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Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

by Hiteshwari Sabrol, Satish Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 126 - Number 1
Year of Publication: 2015
Authors: Hiteshwari Sabrol, Satish Kumar
10.5120/ijca2015905982

Hiteshwari Sabrol, Satish Kumar . Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification. International Journal of Computer Applications. 126, 1 ( September 2015), 44-55. DOI=10.5120/ijca2015905982

@article{ 10.5120/ijca2015905982,
author = { Hiteshwari Sabrol, Satish Kumar },
title = { Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 126 },
number = { 1 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 44-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume126/number1/22519-2015905982/ },
doi = { 10.5120/ijca2015905982 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:16:20.593998+05:30
%A Hiteshwari Sabrol
%A Satish Kumar
%T Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 126
%N 1
%P 44-55
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The plant diseases are a normal part of nature but can cause significant economic, social and ecologic loss globally. It’s difficult to monitor continuously plant health and detection of diseases. The paper presents a survey of recent studies on the area of plant disease recognition and classification from digital images using image processing and soft computing techniques. The main aim of the paper is to focus on the area of plant pathology recognition and classification only. The paper is omitting the disease severity quantification. Although the paper, considering the images of symptoms presents on plant leaves and stems only for limiting the survey. Each considered paper in the review, representing the comprehensive details of the technical implementation of an algorithm. The algorithm begins with digital image acquisition of infected and non-infected plants; perform image preprocessing, differentiate disease infected region from a non-infected region using segmentation, extract features from segmented images for recognition and classification. This survey expected to useful for researchers from plant pathology and pattern recognition field.

