Automatic Rice Leaf Diseases Detection Using SVM

Authors

  • Hema Rajini Alagappa Chettiar Government College of Engineering and Technology, Karaikudi – 630003, Tamilnadu, India

Keywords:

expectation maximization, pyramid of histograms of orientation gradients, support vector machine

Abstract

Rice leaf diseases can be detected and recognized automatically. This proposed method, combines super pixels, expectation maximization algorithm, and pyramid of histograms of orientation gradients, to recognize rice diseases. In the proposed method first, pre-processing is performed. Second simple linear iterative clustering is used to divide a diseased leaf image into a number of compact regions, which can dramatically accelerate the convergence speed of the expectation maximization algorithm that is adopted to segment the diseased leaf regions and obtain the lesion image. Third, the pyramid of histograms of orientation gradients features are extracted from the segmented lesion image. In the fourth stage extracted features are reduced using principal component analysis Finally, support vector machine is used to classify and recognize different rice diseases. A database of rice diseased leaf images, is taken to conduct the experiment and the results show that the proposed method is effective and feasible for recognizing rice diseases.

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References

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Published

2018-01-27

How to Cite

Rajini, H. (2018). Automatic Rice Leaf Diseases Detection Using SVM. International Research Journal of Management, IT and Social Sciences, 5(1), 86–94. Retrieved from https://sloap.org/journals/index.php/irjmis/article/view/897

Issue

Vol. 5 No. 1 (2018): January

Section

Peer Review Articles
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