International Journal of Advanced and Applied Sciences
Int. j. adv. appl. sci.
Print ISSN: 2313-626X
Volume 4, Issue 8 (August 2017), Pages: 79-83
Title: Detection and classification of leaf diseases using integrated approach of support vector machine and particle swarm optimization
Author(s): Prabhjeet Kaur *, Sanjay Singla, Sukhdeep Singh
Computer Science Engineering, IET Bhaddal Technical Campus, Rupnagar, India
Full Text - PDF XML
Plant diseases are one of the common factors responsible for the decrease in plant growth. Plant diseases are analyzed with their leaves. Many researchers have analyzed the different methods to detect the leaf diseases but the evaluated results are not appropriate enough. So, in this paper we have presented an integrated approach of particle swarm optimization (PSO) and support vector machine (SVM) for plant leaf disease detection and classification. Here, the disease affected dataset of plant leaves is considered that is suffered with four diseases Cercospora leaf spot, bacterial blight, anthracnose, and Alternaria alternata. The main objective of this paper is to detect the disease affected portion of leaf and healthy portion of leaf. We have calculated the percentage of leaf affected portion with their classification. Overall results are evaluated in the form of accuracy of proposed integrated approach.
© 2017 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Leaf disease detection, Particle swarm optimization, K-means clustering, Support vector machine
Article History: Received 19 March 2017, Received in revised form 2 July 2017, Accepted 7 July 2017
Digital Object Identifier:
Kaur P, Singla S, and Singh S (2017). Detection and classification of leaf diseases using integrated approach of support vector machine and particle swarm optimization. International Journal of Advanced and Applied Sciences, 4(8): 79-83
- Amari SI and Wu S (1999). Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6): 783-789. https://doi.org/10.1016/S0893-6080(99)00032-5
- Atas M, Yardimci Y, and Temizel A (2011). Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning. In the SPIE 8027 Conference on, Security, and Sensing, International Society for Optics and Photonics (SPIE), Bellingham, USA: 80270F-80270F. https://doi.org/10.1117/12.883237
- Barbedo JGA (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springer Plus, 2(1):1-12. https://doi.org/10.1186/2193-1801-2-660
- Clerc M (2010). Particle swarm optimization. John Wiley and Sons, New Jersey, USA.
- Contreras-Medina LM, Osornio-Rios RA, Torres-Pacheco I, Romero-Troncoso RDJ, Guevara-González RG, and Millan-Almaraz JR (2012). Smart sensor for real-time quantification of common symptoms present in unhealthy plants. Sensors, 12(1): 784-805. https://doi.org/10.3390/s120100784 PMid:22368496 PMCid:PMC3279240
- Dandawate Y and Kokare R (2015). An automated approach for classification of plant diseases towards development of futuristic decision support system in Indian perspective. In the International Conference on Advances in Computing, Communications and Informatics (ICACCI'15), IEEE, Kerala, India: 794-799. https://doi.org/10.1109/ICACCI.2015.7275707
- De Groot RS, Wilson MA, and Boumans RM (2002). A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecological Economics, 41(3): 393-408. https://doi.org/10.1016/S0921-8009(02)00089-7
- Gavhale KR, Gawande U, and Hajari KO (2014). Unhealthy region of citrus leaf detection using image processing techniques. In the International Conference for Convergence of Technology, IEEE: 1-6. https://doi.org/10.1109/I2CT.2014.7092035
- Kaur R and Kang SS (2015). An enhancement in classifier support vector machine to improve plant disease detection. In the IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE'15), IEEE, Amritsar, India: 135-140. https://doi.org/10.1109/MITE.2015.7375303
- Kennedy J (2011). Particle swarm optimization. In: Sammut C and Webb GI (Eds.), Encyclopedia of Machine Learning: 760-766. Springer US, USA.
- Khirade SD and Patil AB (2015). Plant disease detection using image processing. In the International Conference on Computing Communication Control and Automation (ICCUBEA'15), IEEE, Pune, India: 768-771. https://doi.org/10.1109/ICCUBEA.2015.153
- Patil SB and Bodhe SK (2011). Leaf disease severity measurement using image processing. International Journal of Engineering and Technology, 3(5): 297-301.
- Poli R, Kennedy J, and Blackwell T (2007). Particle swarm optimization. Swarm Intelligence, 1(1): 33-57. https://doi.org/10.1007/s11721-007-0002-0
- Pujari JD, Yakkundimath R, and Byadgi AS (2014). December. Identification and classification of fungal disease affected on agriculture/horticulture crops using image processing techniques. In the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC'14), IEEE: 1-4. https://doi.org/10.1109/ICCIC.2014. 7238283
- Raven PH, Evert RF, and Eichhorn SE (2005). Biology of plants. Macmillan, London, England.
- Rothe PR and Kshirsagar RV (2015). Cotton leaf disease identification using pattern recognition techniques. In the International Conference on Pervasive Computing (ICPC'15), 2015, IEEE, Pune, India: 1-6. https://doi.org/10.1109/PERVASIVE.2015.7086983
- Sankaran S, Mishra A, Ehsani R, and Davis C (2010). A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72(1): 1-13. https://doi.org/10.1016/j.compag.2010.02.007
- Singh V and Misra AK (2015). Detection of unhealthy region of plant leaves using Image Processing and Genetic Algorithm. In the International Conference on Advances in Computer Engineering and Applications (ICACEA'15), IEEE: 1028-1032. https://doi.org/10.1109/ICACEA.2015.7164858
- Suykens JA and Vandewalle J (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3): 293-300. https://doi.org/10.1023/A:1018628609742
- Tong S and Chang E (2001). Support vector machine active learning for image retrieval. In the 9th ACM International Conference on Multimedia, ACM, Ottawa, Canada: 107-118. https://doi.org/10.1145/500141.500159
- Wang H, Li G, Ma Z, and Li X (2012). Application of neural networks to image recognition of plant diseases. In the International Conference on Systems and Informatics (ICSAI'12), IEEE, Yantai, China: 2159-2164. https://doi.org/10.1109/ICSAI.2012.6223479