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The plots were harvested with a combine and the yield/ha was calculated; 1,000-seed weights were obtained and samples were analyzed for oil content.

Data driven precision agriculture aspects, particularly the dynamic disease management, require dynamic crop-weatherenvironment data at micro level. An experiment was conducted during four consecutive seasons (2009 Kharif, 2009-10 Rabi, 2010 Kharif and 2010-11 Rabi) in a semi-arid tropic region of India to understand the crop-weather-environmentdisease relations using wireless sensory and field-level surveillance data on the groundnut crop for Leaf Spot (LS) disease, which is economically important yet more prone in the semi-arid tropic. Tailor-made various data mining techniques (Naïve Bayes classification with Gaussian distribution, rapid association rule mining and multivariate regression mining) were developed and applied to turn the data into useful information/knowledge/relations/trends and correlation to understand crop-weather-environment-disease continuum. These dynamics obtained from the data mining techniques and

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