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On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves

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Abstract

Maize is one of the world's most important food crops, but its cultivation is hampered by diseases. Rapid disease identification remains a challenge due to a lack of the necessary infrastructure. This necessitates the development of automated methods to identify diseases. In this research, the use of deep learning models to identify maize leaf diseases is proposed. In this article, we investigate the transfer learning of deep convolutional neural networks for the detection of maize leaf diseases and explore employing the knowledge of pre-trained models and then transferring the knowledge to our dataset. In this attempt, we employ pre-trained VGG16, ResNet50, InceptionV3, and Xception models to classify three common maize leaf diseases using a dataset of 18,888 images of healthy and diseased leaves. Besides, Bayesian optimization is used to choose optimal values for hyperparameters, and image augmentation is used to improve the model's ability to generalize. The work includes a comparative study and analysis of the proposed models. The results demonstrate that all trained models have an accuracy of more than 93% in classifying maize leaf diseases. In particular, VGG16, InceptionV3, and Xception achieved an accuracy of more than 99%. Furthermore, our methodology provides new avenues for the detection of maize leaf diseases.

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References

  1. Mueller DS et al (2016) Corn yield loss estimates due to diseases in the United States and Ontario, Canada from 2012 to 2015. Plant Health Progr 17(3):211–222

    Google Scholar 

  2. Shiferaw B et al (2011) Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur 3(3):307–327

    Google Scholar 

  3. Barbedo JGA (2016) A review on the main challenges in automatic plant disease identification based on visible range images. Biosys Eng 144:52–60

    Google Scholar 

  4. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419

    Google Scholar 

  5. Jiang F et al (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neurol 2(4):230–243

    Google Scholar 

  6. Atila Ü et al (2021) Plant leaf disease classification using EfficientNet deep learning model. Ecol Inf 61:101182

    Google Scholar 

  7. Song K, Sun X, Ji J (2007) Corn leaf disease recognition based on support vector machine method. Trans CSAE 23(1):155–157

    Google Scholar 

  8. Li, C. and L. Wang, (2011) Research on application of probability neural network in maize leaf disease identification. J Agric Mechan Res, 6

  9. Xu L et al (2015) Corn leaf disease identification based on multiple classifiers fusion. Trans Chin Soc Agric Eng 31(14):194–201

    Google Scholar 

  10. Wang N et al (2009) Maize leaf disease identification based on fisher discrimination analysis. Scientia Agricultura Sinica 42(11):3836–3842

    Google Scholar 

  11. Qi Z et al (2016) Identification of maize leaf diseases based on image technology. J Anhui Agric Univ 43(2):325–330

    Google Scholar 

  12. Zhang F (2013) Recognition of corn leaf disease based on quantum neural network and combination characteristic parameter. J Southern Agric 44(8):1286–1290

    Google Scholar 

  13. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Google Scholar 

  14. Kamilaris A, Prenafeta-Boldú FX (2018) A review of the use of convolutional neural networks in agriculture. J Agric Sci 156(3):312–322

    Google Scholar 

  15. Ciresan, D.C., et al. (2011) Flexible, high performance convolutional neural networks for image classification. In: twenty-second international joint conference on artificial intelligence

  16. Hinton G et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Google Scholar 

  17. Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90

    Google Scholar 

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

    Google Scholar 

  19. Hu G et al (2019) Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustain Comput Inf Syst 24:1003

    Google Scholar 

  20. Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants 8(11):468

    Google Scholar 

  21. Lee, S.H., et al. (2015) Deep-plant: plant identification with convolutional neural networks. In: 2015 IEEE international conference on image processing (ICIP), IEEE

  22. S. V. Kogilavani, S.M., (2019) Deep learning based model for plant disease detection. Int J Innovat Technol Explor Eng, 8(12)

