Abstract: Agricultural data is vast, often unstructured and includes many challenges when working with legacy farm systems on premise in rural areas. For instance, traditional farm equipment such as tractors, sprayers, and combines aren’t often from the same vendor, and it’s complex moving data between them. This is further complicated with the vast array of other systems used by our farmers. Furthermore, the number of sensors in agriculture is astonishing, whether it is sensors that measure the gait of the cow walking into the dairy parlor, or chickens that are pecking. All this data needs to turn into usable information on a global scale to improve the yields farmers get and provide greater visibility into what’s going on both in and out of the farm. In this session, a case study will be shared on how data was collected, normalized and analyzed leveraging the open source HPCC Systems platform from remote Farm Management Systems (used by farmers to manage their farms), and when merged with weather data, soil data and actual machinery data, the analyzed predictions is used to feed Agronomists and Crop Protection/Seed Manufacturers to get recommendations back. The goal is to deliver a precision agriculture solution, helping farmers increase their yield, which then helps feed the growing population of the world.
Bio: Jeff Bradshaw is the founder of Adaptris and Group CTO of Adaptris/F4F/DBT within Reed Business Information. He has spent his career integrating data wherever it resides and in-flight across a number of industries including Agriculture, Airlines, Telecommunications, Healthcare, Government and Finance Jeff has worked with and contributed to a number of international standards bodies and continues to work with large enterprises to help them extract value from their data silos and share data seamlessly with their trading partners to achieve business benefit. For the last few years Jeff has been focusing on Big Data and how to gather that across a wide range of sources to help gain insight into the agri-food supply chain.