Crop Disease Detection and Classification Based on Hybrid Information Approach
Keywords:
plant diseases, GENABC clustering, ITELP, EGF, ISRC classifierAbstract
The objective of this paper to identify the diseases in the leaves of the all plants. Plant disease diagnosis helps to improve both the quality and quantity of crop productivity. In existing, to detect the diseases they used the spectroscopic techniques. These techniques are very expensive and can only be utilized by trained persons only. This work proposes an approach for the detection of leaf diseases based on the characterization of texture, shape and color properties. The detection of diseases which are detected using ISRC(improved sparse Representation Classifier) technique. First the GENABC clustering approach is applied to the input image to segment the affected area. Then extract the features from the affected area by using feature extraction techniques. In this paper Improved Transform Encoded Local Pattern used to extract the texture feature, Enhanced Gradient Feature (EGF) to extract the shape and Improved Color Histogram Techniques(ICH) are used to extract the color. And then these features are given to the ISRC classifier to get the exact type of disease on affected leaves. To analyze the performance of the proposed method we use four metrics. They are classification accuracy, error rate, precision value and recall value. From the analysis of experimental results, the ISRC method provides the best result than the existing approach.
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References
S. Phadikar; J. Sil (2008), Rice Disease identification using Pattern Recognition Techniques, IEEE Proceedings of 11th International Conference on Computer and Information Technology (ICCIT 2008), Khulna, Bangladesh, pp. 1-4244-2136-7/08, 25-27 December, 2008.
D. Al Bashish; M. Braik; S. Bani-Ahmad (2010), A Framework for Detection and Classification of Plant Leaf and Stem Diseases, IEEE International Conference on Signal and Image Processing, pp. 978-1-4244-8594-9/10.
Z. B. Husin; A. H. B. A. Aziz; A. Y. B. Md Shakaff; R. B. S. M. Farook (2012), Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques, IEEE Third International Conference on Intelligent Systems Modelling and Simulation, pp. 978-0-7695-4668-1/12.
S. Bashir; N. Sharma (2012), Remote Area Plant Disease Detection Using Image Processing, IOSR Journal of Electronics and Communication Engineering (IOSRJECE) ISSN : 2278-2834 Volume 2, Issue 6, PP 31-34, Sep-Oct 2012.
M. Krishnan; M.G.Sumithra (2013), A Novel Algorithm for Detecting Bacterial Leaf Scorch (BLS) of Shade Trees Using Image Processing, IEEE 11th Malaysia International Conference on Communications, Kuala Lumpur, Malaysia pp. 978-1-4799-1532-3/13, 26th - 28th November 2013.
K. R. Gavhale; U. Gawande, K. O. Hajari (2014), Unhealthy Region of Citrus Leaf Detection Using Image Processing Techniques, IEEE International Conference for Convergence of Technology, pp. 978-1-4799-3759-2/14.
W. M. Fadzil; S. Rizam; R. Jailani, M.T Nooritawati (2014), Orchid Leaf Disease Detection using Border Segmentation Techniques, IEEE Conference on Systems, Process and Control (ICSPC 2014), Kuala Lumpur, Malaysia, pp. 978-1-4799-6106-1/14.
U. Mokhtar; M. A. S. Alit; A. E. Hassenian; H. Hefny (2015), Tomato leaves diseases detection approach based on support vector machines, IEEE pp. 978-1-5090-0275-7/15.
S. D. Khirade; A. B. Patil (2015), Plant Disease Detection Using Image Processing, IEEE International Conference on Computing Communication Control and Automation, pp. 978-1-4799-6892-3/15.
G. M. Choudhary; V. Gulati (2015), Advance in Image Processing for Detection of Plant Diseases, International Journal of Advanced Research in Computer Science and Software Engineering, 5(7), [ISSN:2277 128X], pp. 1090-1093.
M. Ramakrishnan; S. A. Nisha (2015), Groundnut Leaf Disease Detection and Classification by using Back Probagation Algorithm, IEEE ICCSP conference, pp. 978-1-4 799-8081-9/15.
P. M. Mainkar; S. Ghorpade; M. Adawadkar (2015), Plant Leaf Disease Detection and Classification Using Image Processing Techniques, International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 4, e-ISSN: 2394 – 3343, p-ISSN: 2394 – 5494.
P. Mitkal; P. Pawar; M. Nagane; P. Bhosale; M. Padwal; P. Nagane (2016), Leaf Disease Detection and Prevention Using Image processing using Matlab, International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 02, [ISSN:2455-1457].
A. S. Jalal; S. R. Dubey (2012), Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns, IEEE Third International Conference on Computer and Communication Technology, pp. 978-0-7695-4872.
M. Jhuria; R. Borse; A. Kumar (2013), Image Processing for Smart Farming: Detection of Disease and Fruit Grading, Proceeding of the IEEE Second International Conference on Image Information Processing, pp. 978-1-4673-6101.
M. Dhakate, A.B. Ingole (2015), Diagnosis of Pomegranate Plant Diseases using Neural Network, IEEE pp. 978-1-4673-8564.
R. R. Nair; S. S. Adsul; N. V. Khabale; V. S. Kawade (2015), Analysis and Detection of Infected Fruit Part Using Improved k-means Clustering and Segmentation Techniques, IOSR Journal of Computer Engineering (IOSR-JCE), pp. 37-41.
A. Awate; D. Deshmankar; S. Sonavane (2015), Fruit Disease Detection using Color, Texture Analysis and ANN, IEEE International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 978-1-4673-7910.
B. J. Samajpati; S. D. Degadwala (2016), Hybrid Approach for Apple Fruit Diseases Detection and Classification Using Random Forest Classifier, IEEE International Conference on Communication and Signal Processing, pp. 978-5090-0396.
S. Varughese; N. Shinde; S. Yadav; J. Sisodia (2016), Learning-Based Fruit Disease Detection Using Image Processing International Journal of Innovative and Emerging Research in Engineering Volume 3, Issue 2, p-ISSN: 2394-5494.
L. Vidya; S.T. Khot.; P. Supriya; M. Gitanjali; L. Vidya (2016), Pomegranate Disease Detection Using Image Processing Techniques, International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering Techniques, Volume 5, Issue 4, ISSN (Print) : 2320 – 3765.