Identification of Diseases in Grapes using Gray Level Co-Occurrence Matrix &Wavelet Statistical Features
Abstract
Grapes are a crop that is susceptible to many diseases.
However, the degree of susceptibility varies depending on the variety. When no pest management is carried out, damage can generally be severe. Downy mildew and powdery mildew are the major grape diseases in India. Evidently, these diseases can be easily predicted based on the climatic conditions determined by agricultural experts. Technological strategies using machine vision and artificial intelligence are being investigated to achieve intelligent farming forbetter yield. As a part of the prediction process in Grapes, this paper initially deals with the identification of type of disease that has occurred in a grape vine, with a special focus on its leaves. The first step in an effective pest management program is correct identification
of the disease. This paper uses GLCM (Gray Level Co-occurrence Matrix) and Wavelet statistical Features to determine whether a given grape leaf is affected with Powdery Mildew or Downy Mildew by comparing the statistical features with that of an unaffected leaf. The developed algorithm‟s efficiency can successfully detect and classify
the examined diseases with a precision of 94%.
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H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh
“Fast and Accurate Detection and Classification of Plant Diseases”
International Journal of Computer Applications (0975 – 8887) Volume
– No.1, March 2011
R. S. Sabeenian, V. Palanisamy, “Comparison of efficiency for texture
image classification using MRMRF & GLCM techniques”, International
Journal of Computers Information Technology and Engineering
(IJCITAE), December, Vol. 2, No. 2, pp.87–93, 2008
R. S. Sabeenian, V. Palanisamy, “Texture Based Medical Image
Classification of Computed Tomography Images using MRCSF”,
International Journal of Medical Engineering and Informatics Published
by Inderscience Publications (IJMEI), 1, No. 4 pp. 459-472
R .S. Sabeenian, M. E. Paramasivam, „Handloom Silk Fabric Defect
Detection using First order Statistical Features on a NIOS II ProcessorSpringer International Conference on Advances in Information and
Communication Technologies ICT 2010 held on September 2010 at
Cochin, India, 2010, Volume 101, Part 3, pp 475-477, DOI:
1007/978-3-642-15766-0_77
Anami, Basavaraj S, Savakar, Dayanand G, “Regognition and
Classification of Food Grains, Fruits and Flowers Using Machine
Vision,” International Journal of Food Engineering; Vol. 5: Issue. 4,
, Article 14. DOI: 10.2202/1556- 3758.1673.
B. S. Manjunath, Jens – Ranier Ohm, Vinod V. Vasudevan, and Yamada,
Color and Texture descriptiors, IEEE Transaction on Circuits and
Systems for Video Technology, Vol. 11No. 6, 2001 pp 703- 715.
Al – Bashish, D., M. Bralik and S, Bani – Ahmed, “Detection and
Classification of Leaf Diseases using K – means – based segmentation
and neural – networks- based classification. Inform. Technol. J., 10: 267-
DOI: 10.3923”
Taps kanungo, David M. Mount, Nathan S. Netanyahu, Christine D.
Piatko, Ruthu Silverman, and Angela Y. Wu, “An efficient K-means
clustering Algorithm: Analysis and implementation” IEEE Transactions
on Pattern Analysis and Machine Intelligence, VOl. 24, No. 7, July 2002.
Rumpf, T., A. – K. Mahlein, U. Stenier, E-C. Oerke, H. – W. Dehne, L.
Plumer, Early detection and classification of plant diseases with support
vector Machnies based on hyper spectral reflectance, Computers and
Electronics in Agriculture , Vol 74, Issue 1, 2010, pages 91 – 99.
Weizheng, S, yachum, W, Zhanliang, C, and Hongda, W. “Grading
Method of Leaf Spot Disease based on Image Processing ” International
Conference on Computer Science and Software Engineering Vol 06.
CSSE IEEE Computer Society, Washinton.
M. Mrimehdi and M. Petrou “Segmentation of color Textures” IEEE
Transaction on Pattern Analysis and Machine Intelligence, 2(2): 142-159
Feb 2000.
M. S. Prasad Babu and B. Srinivasa Rao, “Leaves Recognition using back
propagation neural networks-advice for pest & disease control on crops”,
Professor of computer Science and systems Engineering, Andhra
University
Youwen, Tian Tianlai, Li Yan, Niu, “ The recognition of cucumber
Disease Based on Image Processing and support Vector Machine”, Image
and Signal Processing , CISP‟ 08, congress on publication May 2008.
Marc Bartels, Hong Wei, David C. Manson,” Wavelets Packets and co
occurrence Matrices for Texture Based Image Segmentation”
Laine, A., Fan, J., Texture Classification by the wavelet Packet signature
IEEE Transaction, PAMI 15 (11), 1186-1191.
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