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Performance enhancement of kernelized SVM with deep learning features for tea leaf disease prediction

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Abstract

India is one of the world's leading tea producers, yet more than 70% of the country's tea is consumed domestically. Tea leaf diseases have a significant impact on the quality and yield of tea. So, it is very important to find a more accurate method to identify tea leaf diseases correctly. Due to very limited number of tea leaf images, classification is very difficult. Very frequently overfitting of model occurs. To cope up with this, we applied images augmentation process, that increased dataset nearly fourteen times. But still this number of datasets is not adequate for DL based classification. So, we used here deep learning for feature extraction and machine learning based classifier for classification. In this work, we have proposed a hybrid technique that combines deep learning-based features of augmented dataset with machine learning based classifier for getting better classification result. In proposed work, VGG-16 is used for colour feature extraction from the tea leaf dataset. Based on this feature, model is built and several machine learning-based classifiers like KNN, XGB, Random Forest, and kernelized SVM are employed for classification task. Our proposed model achieved highest classification accuracy with Sigmoid and Linear kernel based SVM and VGG-16 features. The accuracy of proposed model is 96.67%. We compared our proposed work with existing work on tea leaf dataset and found that our model is performing comparatively better.

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References

  1. Han B, Chen Z (2002) Behavioral and electrophysiological responses of natural enemies to synonymous from tea shoots and kairomones from tea aphids Toxoptera aurantii. J Chem Ecol 28:2203–19

    Article  Google Scholar 

  2. http://www.tocklai.net/list-of-manufacturers-registered-for-production-and-sale-of-bio-pesticides/

  3. https://en.wikipedia.org/wiki/History_of_tea_in_India

  4. Rakshit, Ishita Ayan Dutt & Avishek (18 April 2019). "Goodricke parent Camellia becomes the world's largest private tea producer". Business Standard India – via Business Standard.

  5. Pani, Priyanka. "Tata Global Beverages taps into growing green tea culture". @businessline.

  6. Hu G, Wu H, Zhang Y, Wan M (2019) A low shot learning method for tea leaf’s disease identification. Compute Elect Agric 163:104852

    Article  Google Scholar 

  7. Bhagat M, Kumar D, Kumar D (2019) Role of Internet of Things (IoT) in Smart Farming: A Brief Survey. Devices Integr Circuit 2019:141–145. https://doi.org/10.1109/DEVIC.2019.8783800

    Article  Google Scholar 

  8. Saleem MH, Potgieter J, Arif KM (2020) Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers. Plants 9(10):1319

    Article  Google Scholar 

  9. Römer C, Bürling K, Hunsche M, Rumpf T, Noga G, Plümer L (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 

  10. 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

    Article  Google Scholar 

  11. Kobayashi T, Kanda E, Kitada K, Ishiguro K, Torigoe Y (2001) Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology 91:316–323

    Article  Google Scholar 

  12. Zhang K, Wu Q, Liu A, Meng X (2018) Can Deep Learning Identify Tomato Leaf Disease? Adv Multimed 2018:1–10

    Google Scholar 

  13. Oppenheim D, Shani G, Erlich O, Tsror L (2019) Using Deep Learning for Image-Based Potato Tuber Disease Detection. Phytopathology 109:1083–1087

    Article  Google Scholar 

  14. Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric 161:272–279

    Article  Google Scholar 

  15. Hossain MS, Mou RM, Hasan MM et al (2018) Recognition and detection of tea leaf’s diseases using support vector machine. 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA). Batu Feringghi, Malaysia, pp 150–154

    Chapter  Google Scholar 

  16. Chaudhary A, Kolhe S, Kamal R (2016) An improved random forest classifier for multi-class classification. Inform Process Agric 3(4):215–222

    Google Scholar 

  17. Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384

    Article  Google Scholar 

  18. Ehsan K, Tofik M (2017) Identification of plant disease infection using soft-computing: application to modern botany. Proc Comput Sci 120:893–900

