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

MD ASHRAFUL HAQUE; SUDEEP MARWAHA; ALKA ARORA; RANJIT KUMAR PAUL; KARAMBIR SINGH HOODA; ANU SHARMA; MONENDRA GROVER

doi:10.56093/ijas.v91i9.116089

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. DOI: https://doi.org/10.3389/fpls.2019.00941

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. DOI: https://doi.org/10.1094/PHYTO-11-16-0417-R

Kamilaris A and Prenafeta-Boldú F X. 2018. Deep learning in agriculture: A survey. Computers and electronics in agriculture 147: 70–90. DOI: https://doi.org/10.1016/j.compag.2018.02.016

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. DOI: https://doi.org/10.1038/nature14539

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. DOI: https://doi.org/10.1109/5.726791

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. DOI: https://doi.org/10.1109/ISCAS.2010.5537907

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. DOI: https://doi.org/10.5423/PPJ.OA.11.2017.0251

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. DOI: https://doi.org/10.1186/s13007-020-00582-9

Mohanty S P, Hughes D and Salathe M. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7: 1419. DOI: https://doi.org/10.3389/fpls.2016.01419

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. DOI: https://doi.org/10.1007/s00521-019-04228-3

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 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. DOI: https://doi.org/10.1109/CVPR.2015.7298594

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. DOI: https://doi.org/10.1109/CVPR.2016.308

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. DOI: https://doi.org/10.1109/ACCESS.2018.2844405