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Deep Learning Approach for the Detection of Plant Diseases

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Padmavathi C.. Published in Artificial Intelligence.

Deep Learning Approach for the Detection of Plant Diseases
Communications on Applied Electronics
Year of Publication: 2021
Publisher: Foundation of Computer Science (FCS), NY, USA
Series: Volume 7 Number 34
Authors: Padmavathi C.
10.5120/cae2021652877
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Padmavathi C.. Deep Learning Approach for the Detection of Plant Diseases. Communications on Applied Electronics 7(34):25-33, March 2021. BibTeX

@article{10.5120/cae2021652877,
	author = {Padmavathi C.},
	title = {Deep Learning Approach for the Detection of Plant Diseases},
	journal = {Communications on Applied Electronics},
	issue_date = {March 2021},
	volume = {7},
	number = {34},
	month = {Mar},
	year = {2021},
	issn = {2394-4714},
	pages = {25-33},
	numpages = {9},
	url = {http://www.caeaccess.org/archives/volume7/number34/875-2021652877},
	doi = {10.5120/cae2021652877},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Plant disease detection is one of the most active topics in the modern agriculture. The disease in plants are season-based which depends on the presence of the pathogen, crops, environmental conditions and varieties grown. The monitoring of leaf area is important in studying physiological capabilities associated with plant. This work makes use of image processing technique for the detection of disease and the use of Support Vector Machine for the classification of plant leaf disease. Plant Leaf disease detection and classification is performed, depending on various extracted features from plant leaves utilizing different image processing and deep learning techniques. Detection of plant leaf disease involves steps like data collection, image processing techniques like contrast enhancement, RGB to HSI, K-means clustering, feature extraction, segmentation and SVM based classification. This approach is useful when image dimensions are large and a reduced feature representation is required to efficiently complete tasks such as image matching and retrieval. The proposed work mainly concentrates on four major diseases that affect the plant leaf namely Alternaria alternata, Anthracnose, Bacterial blight and Cercospora leaf spot. The dataset considered for each disease is 22, 23, 20 and 20 respectively. The results of a test case for each of the four diseases are quantified and the percentage of disease affected area was observed to be 15.0013% in Alternaria alternata, 15.0015% in Anthracnose, 15.0142% in Bacterial blight and 23.0225% in Cercospora leaf spot.

References

  1. Prakash M. Mainkar, Shreekant Ghorpade, Mayur Adawadkar,” Plant Leaf Disease Detection and Classification Using Image Processing Techniques,” International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 4, July 2015.
  2. Mrinal Kumar1 , Tathagata Hazra2 , Dr. Sanjaya Shankar Tripathy,” Wheat Leaf Disease Detection Using Image Processing ,“ International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) Volume VI, Issue IV, April 2017
  3. Ramakrishnan.M and Sahaya Anselin Nisha.A, “Groundnut Leaf Disease Detection and Classification by using Back Probagation Algorithm,” IEEE ICCSP 2015 conference, pp. 978-1-4 799-8081, September 2015.
  4. Shreekant Ghorpade and Mayur Adawadkar, “Plant Leaf Disease Detection and Classification Using Image Processing Techniques,” International Journal of Innovative and Emerging Research in Engineering Volume 2, Issue 4, 2015.
  5. J. D. Pujari, R. Yakkundimath, and A.S. Byadgi, “Image processing based detection of fungal diseases in plants,” Procedia Computer Science, vol. 46, pp.1802-1808, 2015.
  6. Sachin D. Khirade and A. B. Patil, “Plant Disease Detection Using Image Processing,” International Conference on Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on,pp. 768-771. IEEE, 2015.
  7. Pooja, Rahul Das, Kanchana , “Identification of plant disease,” International Conference on Technological Innovations in act for agriculture and rural development, IEEE ,2017
  8. Barbedo JGA ,“Digital image processing techniques for detecting ,quantifying and classifying plant diseases,” Springer Plus2(1):1-12.
  9. Dr. K. Thangadurai ,K. Padmavathu,“Computer vision image enhancement for plant leaves disease detection,” World Congress on computing and communication technologies ,2014.
  10. Anand H Kulkarni and Patil R.K.Ashwin , "Applying Image Processing Technique to Detect Plant Diseases," International Journal of Modern Engineering Research, vol. 2, no. 5, pp. 3661-3664, September-October 2012.
  11. M.K.R.Garhale and P.U.Gawande ,“An overview of the research on plant causes disease detection using image processing technique,” IEEE trans and pattern. Anal. Mach .Intell, vol.16 , no.1,pp.10-16 ,2014.
  12. Zulkifli Bin Husin, Abdul Hallis Bin Abdul Aziz, Ali Yeon Bin Md Shakaff ,Rohani Binti S Mohamed Farook, “Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques,” Third International Conference on Intelligent Systems Modelling and Simulation, 2012
  13. Mrunalini R. Badnakhe, Prashant R. Deshmukh, “Infected Leaf Analysis and Comparison by Otsu Threshold and k-Means Clustering,” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3, March 2012.
  14. 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 17– No.1, March 2011
  15. Arivazhagan, S., R. N. Shebiah, S. S. Nidhyanandhan, and Bauer, S. D., F. Korc, W. Forstner , “The potential of automatic methods of classification to identify leaf diseases from multispectral images” ,Volume 12: pp.361-377, 2011.
  16. K. Elangovan , S. Nalini ,“Plant Disease Classification Using Image Segmentation and SVM Techniques,” IJCIRV ISSN 0973-1873 Volume 13, Number 7 ,2017
  17. Sonal P Patel. Mr. Arun Kumar Dewangan, “A Comparative Study on Various Plant Leaf Diseases Detection and Classification,” (IJSRET), ISSN 2278 – 0882 Volume 6, Issue 3, March 2017
  18. R.Rajmohan, M.Pajany, “Smart paddy crop disease identification and management using deep convolution neural network & svm classifier,” International journal of pure and applied mathematics, vol 118, no 5, pp. 255-264, 2017.
  19. Abdullah NE, Rahim AA, Hashim H, Kamal MM, “Classification of rubber tree leaf diseases using multilayer perceptron neural network,” 5th student conference on research and development. Selangor: IEEE; 2007
  20. Al Bashish D, Braik M, Bani-Ahmad S, “A framework for detection and classification of plant leaf and stem diseases,” International conference on signal and image processing. Chennai: IEEE; 2010:113-118.
  21. Anthonys G, Wickramarachchi N , “An image recognition system for crop disease identification of paddy fields in Sri Lanka,” International Conference on Industrial and Information Systems (ICIIS). Sri Lanka: IEEE; 2009
  22. Hairuddin MA, Tahir NM, Baki SRS,”Overview of image processing approach for nutrient deficiencies detection in Elaeis Guineensis,” IEEE international conference on system engineering and technology. Shah Alam: IEEE; 2011
  23. Kurniawati NN, Abdullah SNHS, Abdullah S, Abdullah S,” Investigation on image processing techniques for diagnosing paddy diseases,” International conference of soft computing and pattern recognition. Malacca: IEEE; 2009
  24. Pang J, Bai Zy, Lai Jc, Li Sk, “Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing,” International conference on image analysis and signal processing. Hubei: IEEE, 2011.

Keywords

Plant leaf disease, K-means clustering, image processing, deep learning