References
  1. Al Bashish D, Braik M, Bani-Ahmad S. 2010. A framework for detection and classification of plant leaf and stem diseases. In proceeding of 2010 International conference on signal and image processing. IEEE, Chennai, 113-118. DOI: 10.1109/ICSIP.2010.5697452.
  2. Anthonys G, Wickramarachchi N. 2009. An image recognition system for crop disease identification of paddy fields in Sri Lanka. In Proceeding of 2009 International Conference on Industrial and Information Systems (ICIIS). IEEE, Sri Lanka, 403-407. DOI:10.1109/ICIINFS.2009.5429828.
  3. Arivazhagan S, Shebiah R.N, Ananthi S, Varthini SV. 2013. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int: CIGR Journal Vol. 15(1), 211-217.
  4. Asfarian A, Herdiyeni Y, Rauf A, Mutaqin KM. 2013. Paddy Diseases Identification with Texture Analysis using Fractal Descriptors Based on Fourier Spectrum. In proceeding of International Conference on Computer, Control, Informatics and Its Applications. IEEE, Jakarta, 77-81, DOI: 10.1109/IC3INA.2013.6819152.
  5. Camargo A, Smith JS. 2009a. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst Eng 102(1), 9-21. DOI: 10.1016/j.biosystemseng.2008.09.030/.
  6. Camargo A, Smith JS. 2009b. Image pattern classification for the identification of disease causing agents in plants. Comput Electron Agric, Vol. 66(2009), 121-125. DOI:10.1016/j.compag.2009.01.003.
  7. Du CJ, Sun DW. 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology, Vol 15(2014), 230–249. DOI: 10.1016/j.tifs.2003.10.006.
  8. Garcia J, Barbedo A. 2013. Digital image processing techniques for detecting, quantifying and classifying plant diseases. Barbedo: SpringerPlus 2013, Vol 2:660. DOI:10.1186/2193-1801-2-660.
  9. Gonzalez and Woods. 2008. The title of book(3rd Ed.) Digital Image Processing, Prentice-Hall, New Jersey.
  10. Hairuddin MA, Tahir NM, Baki SRS. 2011. Overview of image processing approach for nutrient deficiencies detection in Elaeis Guineensis. In proceeding of 2011 IEEE International Conference on system engineering and technology, IEEE, Shah Alam, 16-120. DOI:10.1109/ICSEngT.2011.5993432.
  11. Hsu CW, Lin CJ .2002. A comparison of methods for multi-class support vector machines. IEEE Trans Neural Netw, Vol. 13, 415-425. DOI:10.1109/72.991427.
  12. Huang KY. 2007. Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features, Comput Electron Agric Vol. 57, 3-11. DOI:10.1016/j.compag.2007.01.015.
  13. Husin ZB, Abdul Aziz A. HB, Md Shakaff A. YB, Mohamed Farook R. YS. 2012. Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques. In proceeding of 2012 Third International Conference on IntelligentSystem Modelling and Simulation. IEEE, Kota Kinabalu, 291-296. DOI:10.1109/ISMS.2012.33.
  14. Jasmin G.D. and Rajan E.G. 2013. Normalized Vector Codes for Object Recognition using Artificial Neural Networks in the Framework of Picture Description Languages, Global Journal of Computer Science and Technology Neural & Artificial Intelligence, Vol. 13 (2), Global Journals Inc. (USA) 25-33.
  15. Kai S, Zhikun L, Hang S, Chunhong G. 2011. A research of maize disease image recognition of corn based on BP networks. In proceeding of 2011 third international conference on measuring technology and mechatronics automation. IEEE, Shanghai, 246-249. DOI:10.1109/ICMTMA.2011.66.
  16. Krishnan M, Sumrithra MG. 2013. A Novel Algorithm for Detecting Bacterial Leaf Scorch (BLS) of Shade Trees Using Image Processing. In proceeding of IEEE 11th Malaysia International Conference on Communications. IEEE, Kuala Lumpur, 474-478. DOI:10.1109/MICC.2013.6805876.
  17. Kurniawati NN, Abdullah SNHS, Abdullah S, Abdullah S. 2009a. Investigation on image processing techniques for diagnosing paddy diseases. In proceeding of 2009 International conference on soft computing and pattern recognition. IEEE, Malacca, 272-277. /DOI: 10.1109/SoCPaR.2009.62.
  18. Kurniawati NN, Abdullah SNHS, Abdullah S, Abdullah S .2009b. Texture analysis for diagnosing paddy disease. In proceeding of 2009 International conference on electrical engineering and informatics. IEEE, Selangor, 23-27./ DOI: 10.1109/ICEEI.2009.5254824.
  19. Meunkaewjinda A, Kumsawat P, Attakitmongcol K, Srikaew A .2008. Grape leaf disease detection from color imagery using hybrid intelligent system. In proceeding of 2008 5th international conference on electrical engineering/electronics, computer, telecommunications and information technology, IEEE, Krabi, 513-516. DOI:10.1109/ECTICON.2008.4600483.
  20. Molina JF, Gil R, Bojacá C, Gómez F, and Franco H. 2014. Automatic Detection of Early Blight on tomato Crops using a Colo Based Classification Strategy. In proceeding of 2014 19th International Symposium on Image, signal processing and Artificial Vision, IEEE, Armenia, 1-5. DOI:10.1109/STSIVA.2014.7010166.
  21. Oberti R, Marchi M, Tirelli P, Calcante A, Iriti M, Borghese A.N. 2014. Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity. Comput Electron Agric 104(2014), 1-8. DOI:http://dx.DOI.org/10.1016/j.compag.2014.03.001.
