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Data Mining and Wireless Sensor Network for Groundnut Pest Thrips Dynamics and Predictions

Author(s): A. K. Tripathy | J. Adinarayana | S. N. Merchant | U. B. Desa | Vijayalakshmi | D. RajiReddy | S. Ninomiya | M. Hirafuji | T. Kiura

Journal: Journal of Emerging Trends in Computing and Information Sciences
ISSN 2079-8407

Volume: 3;
Issue: 6;
Start page: 913;
Date: 2012;
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Keywords: : Data Mining | Knowledge Discovery | Wireless Sensor Network | Precision Farming and Pest/Disease Management

With the advent of data generation, collection and storage technologies, world is overwhelmed with data everywhere. Following this trend, more and more agricultural data are nowadays are virtually being harvested along with the crops and are being collected/stored in databases. As the volume of the data increases, the gap between the amount of the data stored and the amount of the data analyzed increases. Such data can be used in productive decision making if appropriate data mining techniques are applied. 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 of India to understand the crop-weather-pest relations using wireless sensory and field-level surveillance data on the groundnut pest Thrips. Various data mining techniques were used to turn the data into useful information/knowledge/relations/trends and correlation of crop-weather-pest 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 Kharifseasons (monsoon) and 2009-10 & 2010-11 Rabi seasons (post monsoon) data has been used to develop a prediction model. In this work an attempt has been made to develop a viable model for groundnut pest (Thrips) dynamics using the state of the art data mining techniques to understand the hidden correlations (crop-pest–meteorological continuum) and there by development of Multivariate Regression Models which led to development of forewarning system.
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