Machine Intelligence Research Labs (MIR Labs)
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.