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A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 132 - Number 7 |
| Year of Publication: 2015 |
| Authors: Kapil Prashar, Rajneesh Talwar, Chander Kant |
10.5120/ijca2015907517
|
Kapil Prashar, Rajneesh Talwar, Chander Kant . A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set. International Journal of Computer Applications. 132, 7 ( December 2015), 32-39. DOI=10.5120/ijca2015907517
Abstract
The cotton leaf disease detection is the process of detecting disease by analyzing their visual properties. The visual properties extraction process from the images is known as the feature extraction. The feature extraction process can be done using the various feature descriptors like SIFT, SURF or other most suitable candidate. The feature descriptors are then passed to the classifier for the evaluation of the feature. The classifier is the algorithm, which is used to classify the feature on the basis of its similarity with the training dataset. The training dataset is the collection of features previously extracted from the known objects (the leaves with specific disease in this case). The leaves with disease are classified on the basis of their similarity with the training dataset of disease samples previously described by the feature descriptors. In this paper, our aim is to solve the cotton disease detection problem using the image processing techniques automatically from the input image. The disease classification will primarily based upon the visibility of the disease on the cotton leaves, which further can be used for the identification using the classifier. The proposed model implementation would be done using the MATLAB simulator and the proposed model results would be obtained in the form of the accuracy, precision, recall, elapsed time and many other similar parameters.
References
- Ole Mathis Opstad Kruse, José Manuel Prats-Montalbán, Ulf Geir Indahl, Knut Kvaal, Alberto Ferrer, Cecilia Marie Futsaether “Pixel classification methods for identifying and quantifying leaf surface injury from digital images”, Computers and Electronics in Agriculture, Elsevier 108 (2014) 155–165
- S. Arivazhagan, R. Newlin Shebiah, S. Ananthi, S. Vishnu Varthini, “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture feature,” CIGR Journal, vol. 15, no. 1, March 2013
- A.H.Kulkarni, Dr.H.M.Rai, Dr.K.A.Jahagirdar & P.S.Upparamani in the paper titled “A Leaf recognition technique for plant classification using RBPNN and Zernike Moments”, IJARCCE Journal, Vol. 2, Issue 1, January 2013.
- Qinghai He, Benxue Ma, Duanyang Qu,” Cotton pests and diseases detection based on image processing,” TELKOMANIA, Vol 11, no 6, June 2013
- P. Revathi, M. Hemalatha “Advance Computing Enrichment Evaluation of Cotton Leaf Spot Disease Detection Using Image Edge detection”, IEEE-20180, 26-28 July 2012.
- Yinmao Song, Zhihua Diao, Yunpeng Wang, Huan Wang “Image Feature Extraction of Crop Disease,” 2012 IEEE Symposium on Electrical & Electronics Engineering (EEESYM).
- Prof. Sanjay B. Dhaygude, Nitin P. Kumbhar, “Agricultural plant leaf disease detection using image processing” International Journal of Advanced Research in Electrical, Electronics & Instrumentation, Vol 2, Issue 1, Jan 2013
- Haiguang Wang, Guanlin Li, Zhanhong Ma, Xiaolong Li, “Image Recognition of Plant Diseases Based on Principal Component Analysis and Neural Networks” 2012 8th International Conference on Natural Computation (ICNC 2012)
- Anand Kulkarni & Ashavin Patil R.K. “Applying image processing technique to detect plant diseases,” International Journal of Modern Engineering Research(IJMER), ISSN 2249-6645, Vol 2, Issue 5, pp 3661-6264, Sep-Oct 2012
- Tushar J. Haware, Ravindra D. Badgujar and Prashant G. Patil, ”Crop disease detection using image segmentation,” World Journal of Science & Technology, ISSN 2231-2587, Vol 2(4):190-194,2012
- Kamljot Singh Kailey and Gurjinder Singh Sahdra “Content based image retrieval (CBIR) for identifying image based plant disease,” IJCTA, Vol 3(3), 1099-1104, May-June 2012
- Viraj Gulhane and Dr.A.A.Gurjar “Detection of diseases on cotton leaves and its possible diagnosis,” IJIP, Vol(5), Issue(5),2011
- H. Al-Hiary, Bani-Ahmad, M-Reyalat and M.Braik, “Fast and accurate detection and classification of plant diseases”, IJCA, Vol 17, No. 1, March 2011
- Piyush Chaudhari, Anand K. Chaudhari, Dr.A.N. Cheeran and Sharda Godara,”Color transform based approach for disease spot detection on plant leaf”, IJCST, Vol 3, Issue 6, June 2012
- Dheeb Al Bashish, Malik Braik and Sulieman Bani Ahmad “Detection and classification of leaf disease using K-Mean based segmentation and neural network based classification,” International Technology Journal, ISSN 1812-5638, pp 267-275, 2011
- Sanjay B.Patil and Dr. Shrikant K.Bodhe “Leaf disease severity measurement using image processing,” International Journal of Engineering Technology (IJET), Vol 3(5), 297-301, 2011
- A. Camargo, J.S. Smith “Image pattern classification for the identification of disease causing agents in plants” Computers and Electronics in Agriculture 66 (2009) 121–125, Elsevier journal homepage: ww.elsevier.com/locate/compag
- Di Cui, Oin Zhang, Mingan Li, Youfu Zhao and Glen L.Hartman “Detection of Soyabean rust using a multispectral image sensor, ”Springer Link-Journal article, 2009
- Alexander A.Doudkin, Alexander V. Inyutin, Albert I.Petrovsky, Maxim E.Vatkin “Three Level Neural Network for Data Clusterization on Images of infected crop field”, Journal of Research and Applications in Agriculture Engineering Vol.52 (1), 2007
- Panagiotis Tzionas, Stelios E. Papadakis and Dimitris Manolaki,” Plant leaves classification based on morphological feature and fuzzy surface selection technique,” International Conference on Technology and Automation ICTA’05, Thessaloniki, Greece, pp 365-370,15-16,2005
- Chandrasekhar, Vijay, Gabor Takacs, David Chen, Sam Tsai, Radek Grzeszczuk and Bernd Girod. "Chog: Compressed histogram of gradients a low bit-rate feature descriptor." In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 2504-2511. IEEE, 2009.
- Wu, Jianxin, and James M. Rehg. "CENTRIST: A visual descriptor for scene categorization." Pattern Analysis and Machine Intelligence, IEEE Transactions on 33, no. 8 (2011): 1489-1501.
- Ke, Yan and Rahul Sukthankar. "PCA-SIFT: A more distinctive representation for local image descriptors." In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, vol. 2, pp. II-506. IEEE, 2004.
- Mikolajczyk, Krystian and Cordelia Schmid. "Scale & affine invariant interest point detectors." International journal of computer vision 60, no. 1 (2004): 63-86.
- Yang, Jianchao, Kai Yu, Yihong Gong and Tingwen Huang. "Linear spatial pyramid matching using sparse coding for image classification." In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 1794-1801. IEEE, 2009.
- Li, Shutao, James T. Kwok, Hailong Zhu and Yaonan Wang. "Texture classification using the support vector machines." Pattern recognition 36, no. 12 (2003): 2883-2893.
- Bartlett, Marian Stewart, Gwen Littlewort, Mark Frank, Claudia Lainscsek, Ian Fasel and Javier Movellan. "Recognizing facial expression: machine learning and application to spontaneous behavior." In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2, pp. 568-573. IEEE, 2005.
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