Intelligent diagnosis of northern corn leaf blight with deep learning model

doi:10.1016/S2095-3119(21)63707-3

Journal of Integrative Agriculture ›› 2022, Vol. 21 ›› Issue (4): 1094-1105.DOI: 10.1016/S2095-3119(21)63707-3

所属专题：智慧植保合辑Smart Plant Protection

收稿日期:2021-01-18 接受日期:2021-03-22 出版日期:2022-04-01 发布日期:2021-03-22

Intelligent diagnosis of northern corn leaf blight with deep learning model

PAN Shuai-qun¹, QIAO Jing-fen¹, WANG Rui², YU Hui-lin², WANG Cheng², Kerry TAYLOR¹, PAN Hong-yu²

¹ School of Computing, Australian National University, Canberra 2601, Australia
² College of Plant Sciences, Jilin University, Changchun 130062, P.R.China

Received:2021-01-18 Accepted:2021-03-22 Online:2022-04-01 Published:2021-03-22
About author:PAN Shuai-qun, E-mail: shuaiqun.pan@anu.edu.au; Correspondence PAN Hong-yu, Tel/Fax: +86-431-87835659, E-mail: panhongyu@jlu.edu.cn
Supported by:
This work was financially supported by the Key Planning Projects on Science and Technology of Jilin Province, China (20180201012NY), the Inter-Governmental International Cooperation Special Project of National Key R&D Program of China (2019YFE0114200), and the National Key R&D Program of China (2017YFD0201802).

摘要/Abstract

Abstract: Maize (Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight (NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica (Luttrell) Leonard and Suggs. Early intelligent diagnosis and warning is an effective and economical strategy to control this disease. Today, deep learning is beginning to play an essential role in agriculture. Notably, deep convolutional neural networks (DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis. Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models. We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images. Several proven convolutional neural networks, such as AlexNet, GoogleNet, VGG16, and VGG19, were then used to identify diseases. Based on the best performance of the DCNN pre-trained model GoogleNet, some of the recent loss functions developed for deep facial recognition tasks such as ArcFace, CosFace, and A-Softmax were applied to detect NCLB. We found that a pre-trained GoogleNet architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis. The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras. The techniques, training, validation, and test results are presented in this paper. Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.

Key words: maize , northern corn leaf blight , Setosphaeria turcica , intelligent diagnosis , deep learning , convolutional neural network

. [J]. Journal of Integrative Agriculture, 2022, 21(4): 1094-1105.

PAN Shuai-qun, QIAO Jing-fen, WANG Rui, YU Hui-lin, WANG Cheng, Kerry TAYLOR, PAN Hong-yu. Intelligent diagnosis of northern corn leaf blight with deep learning model[J]. Journal of Integrative Agriculture, 2022, 21(4): 1094-1105.

