Data mining and wireless sensor network for groundnut pest/disease precision protection

Recent technological developments allowed envisioning sensor devices with distributed ambient sensory network, which could be a potential technology for monitoring various natural phenomena (weather parameters, soil moisture, etc.) at micro level. As days more and more agricultural data are virtually being harvested along with the crops and are being collected/stored in databases, the same data can be used in productive decision making if appropriate data mining techniques are developed/applied. An experiment was conducted with four consecutive (Kharif and Rabi) agricultural seasons in a semi-arid region of India to understand the crop-weather-environment-pest/diseases relations using wireless sensory and field-level surveillance data on closely related and interdependent pest/disease dynamics of groundnut crop. Association rule mining and multivariate regression mining techniques/algorithms were designed/ developed/tailor-made to turn the data into useful information/ knowledge/relations/trends to know crop-weather-environment-pest/disease continuum. These findings have been used for development of prediction models (cumulative and non-cumulative) followed by a web based pest/disease decision support system, which will help the decision makers to take viable ameliorative measures.