Singh, A.P. et al. Fractional mega trend diffusion function-based feature extraction for plant disease prediction. Int. J. Mach. Learn. & Cyber. 14, 187–212 (2023). https://doi.org/10.1007/s13042-022-01562-2

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