Image-based identification of maydis leaf blight disease of maize (Zea mays) using deep learning


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Authors

  • MD ASHRAFUL HAQUE ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • SUDEEP MARWAHA ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • ALKA ARORA ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • RANJIT KUMAR PAUL ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • KARAMBIR SINGH HOODA ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi
  • ANU SHARMA ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India
  • MONENDRA GROVER ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India

https://doi.org/10.56093/ijas.v91i9.116089

Keywords:

Convolutional neural networks (CNNs), Deep learning, GoogleNet, Image recognition, Maize, Maydis leaf blight (MLB)

Abstract

In recent years, deep learning techniques have become very popular in the field of image recognition and classification. Image-based diagnosis of diseases in crops using deep learning techniques has become trendy in the current scientific community. In this study, a deep convolutional neural network (CNN) model has been developed to identify the images of maydis leaf bight (MLB) (Cochliobolus heterostrophus) disease of maize (Zea mays L.) crop. A total of 1547 digital images of maize leaves (596 healthy and 951 infected with maydis leaf blight disease) have been collected from different agricultural farms using hand-held camera and smartphones. The images have been collected from the experimental plots of BCKV, West Bengal and ICAR-IARI, New Delhi during 2018-19. The architectural framework of popular state-of-the network 'GoogleNet' has been used to build the deep CNN model. The developed model has been successfully trained, validated and tested on the above-mentioned dataset. The trained model has achieved an overall accuracy of 99.14% on the separate test dataset.

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References

Boulent J, Foucher S, Théau J and St-Charles P L. 2019. Convolutional Neural Networks for the automatic identification of plant diseases. Frontiers in plant science 10: 941.

DeChant C, Wiesner-Hanks T, Chen S, Stewart E L, Yosinski J, Gore M A, Nelson R J and Lipson H. 2017. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107(11): 1426–32.

Kamilaris A and Prenafeta-Boldú F X. 2018. Deep learning in agriculture: A survey. Computers and electronics in agriculture 147: 70–90.

Krizhevsky A, Sutskever I and Hinton G E. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 1: 1097–1105.

LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature 521(7553): 436–44.

LeCun Y, Bottou L, Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. (In) Proceedings of the IEEE, 86th edn. Vol 11, November, pp 2278–2324.

LeCun Y, Kavukcuoglu K and Farabet C. 2010. Convolutional networks and applications in vision. (In) Proceedings of 2010 IEEE International Symposium on Circuits and Systems, May 30, pp. 253–56.

Malik V K, Singh M, Hooda K S, Yadav N K and Chauhan P K. 2018. Efficacy of newer molecules, bioagents and botanicals against maydis leaf blight and banded leaf and sheath blight of maize. Plant Pathology Journal 34(2): 121–25.

Marwaha S, Haque M A, Deb C K, Arora A, Kumar M and Hooda K S. 2019. Maize disease classification using deep CNN model. (In) Proceeding of 8th International Conference on Agricultural Statistics, New Delhi, November 18-21.

Misra T, Arora A, Marwaha S, Chinnusamy V, Rao A R, Jain R, Sahoo R N, Ray M, Kumar S, Raju D and Jha R R. 2020. SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging. Plant Methods 16(1): 1–20.

Mohanty S P, Hughes D and Salathe M. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7: 1419.

Nigam S and Jain R. 2020. Plant disease identification using Deep Learning: A review. Indian Journal of Agricultural Sciences 90(2): 249–57.

Priyadharshini R A, Arivazhagan S, Arun M and Mirnalini A. 2019. Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications 31(12): 8887–95.

Singh R and Srivastava R P. 2016. Southern corn leaf blight–an important disease of maize: an extension fact sheet. Indian Research Journal of Extension Education 12(2): 324–27.

Sladojevic S, Arsenovic M, Anderla A, Culibrk D and Stefanovic D. 2016. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience 2016: Article ID 3289801. Doi: https://doi. org/10.1155/2016/3289801

Srivastava N, Hinton G, Krizhevsky A, Sutskever I and Salakhutdinov R. 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15(1): 1929–58.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions. (In) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–9.

Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z. 2016. Rethinking the inception architecture for computer vision. (In) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–26.

Zhang X, Qiao Y, Meng F, Fan C and Zhang M. 2018. Identification of maize leaf diseases using improved deep Convolutional Neural Networks. IEEE Access 6: 30370–77.

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Submitted

2021-09-27

Published

2021-09-27

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How to Cite

HAQUE, M. A., MARWAHA, S., ARORA, A., PAUL, R. K., HOODA, K. S., SHARMA, A., & GROVER, M. (2021). Image-based identification of maydis leaf blight disease of maize (Zea mays) using deep learning. The Indian Journal of Agricultural Sciences, 91(9), 1362–1367. https://doi.org/10.56093/ijas.v91i9.116089
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