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Performance improvement of Deep Learning Models using image augmentation techniques

  • 1197: Advances in Soft Computing Techniques for Visual Information-based Systems
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Abstract

The major barrier while using deep learning models is lack of large number of images in the training dataset. In fact, there is a need of thousands of images in each image categories based on the complexity of problem. Prior studies have shown that picture augmentation techniques can be used to enhance the number of images in a training dataset artificially. These techniques can aid in improving the overall learning process and performance of a deep learning model. Hence, to address this problem we have proposed three algorithms. Firstly, two image acquisition algorithms have been proposed to systematically obtain real field images for testing and images from public datasets for training a model. Secondly, an algorithm is proposed to describe the procedure how the augmentations can be applied to enhance the datasets. During this study, we have investigated 52 augmentations that can allow enhancing the size of input dataset by improving the quantity of images. To perform the classification process of four maize crop diseases, a new convolutional neural network model is developed and several experiments have been performed to prove its effectiveness. Firstly, two tests were carried out using the original dataset from Kaggle public repository and the augmented dataset. When compared with the original dataset, the model improved by 5.14% with the augmented dataset. Secondly, three experiments carried out to evaluate the performance of proposed augmentation method. Experimental results demonstrated that the proposed approach outperforms the existing three approaches by 27.38%, 3.14%, and 1.34% during the classification process. The proposed IPA augmentation method has been compared with six existing methods: Full Stage Data Augmentation Framework, LeafGAN, Novel Augmentation method based on GAN, Wasserstein Generative Adversarial Network (WGAN), Activation Reconstruction-GAN, and Step-by-Step Data Augmentation Method and experimental results show that performance is better than existing methods by 28.31%, 19.76%, 20.18%, 13.75%, 2.42%, and 12.68% respectively.

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References

  1. Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D  (2019) Solving current limitations of deep learning based approaches for plant disease detection. Symmetry 11:939. https://doi.org/10.3390/sym11070939

  2. Available online at https://www.kaggle.com/smaranjitghose/corn-or-maize-leaf-disease-dataset. Accessed 29 Sept 2021

  3. Avuçlu E (2021) A new data augmentation method to use in machine learning algorithms using statistical measurements. Measurement 180:14

    Article  Google Scholar 

  4. Bansilal S (2017) The application of the percentage change calculation in the context of inflation in Mathematical. Literacy Pythagoras 38(1):a314. https://doi.org/10.4102/pythagoras.v38i1.314

    Article  Google Scholar 

  5. Cap QH, Uga H, Kagiwada S, Iyatomi H (2020) LeafGAN: an effective data augmentation method for practical plant disease diagnosis, computer vision and pattern recognition, pp 1-10

  6. Chen K, Franko K, Sang R (2021) Structured model pruning of convolutional networks on tensor processing units, 1-6. arXiv:2107.04191v2 [cs.LG]

  7. Dyrmann M, Karstoft H, Midtiby HS (2016) Plant species classification using deep convolutional neural network. Biosyst Eng 151:72–80

    Article  Google Scholar 

  8. Enkvetchakul P, Surinta O (2021) Effective data augmentation and training techniques for improving deep learning in plant leaf disease recognition. Appl Sci Eng Progress. https://doi.org/10.14416/j.asep.2021.01.003

    Article  Google Scholar 

  9. Fengle Zhua M, Hea Z, Zheng (2020) Data augmentation using improved cDCGAN for plant vigor rating. Comput Electron Agric 175:105612

    Google Scholar 

  10. Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), pp 2414-2423

  11. Joffrey LL, Taghi MK, Richard AB, Naeem S (2018) A survey on addressing high-class imbalance in big data. Springer J Big Data, pp 5–42

  12. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):1–9

    Article  Google Scholar 

  13. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

  14. Luning Bi, Guiping, Hu (2020) Improving image-based plant disease classification with generative adversarial network under limited training set. Front Plant Sci 11:12

    Article  Google Scholar 

  15. Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 154:18–24

    Article  Google Scholar 

  16. Perez L, Wang J (2007) The effectiveness of data augmentation in image classification using deep learning. Computer Vision and Pattern Recognition, pp 1- 8

  17. Pires RDL, Gonc¸alves DN, Orueˆ JPM, Kanashiro WES, Rodrigues JF, Machado BB, Gonc¸alves WN (2016) Local descriptors for soybean disease recognition. Comput Electron Agric 125:48–55

    Article  Google Scholar 

  18. Prasad S, Peddoju SK, Ghosh D (2016) Multi-resolution mobile vision system for plant leaf disease diagnosis. Signal Image Video Process 10(2):379–388

    Article  Google Scholar 

  19. Qian Y, Yang B, Wang W, Wang B, Chen P, Zhang J (2020) Apple leaf diseases recognition based on an improved convolutional neural network. Sensors 20:3535. https://doi.org/10.3390/s20123535

    Article  Google Scholar 

  20. Ramcharan A, Baranowski K, McCloskey P, Ahamed B, Legg J, Hughes D (2017) Using transfer learning for image-based cassava disease detection. Front Plant Sci 8:1852. https://doi.org/10.3389/fpls.2017.01852

    Article  Google Scholar 

  21. Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6:60

    Article  Google Scholar 

  22. Srdjan Sladojevic M, Arsenovic A, Anderla D, Culibrk, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, pp 1-12

  23. Sukhvir Kaur S, Pandey S, Goel (2018) Plants disease identification and classification through leaf images: a survey. Computational Methods in Engineering, pp 1-24

  24. Xu Y, Jia R, Mou L, Li G, Chen Y, Lu Y, Jin Z (2016) Improved relation classification by deep recurrent neural networks with data augmentation. arXiv:1601.03651v2

  25. Yang QZhengMingqiang, Tian X, Jiang N, Wang D (2020) A full stage data augmentation method in deep convolutional neural network for natural image classification. Discrete Dyn Nat Soc 1:12

    MATH  Google Scholar 

  26. Yunong Tian G, Yang Z, Wang E, Li, Liang Z (2019) Detection of apple lesions in orchards based on deep learning methods of CycleGAN and YOLOV3-dense. J Sens 1:13. https://doi.org/10.1155/2019/7630926

    Article  Google Scholar 

  27. Zhang SW, Shang YJ, Wang L (2015) Plant disease recognition based on plant leaf image. J Anim Plant Sci 25(Suppl 1):42–45

    Google Scholar 

  28. Zhang X, Qiao Y, Meng F, Zhang M (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370–30378

    Article  Google Scholar 

  29. Zhang J, Rao Y, Man C, Jiang Z, Li S (2021) Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things. Int J Distrib Sens Netw  17(4):1:13

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Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

    M. Nagaraju & Priyanka Chawla

  2. Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala, Punjab, India

    Neeraj Kumar

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  1. M. Nagaraju
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  2. Priyanka Chawla
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  3. Neeraj Kumar
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Correspondence to Priyanka Chawla.

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Nagaraju, M., Chawla, P. & Kumar, N. Performance improvement of Deep Learning Models using image augmentation techniques. Multimed Tools Appl 81, 9177–9200 (2022). https://doi.org/10.1007/s11042-021-11869-x

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