Crop Diseases Severity Identification by Deep Learning Approach
Abstract
Improving yield and maintaining crop strength with optimization in use of resources are the major requirements in smart farming. To build a smart decision support system for improving production with flexibility, it requires Remote Sensing Systems. Now days with effective use of machine learning and deep learning techniques, it is possible to make the system flexible and cost effective. The deep learning based system has enormous potential, so that it can process a large number of input data and it can also control nonlinear functions. Here it should be discussed that from continuous monitoring of crop leaves images shall ensures the diseases identification.The research concludes that the quick advances in deep learning methodology will provide gainful and complete classification of crop with 98.7% to 99.9% accuracy. In this research, different cropdiseases are classified based on image processing and Convolutional neural network method. For classification of maize crop diseases, different models have been developed, compared, and finally best one is found out.Also the finest model has been tested for different crop diseases to check its consistency.
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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.
G. F. Sprague, Corn and Corn Improvement. New York, NY, USA: Academic, 1955, pp. 89–150
Xihai Zhang, Yue Qiao, Fanfeng Meng, Chengguo Fan and Mingming Zhang, Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks. IEEE Access, Volume 6, 2018 pp. 30370-30377.
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
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.
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.
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.
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.
E. L. Stewart and B. A. McDonald, Measuring quantitative virulence in the wheat pathogen zymoseptoria tritici using high-throughput automated image analysis, Phytopathology, vol. 104, no. 9, pp. 985–992, 2014.
Guan Wang, Yu Sun and Jianxin Wang Automatic image-based plant disease severity estimation using deep learning, Computational Intelligence and Neuro science, Hindawi. Volume 2017, Article ID 2917536, pp. 1-8. https://doi.org/10.1155/ 2017/2917536
J. G. A. Barbedo, An automatic method to detect and measure leaf disease symptoms using digital image processing, PlantDisease, vol. 98, no. 12, pp. 1709–1716, 2014.
Le Hoang Thai, Tran Son Hai, Nguyen Thanh Thuy, Image Classification using Support Vector Machine and Artificial Neural Network. I.J. Information Technology and Computer Science, 2012, Volume 5, pp.32-38, DOI: 10.5815 /ijitcs. 2012.05.05.
Y. Atoum, M. J. Afridi, X. Liu, J. M. McGrath, and L. E. Hanson, On developing and enhancing plant-level disease rating systems in real fields, Pattern Recognition, vol. 53, pp. 287-289,2016.
S. Choi, Plant identification with deep convolutional neural network: SNUMedinfo at LifeCLEF plant identification task 2015, in Proceedings of the 16th Conference and Labs of the Evaluation Forum, CLEF 2015, September 2015.
S. T.Hang and M.Aono, Openworld plant image identification based on convolutional neural network in proceedings of the 8 Computational Inteligence and Neuroscience
M. Mehdipour 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.
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification, Computational Intelligence and Neuroscience, vol. 2016, Article ID 3289801, 2016.
S. P. Mohanty, D. P. Hughes, and M. Salath´e, “Using deep learning for image-based plant disease detection, Frontiers in Plant Science, vol. 7, article 1419, 2016.
Plant Polyphenols, Prenatal Development and Health Outcomes, Biological Systems: Open Access, vol. 03, no. 01, 2014.
Gong Cheng, Zhenpeng Li, Xiwen Yao, Lei Guo, and Zhongliang Wei, Remote Sensing Image Scene Classification Using Bag of Convolutional Features, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. Volume 14, Issue 10
H. Al Hiary, S. Bani Ahmad,M. Reyalat,M. Braik, andZ.ALRahamneh, Fast and Accurate Detection and Classification of Plant Diseases, International Journal of Computer Applications, vol. 17, no. 1, pp. 31–38, 2011.
Sabtisudha Panigrahi, Anuja Nanda, Tripti Swarnkar, Deep Learning Approach for Image Classification, 2018 2nd International Conference on Data Science and Business Analytics. 511-516.doi:10.1109/icdsba.2018. 00101
Mingyuan Xin and Yong Wang, Research on image classification model based on deep convolution neural network. EURASIP Journal on Image and Video Processing volume 2019(40).
Sandeep Kumar , Zeeshan Khan , Anurag Jain, A Review of Content Based Image Classification using Machine Learning Approach. International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-2 Number-3 Issue-5 September-2012.
Anna Bosch, Andrew Zisserman, Xavier Munoz, Image Classification using Random Forests and Ferns. 2007 IEEE 11th International Conference on Computer Vision.
Md Tohidul Islam, B.M. Nafiz Karim Siddique, Sagidur Rahman, Taskeed Jabid, Image Recognition with Deep Learning ICIIBMS 2018, Track 2: Artificial Intelligent, Robotics, and Human-Computer Interaction, Bangkok, Thailand
Hossein Aghighi , Mohsen Azadbakht, Davoud Ashourloo , Hamid Salehi Shahrabi, Soheil Radiom, Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.Volume 11(12).
Xiao Boxiang Wang Chuanyu1, Guo Xinyu, Wu Sheng, Image acquisition system for agricultural context-aware computing,International Journal Agric &Biol Eng, Open Access at http://www. ijabe.org Vol. 7 (4).
G. Hinton, L. Deng, D. Yu et al., Deep neural networks for acoustic modelling in speech recognition: the shared views of four research groups, IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82–97, 2012
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