参考文献

Albawi S, Mohammed T A, Al-Zawi S. 2017. Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET). 21–23 August, 2017. Antaya, Turkey. pp. 1–6.
Bengio Y, Courville A, Vincent P. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1798–1828.
Bisong E. 2019. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley.
Chen J, Chen J, Zhang D, Sun Y, Nanehkaran Y A. 2020. Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393.
DeChant C, Wiesner-Hanks T, Chen S, Stewart E L, Yosinski J, Gore M A, Nelson R J, Lipson H. 2017. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology, 107, 1426–1432.
Deng J, Dong W, Socher R, Li L J, Li K, Li F F. 2009. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. 20–25 June, 2009. Miami, FL, USA. pp. 248–255.
Deng J, Guo J, Xue N, Zafeiriou S. 2019. Arcface: Additive Angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 16–20 June, 2019. Long Beach, CA, USA. pp. 4690–4699.
Ferentinos K P. 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
Gulli A, Pal S. 2017. Deep Learning with Keras. Packt Publishing.
Hinton G E, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R R. 2012. Improving neural networks by preventing co-adaptation of feature detectors. Computer Science, arXiv preprint arXiv: 1207.0580.
Huang G B, Mattar M, Berg T, Learned-Miller E. 2008. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In: Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition. October, 2008. Marseille, France.
Hubert B, Rosegrant M, van Boekel M A J S, Ortiz R. 2010. The future of food: Scenarios for 2050. Crop Science, 50, S33–S50.
Kamilaris A, Prenafeta-Boldú F X. 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90.
Kemelmacher-Shlizerman I, Seitz S M, Miller D, Brossard E. 2016. The megaface benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 27–30 June, 2016. Las Vegas, NV, USA. pp. 4873–4882.
Ketkar N. 2017. Introduction to Pytorch. In: Deep Learning with Python. Apress, Berkeley, CA. pp. 195–208.
Krizhevsky A, Sutskever I, Hinton G E. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
LeCun Y, Bottou L, Bengio Y, Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.
LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature, 521, 436–444.
LeCun Y, Kavukcuoglu K, Farabet C. 2010. Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems. 30 May June, 2010. Paris, France. pp. 253–256.
Liu W, Wen Y, Yu Z, Li M, Raj B, Song L. 2017. Sphereface: Deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 21–26 July, 2017. Honolulu, HI, USA. pp. 212–220.
Liu W, Wen Y, Yu Z, Yang M. 2016. Large-margin softmax loss for convolutional neural networks. In: Proceedings of the International Conference on Machine Learning (ICML). p. 7.
Lu Y, Yi S, Zeng N, Liu Y, Zhang Y. 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, 378–384.
Lucas J A. 2011. Advances in plant disease and pest management. The Journal of Agricultural Science, 149, 91–114.
Mansfield B D, Mumm R H. 2014. Survey of plant density tolerance in US maize germplasm. Crop Science, 54, 157–173.
Mohanty S P, Hughes D P, Salathé M. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.
Nech A, Kemelmacher-Shlizerman I. 2017. Level playing field for million scale face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 21–26 July, 2017. Honolulu, HI, USA. pp. 7044–7053.
Odegard I Y R, Van der Voet E. 2014. The future of food - Scenarios and the effect on natural resource use in agriculture in 2050. Ecological Economics, 97, 51–59.
O’Shea K, Nash R. 2015. An introduction to convolutional neural networks. Computer Science, arXiv preprint arXiv: 1511.08458.
Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A. 2017. Automatic differentiation in PyTorch. In: NIPS 2017 Workshop AutoDiff. 29 October, 2017. Long Beach, Calfornia.
Priyadharshini R A, Arivazhagan S, Arun M, Mirnalini A. 2019. Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31, 8887–8895.
Rahnemoonfar M, Sheppard C. 2017. Deep count: Fruit counting based on deep simulated learning. Sensors, 17, 905.
Ren S, He K, Girshick R, Sun J. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28, 91–99.
Schroff F, Kalenichenko D, Philbin J. 2015. Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8–10 June, 2015. Boston, MA, USA. pp. 815–823.
Shi F, Zhang Y, Wang K, Meng Q, Liu X, Ma L, Li Y, Liu J, Ma L. 2018. Expression profile analysis of maize in response to Setosphaeria turcica. Gene, 659, 100–108.
Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. Proceedings International Conference on Learning Representations, arXiv preprint arXiv: 1409.1556.
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. 2016. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 11.
Steen K A, Christiansen P, Karstoft H, Jørgensen R N. 2016. Using deep learning to challenge safety standard for highly autonomous machines in agriculture. Journal of Imaging, 2, 6.
Sun G, Liu J, Li G, Zhang X, Chen T, Chen J, Zhang H, Wang D, Sun F, Pan H. 2015. Quick and accurate detection and quantification of Magnaporthe oryzae in rice using real-time quantitative polymerase chain reaction. Plant Disease, 99, 219–224.
Sun J, Yang Y, He X, Wu X. 2020. Northern maize leaf blight detection under complex field environment based on deep learning. IEEE Access, 8, 33679–33688.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8–10 June, 2015. Boston, MA, USA. pp. 1–9.
Tavakoli H, Alirezazadeh P, Hedayatipour A, Nasib A B, Landwehr N. 2021. Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. Computers and Electronics in Agriculture, 181, 105935.
Türkoğlu M, Hanbay D. 2019. Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering & Computer Sciences, 27, 1636–1651.
Waheed A, Goyal M, Gupta D, Khanna A, Hassanien A E, Pandey H M. 2020. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175, 105456.
Wang F, Cheng J, Liu W, Liu H. 2018. Additive margin softmax for face verification. IEEE Signal Processing Letters, 25, 926–930.
Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W. 2018. Cosface: Large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 18–23 June, 2018. Salt Lake City, UT, USA. pp. 5265–5274.
Wolf L, Hassner T, Maoz I. 2011. Face recognition in unconstrained videos with matched background similarity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011. 21–23 June, 2011. Colorado Springs, CO, USA. pp. 529–534.
Yang N, Xu X W, Wang R R, Peng W L, Cai L, Song J M, Li W, Luo X, Niu L, Wang Y, Jin M. 2017. Contributions of Zea mays subspecies mexicana haplotypes to modern maize. Nature Communications, 8, 1–10.
Zeiler M D, Fergus R. Visualizing and understanding convolutional networks. In: European Conference on Computer Vision. 6–12 September, 2014. Zurich, Switzerand. pp. 818–833.
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.
Zhong Y, Zhao M. 2020. Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture, 168, 105146.

Intelligent diagnosis of northern corn leaf blight with deep learning model

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