Data Mining and Wireless Sensor Network for Groundnut Pest/Disease Interaction and Predictions - A Preliminary Study

Data driven precision agriculture aspects, particularly the pest/disease management, require a dynamic crop-weather data. An experiment was conducted in semi-arid region of India 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 (peanut) crop. Various 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 ground level surveillance data. It was found that Bud Necrosis viral disease infection is strongly influenced by Humidity, Maximum Temperature, prolonged duration of leaf wetness, age of the crop and propelled by a carrier pest Thrips. Results obtained from the four continuous agriculture seasons (monsoon & post monsoon) data has led to develop cumulative and non-cumulative prediction models, which can assist the user community to take respective ameliorative measures.