  22. Opstad Kruse OM, Prats-Montalbán JM, Indahl UG, Kvaal K, Ferrer A, Futsaether CM .2014. Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Comput Electron Agric Vol. 108(2014), 155-165. DOI:http://dx.DOI.org/10.1016/j.compag.2014.07.010.
  23. Phadikar S, Sil J. 2008. Rice disease identification using pattern recognition techniques. In proceedings of 11th International Conference on Computer and Information Technology (ICCIT 2008), IEEE, Khulna, 420–423. DOI: 10.1109/ICCITECHN.2008.4803079.
  24. Prewitt J .1970. Object enhancement and extraction, Picture processing and psychopictorics, Academic Press, Orlando.
  25. Pydipati R, Burks TF, Lee WS. 2006. Identification of citrus disease using color texture features and discriminant analysis. Comput Electron Agric 52(2006), 49-59. DOI:10.1016/j.compag.2006.01.004.
  26. Sankaran S, Mishra A, Ehsani R, Davis C. 2010. A review of advanced techniques for detecting plant diseases. Comput Electron Agric Vol.72, 1–13. DOI:10.1016/j.compag.2010.02.007.
  27. Sannakki SS, Rajpurohit VS, Nargund VB, Kulkarni P. 2013. Diagnosis and Classification of Grape Leaf Diseases using Neural Networks, In proceeding of 4th International Conference (ICCCNT), IEEE, Tiruchengode, /1-5. DOI: 10.1109/ICCCNT.2013.6726616.
  28. Sanyal P, Bhattacharya U, Parui SK, Bandyopadhyay SK, Patel S. 2007. Color texture analysis of rice leaves diagnosing deficiency in the balance of mineral levels towards improvement of crop productivity. In proceeding of 10th International Conference on Information Technology (ICIT 2007), IEEE, Orissa, 85-90. DOI: 10.1109/ICIT.2007.40.
  29. Wang H, Li G, Ma Z, Li X. 2012a. Application of neural networks to image recognition of plant diseases. In Proceedings of the 2012 International Conference on Systems and Informatics (ICSAI), IEEE, Yantai, 2159-2164. DOI: 10.1109/ICSAI.2012.6223479.
  30. Wang H, Li G, Ma Z, Li X. 2012b. Image recognition of plant disease based on principal component analysis and neural networks. In Proceedings of the 2012 8th International Conference on Natural Computation (ICNC 2012), IEEE, Chongqing, 246-252. DOI: 10.1109/ICNC.2012.6234701.
  31. Wang D, Wang B, Y Yan. 2013. The identification of powdery mildew spores image based on the integration of intelligent spore image sequence capture device. In proceeding of Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IEEE, Beijing, 170-180. DOI: 10.1109/IIH-MSP.2013.53.
  32. Wang L, Dong F, Guo Q, Nie C, Sun S. 2014. Improved Rotational Kernel Transformation Directional Feature for Recognition of Wheat Stripe Rust and Powdery Mildew. In proceeding of Seventh International Conference on Image and Signal Processing, IEEE, Dalian, 286-291. DOI: 10.1109/CISP.2014.7003793.
  33. Wiwart M, Fordonski G, Zuk-Golaszewska K, Suchowilska E. 2010. Early diagnostics of macronutrient deficiencies in three legume species by color image analysis. Comput Electron Agric 65(2010), 125-132./ DOI:10.1016/j.compag.2010.06.009.
  34. Xu G, Zhang F, Shah SG, Ye Y, Mao H. 2011. Use of leaf color images to identify nitrogen and potassium deficient tomatoes. Pattern Recognit Lett Vol. 32(2011), 1584-1590. DOI:10.1016/j.patrec.2011.04.020.
  35. Youwen T, Tianlai L, Yan N. 2008. The recognition of cucumber disease based on image processing and support vector machine. In proceeding of 2008 Congress on image and signal processing, IEEE, Sanya, 262-267./ DOI: 10.1109/CISP.2008.29.
  36. Yao Q, Guan Z, Zhou Y, Tang J, Hu Y, Yang B. 2009. Application of support vector machine for detecting rice diseases using shape and color texture features. In proceeding of 2009 International conference on engineering computation, IEEE, Hong Kong, 79-83. DOI: 10.1109/ICEC.2009.73.
  37. Zhang J, Zang W. 2010. Support Vector Machine For Recognition Of Cucumber Leaf Diseases, In Proceeding of 2nd International Conference on Advanced Computer Control (ICACC), IEEE, Shenyang, 590-594. DOI: 10.1109/ICACC.2010.5487242.
  38. Zhang M. 2010. Citrus canker detection based on leaf images analysis, In proceeding of 2nd International Conference on information science and engineering, IEEE, Hangzhou, 3584-3587. DOI: 10.1109/ICISE.2010.5691630.
  39. Zhang M, Meng Q. 2011. Automatic citrus canker detection from leaf images captured in field. Pattern Recognit Lett, Vol. 32(15), 2036-2046. DOI:10.1016/j.patrec.2011.08.003.
  40. Zhang S, Zhang C. 2013. Orthogonal locally discriminant projection for classification of plant leaf diseases. In proceeding of Ninth International Conference on Computational Intelligence and Security. IEEE, Leshan, 241-245. DOI: 10.1109/CIS.2013.57.
  41. Zhuo Z, Zang Y, Li Y, Zhang Y, Wang P, Luo X. 2013. Rice plant-hopper infestation detection and classification algorithms based on fractal dimension values and fuzzy C-means. Mathematical and Computer Modelling 58(2013), 701–709. DOI:10.1016/j.mcm.2011.10.028.
Index Terms

Computer Science
Information Sciences

Keywords

Plant diseases image processing support vector machine (SVM) neural network.

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