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A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm

A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm

K. Lavanya, M.A. Saleem Durai, N.Ch.S.N. Iyengar
Copyright: © 2016 |Volume: 7 |Issue: 2 |Pages: 18
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781466691995|DOI: 10.4018/IJAEIS.2016040103
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MLA

Lavanya, K., et al. "A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm." IJAEIS vol.7, no.2 2016: pp.52-69. http://doi.org/10.4018/IJAEIS.2016040103

APA

Lavanya, K., Durai, M. S., & Iyengar, N. (2016). A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 7(2), 52-69. http://doi.org/10.4018/IJAEIS.2016040103

Chicago

Lavanya, K., M.A. Saleem Durai, and N.Ch.S.N. Iyengar. "A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 7, no.2: 52-69. http://doi.org/10.4018/IJAEIS.2016040103

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Abstract

Disease prediction is often characterized by a high degree of fuzziness and uncertainty. This may reside in the imperfect and complex nature of symptoms that aids in diagnosis.. For precise rice disease diagnosis, domain knowledge of expertise pathologists along with clinically screened database of crop symptoms is considered as knowledge base. The hybrid method pre treats the crop symptoms for removal of noise and redundancy. It forms as target data for rice disease diagnostic model. The Entropy assisted GEANN algorithm reduces the n- dimensionality of diagnostic symptoms and optimizes the target data search space for higher accuracy. Finally the neuro fuzzy system make way for prediction of diseases based on the rules derived from qualitative interpretation of crop symptoms uniqueness. The algorithm is tested for real time case studies of Vellore district, Tamilnadu, India and the results evolved consistent performance against regression, back propagation algorithm and fuzzy network in disease prediction.

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