  23. 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 

  24. Joshi RC et al (2021) VirLeafNet: automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant. Ecol Inf 61:101197

    Google Scholar 

  25. Best N, Ott J, Linstead EJ (2020) Exploring the efficacy of transfer learning in mining image-based software artifacts. J Big Data 7(1):1–10

    Google Scholar 

  26. Tian, L., et al. (2015) Stacked PCA network (SPCANet): an effective deep learning for face recognition. In: 2015 IEEE international conference on digital signal processing (DSP), IEEE

  27. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  28. Chen J et al (2021) Attention embedded lightweight network for maize disease recognition. Plant Pathol 70(3):630–642

    Google Scholar 

  29. Priyadharshini RA et al (2019) Maize leaf disease classification using deep convolutional neural networks. Neural Comput Appl 31(12):8887–8895

    Google Scholar 

  30. Sun, X. and J. Wei. (2020) Identification of maize disease based on transfer learning. In: journal of physics: conference series, IOP Publishing

  31. Waheed A et al (2020) An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Comput Electr Agric 175:105456

    Google Scholar 

  32. Mishra S, Sachan R, Rajpal D (2020) Deep convolutional neural network based detection system for real-time corn plant disease recognition. Procedia Comput Sci 167:2003–2010

    Google Scholar 

  33. da Rocha, E.L., L. Rodrigues, and J.F. Mari. (2020) Maize leaf disease classification using convolutional neural networks and hyperparameter optimization. In: Anais do XVI Workshop de Visão Computacional, SBC

  34. Sibiya M, Sumbwanyambe M (2019) A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering 1(1):119–131

    Google Scholar 

  35. Lin Z et al (2018) A novel method of maize leaf disease image identification based on a multichannel convolutional neural network. Trans ASABE 61(5):1461–1474

    Google Scholar 

  36. Arora J, Agrawal U (2020) Classification of maize leaf diseases from healthy leaves using deep forest. J Artif Intell Syst 2(1):14–26

    Google Scholar 

  37. Sibiya M, Sumbwanyambe M (2021) Automatic fuzzy logic-based maize common rust disease severity predictions with thresholding and deep learning. Pathogens 10(2):131

    Google Scholar 

  38. Sun J et al (2020) Northern maize leaf blight detection under complex field environment based on deep learning. IEEE Access 8:33679–33688

    Google Scholar 

  39. Zhang X et al (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370–30377

    Google Scholar 

  40. Bhatt, P., et al. (2019) Identification of diseases in corn leaves using convolutional neural networks and boosting. In: ICPRAM

  41. Chen G et al (2019) Corn plant disease recognition based on migration learning and convolutional neural network. Smart Agric 1(2):34

    Google Scholar 

  42. Agarwal, M., et al. (2019) A convolution neural network based approach to detect the disease in corn crop. In: 2019 IEEE 9th international conference on advanced computing (IACC), IEEE

  43. Ruedeeniraman, N., M. Ikeda, and L. Barolli. (2020) An intelligent VegeCare tool for corn disease classification. In: international conference on P2P, parallel, grid, cloud and internet computing, Springer

  44. Hu, R., et al. (2020) The identification of corn leaf diseases based on transfer learning and data augmentation. In: proceedings of the 2020 3rd international conference on computer science and software engineering

  45. Panigrahi, K.P., A.K. Sahoo, and H. Das. (2020) A CNN approach for corn leaves disease detection to support digital agricultural system. In: 2020 4th international conference on trends in electronics and informatics (ICOEI) (48184), IEEE

  46. Blake Richey, M.V.S., (2021) Deep learning based real-time detection of Northern Corn Leaf Blight crop disease using YoloV4. In: proceeding SPIE 11736, real-time image processing and deep learning

  47. Hidayat A, Irmawati UD (2019) Detection of disease on corn plants using convolutional neural network methods. J Comput Sci Inf. 12(1):51–56

    Google Scholar 

  48. Wang Z, Zhang S (2018) Segmentation of corn leaf disease based on fully convolution neural network. Acad J Comput Inf Sci. https://doi.org/10.25236/AJCIS.010002

