Data mining and wireless sensor network for agriculture pest/disease predictions

Data driven precision agriculture aspects, particularly the pest/disease management, require a dynamic crop-weather data. An experiment was conducted in a semi-arid region to understand the crop-weather-pest/disease relations using wireless sensory and field-level surveillance data on closely relate...

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Bibliographic Details
Published in2011 World Congress on Information and Communication Technologies pp. 1229 - 1234
Main Authors Tripathy, A. K., Adinarayana, J., Sudharsan, D., Merchant, S. N., Desai, U. B., Vijayalakshmi, K., Reddy, D. Raji, Sreenivas, G., Ninomiya, S., Hirafuji, M., Kiura, T., Tanaka, K.
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.12.2011
Subjects
Agriculture
Correlation
Data mining
Diseases
Indexes
Meteorology
Pest/Disease Management
Precision Agriculture
Wireless sensor networks
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ISBN1467301272
9781467301275
DOI10.1109/WICT.2011.6141424

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Summary:Data driven precision agriculture aspects, particularly the pest/disease management, require a dynamic crop-weather data. An experiment was conducted in a semi-arid region to understand the crop-weather-pest/disease relations using wireless sensory and field-level surveillance data on closely related and interdependent pest (Thrips) - disease (Bud Necrosis) dynamics of groundnut crop. Data mining techniques were used to turn the data into useful information/knowledge/relations/trends and correlation of crop-weather-pest/disease continuum. These dynamics obtained from the data mining techniques and trained through mathematical models were validated with corresponding surveillance data. Results obtained from 2009 & 2010 kharif seasons (monsoon) and 2009-10 & 2010-11 rabi seasons (post monsoon) data could be used to develop a real to near real-time decision support system for pest/disease predictions.
ISBN:1467301272
9781467301275
DOI:10.1109/WICT.2011.6141424