Identification of maize leaf diseases using improved deep convolutional neural networks

IEEE Access

Volumn 6, Issue , 2018, Pages 30370-30377

  Zhang, Xihai ^a   Qiao, Yue ^a   Meng, Fanfeng ^a   Fan, Chengguo ^a   Zhang, Mingming ^a  

^a NORTHEAST AGRICULTURAL UNIVERSITY   (China)

Author keywords

deep convolutional neural networks; Deep learning; identification; image processing; leaf diseases

Indexed keywords

AUTOMATION; CONVOLUTION; DEEP LEARNING; DIAGNOSIS; IDENTIFICATION (CONTROL SYSTEMS); IMAGE ENHANCEMENT; IMAGE PROCESSING; NEURAL NETWORKS;

AGRICULTURAL INFORMATIONS; AUTOMATIC IDENTIFICATION; DEEP CONVOLUTIONAL NEURAL NETWORKS; IDENTIFICATION ACCURACY; LEAF DISEASE; MODEL TRAINING; NETWORK PARAMETERS; RECOGNITION EFFICIENCY;

DEEP NEURAL NETWORKS;

EID: 85048152436     PISSN: None     EISSN: 21693536     Source Type: Journal    
DOI: 10.1109/ACCESS.2018.2844405     Document Type: Article

References (34)

1. G. F. Sprague, Corn and Corn Improvement. New York, NY, USA: Academic, 1955, pp. 89-150.
2. F. M. Latterell and A. E. Rossi, Gray leaf spot of corn: A disease on the move,'' Plant Disease, vol. 67, no. 8, pp. 842-847, 1983.
3. L. E. Williams and L. J. Alexander, Maize dwarf mosaic, a new corn disease,'' Phytopathology, vol. 55, no. 7, pp. 802-804, 1965.
4. F. C. Dai et al., Curvularia leaf spot of maize: Pathogens and varietal resistance,'' Acta Phytopathologica Sinica, vol. 28, no. 2, pp. 123-129, Feb. 1998.
5. X. X. Li et al., The corn disease remote diagnostic system in China,'' J. Food Agricult. Environ., vol. 10, no. 1, pp. 617-620, Jan. 2012.
6. J. M. J. Ward, E. L. Stromberg, D. C. Nowell, and F. W. Nutter, Jr., Gray leaf spot: A disease of global importance in maize production,'' Plant Disease, vol. 83, no. 10, pp. 884-895, 1999.
7. S. A. Miller, F. D. Beed, and C. L. Harmon, Plant disease diagnostic capabilities and networks,'' Annu. Rev. Phytopathol., vol. 47, no. 1, pp. 15-38, 2009.
8. K. Song, X. Y. Sun, and J. W. Ji, Corn leaf disease recognition based on support vector machine method,'' Trans. Chin. Soc. Agricult. Eng., vol. 23, no. 1, pp. 155-157, Jan. 2007.
9. L. Chen and L. Y. Wang, Research on application of probability neural network in maize leaf disease identification,'' J. Agricult. Mech. Res., vol. 33, no. 6, pp. 145-148, Jun. 2011.
10. L. F. Xu, X. B. Xu, and H. Min, Corn leaf disease identification based on multiple classifiers fusion,'' Trans. Chin. Soc. Agricult. Eng., vol. 31, no. 14, pp. 194-201, 2015.
11. N. Wang, K. Wang, R. Xie, J. Lai, B. Ming, and S. Li, Maize leaf disease identification based on fisher discrimination analysis,'' Scientia Agricultura Sinica, vol. 42, no. 11, pp. 3836-3842, 2009.
12. Z. Qi et al., Identification of maize leaf diseases based on image technology,'' J. Anhui Agricult. Univ., vol. 43, no. 2, pp. 325-330, Feb. 2016.
13. F. Zhang, Recognition of corn leaf disease based on quantum neural network and combination characteristic parameter,'' J. Southern Agriculture, vol. 44, no. 8, pp. 1286-1290, 2013.
14. Y. LeCun, Y. Bengio, and G. Hinton, Deep learning,'' Nature, vol. 521, pp. 436-444, May 2015.
15. Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, Deep learning for visual understanding: A review,'' Neurocomputing, vol. 187, pp. 27-48, Apr. 2016.
16. I. Lenz, H. Lee, and A. Saxena, Deep learning for detecting robotic grasps,'' Int. J. Robot. Res., vol. 34, nos. 4-5, pp. 705-724, 2013.
17. B. Alipanahi, A. Delong, M. T. Weirauch, and B. J. Frey, Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning,'' Nature Biotechnol., vol. 33, pp. 831-838, Jul. 2015.
18. L. P. Zhang, G. S. Xia, T. Wu, L. Lin, and X. C. Tai, Deep learning for remote sensing image understanding,'' J. Sensors, vol. 2016, Jun. 2015, Art. no. 7954154.
19. Y. Bengio, Learning deep architectures for AI,'' Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1-127, 2009.
20. M. Everingham, L.Van Gool, C. K. I.Williams, J.Winn, and A. Zisserman, The PASCAL visual object classes (VOC) challenge,'' Int. J. Comput. Vis., vol. 88, no. 2, pp. 303-338, Jun. 2010.
21. O. Russakovsky et al., ImageNet large scale visual recognition challenge,'' Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, Dec. 2015.
22. A. Krizhevsky, I. Sutskever, and G. Hinton, ImageNet classification with deep convolutional neural networks,'' in Proc. Adv. Neural Inf. Process. Syst., Lake Tahoe, NV, USA, 2012, pp. 1097-1105.
23. K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition,'' in Proc. Int. Conf. Learn. Represent., 2015, pp. 1-14.
24. M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks,'' in Proc. Eur. Conf. Comput. Vis., 2014, pp. 818-833.
25. Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, Identification of rice diseases using deep convolutional neural networks,'' Neurocomputing, vol. 267, pp. 378-384, Dec. 2017.
26. C. Dechant et al., Automated identification of northern leaf blightinfected maize plants from field imagery using deep learning,'' Phy-topathology, vol. 107, no. 11, pp. 1426-1432, 2017.
27. S. P. Mohanty, D. P. Hughes, and M. Salathé, Using deep learning for image-based plant disease detection,'' Frontiers Plant Sci., vol. 7, p. 1419, Sep. 2016.
28. G. Wang, Y. Sun, and J. X. Wang, Automatic image-based plant disease severity estimation using deep learning,'' in Computational Intelligence and Neuroscience, 2017, pp. 1-8.
29. D. Oppenheim and G. Shani, Potato disease classification using convolution neural networks,'' in Proc. 11th Eur. Conf. Precis. Agriculture (ECPA), 2017, pp. 244-249.
30. D. M. Hawkins, The problem of overfitting,'' J. Chem. Inf. Comput. Sci., vol. 44, no. 1, pp. 1-12, 2004.
31. Y. Jia et al., Caffe: Convolutional architecture for fast feature embedding,'' in Proc. 22nd ACM Int. Conf. Multimedia, Orlando, FL, USA, 2014, pp. 675-678.
32. S. Chetlur et al., cuDNN: Efficient primitives for deep learning,'' in Proc. Deep Learn. Represent. Learn. Workshop, 2014, pp. 1-9.
33. C. Szegedy et al., Going deeper with convolutions,'' in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston, MA, USA, Jun. 2014, pp. 1-9.
34. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting,'' J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, 2014.