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Leaf Disease Detection Using Support Vector Machine Based on Image Processing
Abstract
ABSTRACT
The project proposes to build a system wherein we take the images of the leaves and detect if it has been affected by any disease .Usually 30% of the crop produced is wasted because of the diseases affected that affect them; this can be avoided if we detect the disease and use appropriate pesticides. The project can be split up into two main parts: the first part of the project takes the leaf images and detects the disease whereas the second part of the project is a hardware which sprays the required pesticide. The database consists of the defined diseases in it and when we capture a image of the diseased leaf, it tells us the affected disease. The hardware part uses a micro-controller interfaced wirelessly to the software part. Features of shape, color and texture are extracted from these images. After that, these images are classified by support vector machine classifier. When a single feature is used, shape feature has the lowest accuracy and texture feature has the highest accuracy. A combination of texture and color feature extraction results highest classification accuracy.
Keywords: Support vector machine, MATLAB, image processing, classifier, feature extraction
References
Dang K., Sun H., Chanet J. P., et al. Wireless Multimedia Sensor Network for plant disease detections. France-China International Workshop,18-20 September 2013, Clermont-Ferrand, France.
Huddar R., Gowri S., Rupanagudi S.R. Novel Algorithm for Segmentation and Automatic Identification of Pests on Plants using Image Processing. Third International Conference Computing Communication & Networking Technologies (ICCCNT), 26-28 July 2012.
Mukherjee M., Pal T., Samanta D. Damaged Paddy leaf detection using image processing. Volume 3, No. 10, October 2012.
Al-Hiary H., Bani-Ahmad S., Reyalat M. Fast and Accurate Detection and Classification of Plant Diseases. International Journal of Computer Applications (0975 – 8887) Volume 17– No.1, March 2011.
Patil S.B. Leaf Disease Severity Measurement Using Image Processing. International Journal of Engineering and Technology Vol.3 (5), 2011, 297-301.
Martin V., Moisan S. Early Pest Detection in Greenhouses. Computer and Electronics in Agriculture Journal, 2009.
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Peng R., Mao-heng S., You-min Z. ZigBee Routing Selection Strategy Based on Data Services and Energy-balanced ZigBee Routing. IEEE Asia-Pacific conference on Services Computing (December 2006)
Bhanu B.B., Hussain M.A., Ande P. Monitoring of soil parameters for effective irrigation using Wireless Sensor Networks. (ICoAC) Sixth International Conference on Advanced Computing, 211-215p, 2014.
Rani M.U., Kamalesh S. Web based service to monitor automatic irrigation system for the agriculture field using sensor. Advances in Electrical Engineering (ICAEE) International Conference on, 1-5p, 2014.
War1e T., Corke P., Sikka P., et al. Transforming Agriculture through Pervasive Wireless Sensor Networks. IEEE Pervasive Computing, vol. 6, no. 2, 50-57p, 2007.
Patil S.S., Thorat S.A. Early detection of grapes diseases using machine learning and IoT.Cognitive Computing and Information Processing (CCIP) 2016 Second International Conference on, 1-5p, 2016.
Jason L.H. System Architecture for Wireless Sensor Networks. 2003.
Sukkar R., Katz E., Zhang Y., et al. Disease progression modeling using hidden Markov models. Engineering in Medicine and Biology Society (EMBC) 2012 Annual International Conference of the IEEE, 2845-2848p, 2012, August.
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