Detection Of Rice Blast Disease Using Pattern Recognition Model
M.Ananthkumar,  Rajesh Kumar.S,  Vasanth.S
The techniques of machine vision are extensively applied to agricultural science, and it has great perspective especially in the plant protection field, which ultimately leads to crops management. The paper describes a software prototype system for rice disease detection based on the infected images of various rice plants. Images of the infected rice plants are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the plants. Then the infected part of the leaf has been used for the classification purpose using neural network. The methods evolved in this system are both image processing and soft computing technique applied on number of diseased rice plants. We proposed in this project to detect the blast disease in rice through image segmentation, HOG (Histogram of Gradient) feature extraction and classify the disease in high evaluated pattern recognition model called SVM (support vector machine). The experimental result shows in MATLAB in accurate manner.
Keywords- MATLAB,Support Vector Machine,Histogram of Gradient,Detection
Cite this Article
M.Ananthkumar,  Rajesh Kumar.S,  Vasanth.S,   "Detection Of Rice Blast Disease Using Pattern Recognition Model"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.7, Issue 2, pp.322-324, May 2019, Available at :http://www.ijedr.org/papers/IJEDR1902062.pdf