i-manager Publications

A Comparative Analysis of Leaf Disease Detection using Image Processing Technique

C. M. Samiha*, S. P. Pavan Kumar **

* PG Scholar, Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.

** Assistant Professor, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.

Periodicity:July - September'2018
DOI : https://doi.org/10.26634/jip.5.3.15044

Abstract

The essential part of any ecosystem is plant. All the organisms get energy from plants directly or indirectly. It is important to identify the disease in plant parts like leaf, stem, and fruit. Leaf diseases are caused by virus, bacteria, etc. Normally, a farmer identifies the leaf disease by observing spots, color, and shape of the leaf, but sometimes they take help from the experts to detect diseased leaf or crops. The manual detection of disease is less accurate and complex. Image processing techniques help farmers for timely detection of the diseases. K-Nearest Neighbor (KNN), K-Means clustering, Support Vector Machine (SVM), Artificial Neural Network (ANN), and various segmentation algorithm and classifiers are used for detection and classification of leaf diseases. In this paper, various diseases that occur in parts of the plant and identification of leaf diseases were discussed.

Keywords

ANN Classifier, KNN Classifier, K-Means Clustering Segmentation, GLCM.

How to Cite this Article?

Samiha, C.M., and Kumar, P.S.P., (2018). A Comparative Analysis of Leaf Disease Detection Using Image Processing Technique. i-manager’s Journal on Image Processing, 5(3), 40-46. https://doi.org/10.26634/jip.5.3.15044

References

[1]. Amoda, N., Jadhav, B., & Naikwadi, S. (2014). Detection and classification of plant diseases by image processing. International Journal of Innovative Science, Engineering and Technology, 1(2), 70-74.

[2]. Barbedo, J. G. A. (2016). A novel algorithm for semi-automatic segmentation of plant leaf disease symptoms using digital image processing. Tropical Plant Pathology, 41(4), 210-224.

[3]. Bhargavi, K., & Jyothi, S. (2014). A survey on threshold-based segmentation technique in image processing. International Journal of Innovative Research and Development, 3(12), 234-239.

[4]. Chaudhary, P., Chaudhari, A. K., Cheeran, A. N., & Godara, S. (2012). Color transform-based approach for disease spot detection on plant leaf. International Journal of Computer Science and Telecommunications, 3(6), 65-70.

[5]. Dhaygude, S. B., & Kumbhar, N. P. (2013). Agricultural plant leaf disease detection using image processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(1), 599- 602.

[6]. FAO. (2000). Report of the expert consultation on viticulture in Asia and the Pacific. RAP Publication: 2000/13. Retrieved from: http://www.fao.org/3/a-x6903e. pdf

[7]. Haralick, R. M., Shanmugam, K., Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610-621. doi: 10.1109/TSMC.1973.4309314

[8]. Image Processing Toolbox™ 7 User's Guide. (2013). In TechyLib. Retrieved from https://www.techylib. com/en/view/pancakesnightmute/image_processing_toolbox_users_guide_mathworks

[9]. Krithika, N., & Selvarani, A. G. (2017, March). An individual grape leaf disease identification using leaf skeletons and KNN classification. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-5). IEEE.

[10]. Kulkarni, A. H., & Patil, A. (2012). Applying image processing technique to detect plant diseases. International Journal of Modern Engineering Research, 2(5), 3661-3664.

[11]. Larese, M. G., Namías, R., Craviotto, R. M., Arango, M. R., Gallo, C., & Granitto, P. M. (2014). Automatic classification of legumes using leaf vein image features. Pattern Recognition, 47(1), 158-168.

[12]. Naikwadi, S., & Amoda, N. (2013). Advances in image processing for detection of plant diseases. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 2(11), 168-175.

[13]. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.

[14]. Padol, P. B., & Yadav, A. A. (2016, June). SVM classifier based grape leaf disease detection. In 2016 Conference on Advances in Signal Processing (CASP) (pp. 175-179). IEEE.

[15]. Prakash, R. M., Saraswathy, G. P., Ramalakshmi, G., Mangaleswari, K. H., & Kaviya, T. (2017, March). Detection of leaf diseases and classification using digital image processing. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-4). IEEE.

[16]. Stergiou, C., & Siganos, D. (n.d.). Neural Networks. Retrieved from https://www.doc.ic.ac.uk/~nd/surprise _96/journal/vol4/cs11/report.html

[17]. Suman, T., & Dhruvakumar, T. (2015). Classification of paddy leaf diseases using shape and color features. IJEEE, 7(01), 239-250.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.