    Article  Google Scholar 

  19. Tetila EC, Machado BB, Belete NAS et al (2017) Identification of soybean foliar diseases using unmanned aerial vehicle images. IEEE Geosci Remote Sensing Lett 14(12):2190–2194

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Proc Comput Sci 133:1040–1047

    Article  Google Scholar 

  22. Zhang X, Qiao Y, Meng F, Fan C, Zhang M (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370–30377

    Article  Google Scholar 

  23. Wang T, Zhang K, Zhang W, Wang R, Wan S, Rao Y, Jiang Z, Gu L (2021) Tea picking point detection and location based on Mask-RCNN. Inform Process Agric 10(2):267–275

    Google Scholar 

  24. Hu G et al (2021) Semantic segmentation of tea geometrid in natural scene images using discriminative pyramid network. Appl Soft Comput 113:107984

    Article  Google Scholar 

  25. Hu G, Fang M (2022) Using a multi-convolutional neural network to automatically identify small-sample tea leaf diseases. Sustain Comput: Inform Syst 35:100696

    Google Scholar 

  26. Zhao X et al (2022) Detection and discrimination of disease and insect stress of tea plants using hyperspectral imaging combined with wavelet analysis. Comput Electron Agric 193:106717

    Article  Google Scholar 

  27. Bhagat, M., Kumar, D (2022) A comprehensive survey on leaf disease identification & classification. Multimed Tools Applhttps://doi.org/10.1007/s11042-022-12984-z

  28. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, Sep 2014.

  29. Liu F et al (2019) Intelligent and secure content-based image retrieval for mobile users. IEEE Access 7:119209–119222

    Article  Google Scholar 

  30. Nwankpa, Chigozie, et al. "Activation functions: Comparison of trends in practice and research for deep learning." arXiv preprint arXiv:1811.03378 (2018).

  31. I. Goodfellow, Y. Bengio, and A. Courville, “Deep learning.” MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org

  32. V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” Haifa, 2010, pp. 807–814. [Online]. Available: https://dl.acm.org/citation.cfm

  33. Li S, Zhang X (2020) Research on orthopedic auxiliary classification and prediction model based on XGBoost algorithm. Neural Comput Appl 32:1971–1979

    Article  Google Scholar 

  34. Bhagat M, Kumar D, Haque I, Munda HS, Bhagat R (2020) "Plant Leaf Disease Classification Using Grid Search Based SVM," 2nd International Conference on Data, Engineering and Applications (IDEA), 1–6, https://doi.org/10.1109/IDEA49133.2020.9170725.

  35. Zhang X, Wu Z, Cao C, Luo K, Qin K, Huang Y, Cao J (2023) Design and operation of a deep-learning-based fresh tea-leaf sorting robot. Comput Electron Agric 206:107664. https://doi.org/10.1016/j.compag.2023.107664

    Article  Google Scholar 

  36. Bhagat M, Kumar D, Kumar S (2023) Bell pepper leaf disease classification with LBP and VGG-16 based fused features and RF classifier. Int j inf tecnol 15:465–475. https://doi.org/10.1007/s41870-022-01136-z

    Article  Google Scholar 

  37. Bhagat M, Kumar D (2023) Performance evaluation of PCA based reduced features of leaf images extracted by DWT using random Forest and XGBoost classifier. Multimed Tools Appl 82:26225–26254. https://doi.org/10.1007/s11042-023-14370-9

    Article  Google Scholar 

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This study received no specific financing from governmental, private, or non-profit funding bodies.

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  1. Computer Science & Engineering, National Institute of Technology Jamshedpur, Jharkhand, India

    Monu Bhagat & Dilip Kumar

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  1. Monu Bhagat
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Correspondence to Monu Bhagat.

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Bhagat, M., Kumar, D. Performance enhancement of kernelized SVM with deep learning features for tea leaf disease prediction. Multimed Tools Appl 83, 39117–39134 (2024). https://doi.org/10.1007/s11042-023-17172-1

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  • DOI: https://doi.org/10.1007/s11042-023-17172-1

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