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Detection of Diseases on Cotton Leaves and its Possible Diagnosis
Viraj Ashokrao Gulhane, Ajay A. Gurjar
Pages - 590 - 598     |    Revised - 01-11-2011     |    Published - 15-12-2011
Volume - 5   Issue - 5    |    Publication Date - November / December 2011  Table of Contents
Image Processing Application in Agriculture Scienc, Coading Analysis and Recognition, Biomedical Image Processing
In a research of identifying and diagnosing cotton disease, the pattern of disease is important part in that, various features of the images are extracted viz. the color of actual infected image, there are so many diseases occurred on the cotton leaf so the leaf color for different diseases is also different, also there are various other features related to shape of image, also there are different shape of holes are present on the leaf of the image, generally the leaf of infected image have elliptical shape of holes, so calculating the major and minor axis is the major task . The features could be extracted using self organizing feature map together with a back-propagation neural network is used to recognize color of image. This information is used to segment cotton leaf pixels within the image, now image which is under consideration is well analyzed and depending upon this software perform further analysis based on the nature of this image.
CITED BY (31)  
1 Bhong, V. S., & Pawar, B. V. Study and Analysis of Cotton Leaf Disease Detection Using Image Processing.
2 Warne, P. P., & Ganorkar, S. R. (2015). Detection of Diseases on Cotton Leaves Using K-Mean Clustering Method.
3 Yun, S., Xianfeng, W., Shanwen, Z., & Chuanlei, Z. (2015). PNN based crop disease recognition with leaf image features and meteorological data. International Journal of Agricultural and Biological Engineering, 8(4), 60.
4 Islam, R., & Islam, M. R. (2015). An Image Processing Technique to Calculate Percentage of Disease Affected Pixels of Paddy Leaf. International Journal of Computer Applications, 123(12).
5 Kalmegh, S. M., & Dhopte, S. V. Determination of Nitrogen Deficiency in Soya Bean Using Edge Detection.
6 CHAVHAN, S. D., & dudhe, a. an efficient disease diagnostic and treatment system for cotton plant using digital image processing.
7 Majumdar, D., Kole, D. K., Chakraborty, A., & Majumder, D. D. (2015, August). An Integrated Digital Image Analysis System for Detection, Recognition and Diagnosis of Disease in Wheat Leaves. In Proceedings of the Third International Symposium on Women in Computing and Informatics (pp. 400-405). ACM.
8 Vibhute, A. S. (2015). An Image Processing Approach for Fertilizer and Pesticide Management.
9 Prashar, K., Talwar, R., & Kant, C. (2015). A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set.
10 Jagadeesh, D. P., Yakkundimath, R., & Byadgi, A. S. (2014). Neuro-kNN classification system for detecting fungal disease on vegetable crops using local binary patterns. Agric Eng Int: CIGR Journal, 16(4), 299-308.
11 Ahmad, F., & Airuddin, A. (2014). Leaf Lesion Detection Method Using Artificial Bee Colony Algorithm. In Advances in Computer Science and its Applications (pp. 989-995). Springer Berlin Heidelberg.
12 Tijare, N. S., & Sagar, S. B. (2014). Image Recognition Based Crop Disease Identification System: A Survey. International Journal of Computer Science and Mobile Computing, 3(4), 868-873.
13 Chaudhari, V., & Patil, C. Y. (2014). Disease Detection of Cotton Leaves Using Advanced Image Processing. International Journal of Advanced Computer Research, 4(2), 653.
14 Jaganathan, V., & Arumugam, S. (2014). Powdery Mildew Disease Identification in Karpoori Variety of Betel vine Plants Using Histogram Based Techniques. Advances in Image and Video Processing, 2(5), 63-75.
15 Majumdar, D., Kole, D. K., Chakraborty, A., & Dutta, D. review: detection & diagnosis of plant leaf disease using integrated image processing approach.
16 Ahmad, F., & Airuddin, A. An Edge Detection Algorithm to Identify Multi-Size Lesions.
17 Khairnar, K., & Dagade, R. (2014). Disease Detection and Diagnosis on Plant using Image Processing â [euro]" A Review. International Journal of Computer Applications, 108(13).
18 Revathi, P., & Hemalatha, M. (2014). Cotton Disease Identi?cation Using Proposed CIG-DFNN Classifier. Asian Journal of Scientific Research, 7(2), 225-231.
