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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.
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