Browse > Article
http://dx.doi.org/10.9717/JMIS.2017.4.4.233

Plant Disease Identification using Deep Neural Networks  

Mukherjee, Subham (Institute of Engineering and Management)
Kumar, Pradeep (IIT Roorkee)
Saini, Rajkumar (IIT Roorkee)
Roy, Partha Pratim (IIT Roorkee)
Dogra, Debi Prosad (IIT Bhubaneswar)
Kim, Byung-Gyu (Sookmyung Womens University)
Publication Information
Journal of Multimedia Information System / v.4, no.4, 2017 , pp. 233-238 More about this Journal
Abstract
Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.
Keywords
Plant Leaf Disease; CNN; GoogLeNet;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. B. Barbedo "Digital image processing techniques for detecting, quantifying and classifying plant diseases," SpringerPlus, vol. 2, no. 1, pp. 660, 2013.   DOI
2 A. Camargo and J. S. Smith, "Image pattern classification for the identification of disease causing agents in plants," Computers and Electronics in Agriculture, vols. 66, no. 2, pp. 121-125, 2009.   DOI
3 D. Casanova, J. J. de Mesquita Sa Junior and O. M. Bruno, "Plant leaf identification using Gabor wavelets". International Journal of Imaging Systems and Technology, vol. 19, no. 3, pp.236-243, 2009.   DOI
4 C. Farabet, C. Couprie, L. Najman and Y. LeCun, "Learning hierarchical features for scene labeling". IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 8, pp. 1915-1929, 2013.   DOI
5 K. Kavukcuoglu, P. Sermanet, Y. L. Boureau, K. Gregor, M. Mathieu and Y. L. Cun, "Learning convolutional feature hierarchies for visual recognition". In Advances in neural information processing systems, pp. 1090-1098, 2010.
6 F. Garcia-Ruiz, S. Sankaran, J. M. Maja, W. S. Lee, J. Rasmussen and R. Ehsani, "Comparison of two aerial imaging platforms for identification of Huangl ongbing-infected citrus trees," Computers and Electronics in Agriculture, vol. 91, pp. 106-115, 2013.   DOI
7 M. M. Ghazi, B. Yanikoglu and E. Aptoula, "Plant identification using deep neural networks via optimization of transfer learning parameters," Neurocomputing, vol. 235, pp. 228-235, 2017.   DOI
8 G. E. Hinton, S. Osindero and Y. W. The, "A fast learning algorithm for deep belief nets," Neural computation, vol. 18, no. 7, pp. 1527-1554, 2006.   DOI
9 A. Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," In Advances in neural information processing systems, pp. 1097-1105, 2012.
10 Y. LeCun, Y. Bengio Y and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, 2015.   DOI
11 Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel, "Back propagation applied to handwritten zip code recognition," Neural computation, vol. 1, no. 4, pp. 541-551, 1989.   DOI
12 S. H. Lee, C. S. Chan, P. Wilkin and P. Remagnino, "Deep-plant: Plant identification with convolutional neural networks," In Image Processing (ICIP), 2015 IEEE International Conference on, pp. 452-456, 2015.
13 S. Sankaran, A. Mishra, R. Ehsani and C Davis, "A review of advanced techniques for detecting plant diseases," Computers and Electronics in Agriculture, vol. 72, no. 1, pp. 1-3, 2010.   DOI
14 S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359, 2010.   DOI
15 A. K. Reyes, J. C. Caicedo and J. E. Camargo, "Fine-tuning Deep Convolutional Networks for Plant Recognition," In CLEF (Working Notes), 2015.
16 O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein and A. C. Berg, "Imagenet large scale visual recognition challenge," International Journal of Computer Vision, vol. 115, no. 3, pp. 211- 252, 2015.   DOI
17 Jr. D. G. Sena, F. A. Pinto, D. M. Queiroz and P. A. Viana, "Fall armyworm damaged maize plant identification using digital images," Biosystems Engineering, vol. 85. No. 4, pp. 449-454, 2003.   DOI
18 K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
19 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, "Going deeper with convolutions," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
20 L. Torrey and J. Shavlik, Transfer learning. Hand book of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques. IGI-Global, 2009.
21 L. Wang, Y. Xiong, Z. Wang and Y. Qiao, "Towards good practices for very deep two-stream convnets," arXiv preprint arXiv:1507.02159, 2015.
22 B. Zhou, A. Lapedriza, J. Xiao, A. Torralba and A. Oliva, "Learning deep features for scene recognition using places database," In Advances in neural information processing systems, pp. 487-495, 2014.
23 Y. Wang, G. W. Cottrell, "Bikers are like tobacco shops, formal dressers are like suits: Recognizing urban tribes with caffe," In Applications of Computer Vision (WACV), pp. 876-883, 2015.