19 Revathi, P., & Hemalatha, M. (2014). Cotton Disease Identification Using Proposed CIG-DFNN Classifier. Asian Journal of Scientific Research, 7(2), 225.
20 Wan Mohd Fadzil, W. M. N., Rizam, M. S. B., Jailani, R., & Nooritawati, M. T. (2014, December). Orchid leaf disease detection using border segmentation techniques. In Systems, Process and Control (ICSPC), 2014 IEEE Conference on (pp. 168-173). IEEE.
21 Ahmad, F., & Airuddin, A. (2014, December). Geometry based hybrid method for determining lesion area. In Information and Communication Technologies (WICT), 2014 Fourth World Congress on (pp. 246-250). IEEE.
22 Anasane, a., & tufail, m. international journal of pure and applied research in engineering and technology.
23 Longani, S., & Dixit, V. V. Pest Detection on Leaves Using Poission’s Thresholding Techniques.
24 Revathi, P., & Hemalatha, M. (2013, January). SMS Based HPCCDD Algorithm for the Identification of Leaf Spot Diseases. In Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012) (pp. 49-57). Springer India.
25 Bodhe, T. S., & Mukherji, P. (2013, January). Selection of color space for image segmentation in pest detection. In Advances in Technology and Engineering (ICATE), 2013 International Conference on (pp. 1-7). IEEE.
26 RAHUMAN, S. A. A., & VEERAPPAN, D. A Peer Reviewed International Journal.
27 Warne, P. P., & Ganorkar, S. R. Detection of Disease on Cotton Leaves Using Gabor Filter Method.
28 Revathi, P., & Hemalatha, M. (2012). Computing Performance evaluation of Cotton leaves Spot diseases recognition using Image Segmentation. International Journal of Advanced Research in Computer Science, 3(3).
29 Samanta, D., & Ghosh, A. (2012). Histogram Approach for Detection of Maize Leaf Damage. International Journal of Computer Science and Telecommunications, 3(2), 26-28.
30 Revathi, P., & Hemalatha, M. (2012, July). Advance computing enrichment evaluation of cotton leaf spot disease detection using Image Edge detection. In Computing Communication & Networking Technologies (ICCCNT), 2012 Third International Conference on (pp. 1-5). IEEE.
31 Revathi, P., & Hemalatha, M. (2012, December). Classification of cotton leaf spot diseases using image processing edge detection techniques. In Emerging Trends in Science, Engineering and Technology (INCOSET), 2012 International Conference on (pp. 169-173). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A.Meunkaewjinda, P. Kumsawat, K.Attakitmongcol And A.Sirikaew“ Grape leaf disease detection from colour imaginary using Hybrid intelligent system. ”proceedings of ECTI-CON 2008.
Detection of Citrus Greening Using Microscopic Imaging by Dae G. Kim
Fu, K.S., Mui, J.K., 1981.” A survey on image segmentation. Pattern Recognition” 13(1)
Funck J.W., Zhong Y., Butler D.A., Brunner C.C., ForrerJ.B.,2003.” Image segmentation algorithms applied to wood defect detection.” Computers and Electronics in Agri 41(13).
http://www.pdkv.ac.in/CottonUnit.php(cotton area in viderbha)
Jiazhi Pan,Young He. “Recognition of plants by leaves digital image and neural network”IEEE proceedings on 2008 International Conference on Computer Science and Software Engineering.
Liu C and Wechsler H, “Gabor feature based classification using the enhanced fisher discriminant model for face recognition,” IEEE Transactions on image processing, vol.11, pp46,2002.
Management of seeding diseases of Cotton by Thomas Isakeit, Associate professor and extension plant pathologist Texas A&M University college station.
W.C. Schnathorst and P.M. Halisky “Potentially serious cotton disease, angular leaf spot established in California”
Yan Cheng Zhang, Han Ping Mao, Bo Hu, Ming Xili “features selection of Cotton disease leaves image based on fuzzy feature selection techniques” IEEE Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing,China, 2-4 Nov. 2007.
Mr. Viraj Ashokrao Gulhane
Sipna college of engineering and technology - India
Dr. Ajay A. Gurjar
Sipna College of engineering and Tech. - India