    Article  Google Scholar 

  49. Yang M, Zhang Y, Liu T (2020) Corn disease recognition based on the Convolutional Neural Network with a small sampling size. Chin J Eco-Agric 28(12):1924–1931

    Google Scholar 

  50. Goodfellow I et al (2016) Deep learning, vol 1. MIT Press, Cambridge

    MATH  Google Scholar 

  51. Ponti, M.A., et al. (2017) Everything you wanted to know about deep learning for computer vision but were afraid to ask. In: 2017 30th SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T), IEEE

  52. LeCun, Y., F.J. Huang, and L. Bottou. (2004) Learning methods for generic object recognition with invariance to pose and lighting. In: proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, IEEE

  53. LeCun Y et al (1995) Learning algorithms for classification: a comparison on handwritten digit recognition. Neural Netw Statistical Mech Perspect 261(276):2

    Google Scholar 

  54. Patterson J, Gibson A (2017) Deep learning: a practitioner’s approach. O’Reilly Media, Inc., United States

    Google Scholar 

  55. Simonyan, K. and A. Zisserman, (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  56. Chollet F (2018) Deep learning with Python, vol 361. Manning, New York

    Google Scholar 

  57. He, K., et al. (2016) Deep residual learning for image recognition. In: proceedings of the IEEE conference on computer vision and pattern recognition

  58. Szegedy, C., et al. (2015) Going deeper with convolutions. In: proceedings of the IEEE conference on computer vision and pattern recognition

  59. Ioffe, S. and C. Szegedy (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: international conference on machine learning, PMLR

  60. Szegedy, C., et al. (2016) Rethinking the inception architecture for computer vision. In: proceedings of the IEEE conference on computer vision and pattern recognition

  61. Chollet, F. (2017) Xception: deep learning with depthwise separable convolutions. In: proceedings of the IEEE conference on computer vision and pattern recognition

  62. Chen J et al (2020) Using deep transfer learning for image-based plant disease identification. Comput Electr Agric 173:105393

    Google Scholar 

  63. Canziani, A., A. Paszke, and E. Culurciello, (2016) An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678

  64. Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. Catena 186:104249

    Google Scholar 

  65. Pelikan, M., D.E. Goldberg, and E. Cantú-Paz. BOA: The Bayesian optimization algorithm. In: proceedings of the genetic and evolutionary computation conference GECCO-99. 1999. Citeseer

  66. Google Colaboratory: Frequently Asked Questions. . 2018; Available from: https://research.google.com/colaboratory/faq.html

  67. Xu Y et al (2021) Maize diseases identification method based on multi-scale convolutional global pooling neural network. IEEE Access 9:27959–27970

    Google Scholar 

  68. Lv M et al (2020) maize leaf disease identification based on feature enhancement and DMS-robust alexnet. IEEE Access 8:57952–57966

    Google Scholar 

Download references

Acknowledgements

We acknowledge and thank the Department of Science and Technology (Government of India) for sanctioning the research grant (Ref. No.SR/FST/COLLEGE-096/2017 dated 16.01.2018) under the Fund for Improvement of S&T Infrastructure (FIST) program for completing this work.

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Authors and Affiliations

  1. Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India

    Malliga Subramanian, Kogilavani Shanmugavadivel & P. S. Nandhini

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  1. Malliga Subramanian
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  2. Kogilavani Shanmugavadivel
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  3. P. S. Nandhini
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Contributions

MS: Performed the analysis, wrote the paper. KS: Collected the data and analysis of data, assisted in writing the paper. PSN: Designed the models.

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Correspondence to Malliga Subramanian.

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Subramanian, M., Shanmugavadivel, K. & Nandhini, P.S. On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Comput & Applic 34, 13951–13968 (2022). https://doi.org/10.1007/s00521-022-07246-w

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