Extracting spatial information from social media in support of agricultural management decisions

Abstract

Farmers face pressure to respond to unpredictable weather, the spread of pests, and other variable events on their farms. This paper proposes a framework for data aggregation from diverse sources that extracts named places impacted by events relevant to agricultural practices. Our vision is to couple natural language processing, geocoding, and existing geographic information retrieval techniques to increase the value of already-available data through aggregation, filtering, validation, and notifications, helping farmers make timely and informed decisions with greater ease.