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Automated Logistics Processing of GIS Data for Agricultural Harvest Equipment
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Paper number 131596410, 2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: http://dx.doi.org/10.13031/aim.20131596410) @2013
Authors: Jeff C Askey, Matthew Darr, Keith Webster, Benjamin Covington, Jeremy Brue
Keywords: GIS, CAN, Data Logistics, Efficiencies, Productivity
Abstract. Technological advancements have significantly eased the communication and control of todayâ€™s agricultural equipment. All machine functions communicate and are controlled through the vehicles controller-area network (CAN) bus. By accessing the CAN bus on a machine it is possible to capture an enormous amount of data that can unlock knowledge about its performances. Collecting and properly analyzing this data, allows gathering information that is useful for better management of machines, which leads to enhanced machine efficiencies and increased productivity. The main objective of this study was to automate the processing of the Geographic Information System (GIS) data collected through CAN bus systems. GIS data allows for specific machinery parameters to be linked to a specific GPS (Global Positioning System) location. The GIS data can then be sorted and mapped spatially on a per field basis, allowing for each field to be processed and analyzed separately. Processing this data using specifically defined metrics allows the data points to be sorted into discrete machine categories, such as â€œActiveâ€ and â€œIdleâ€. Considering the amount of time required to perform such operations manually, this study will automate the logistics processing of GIS data, to reduce turnaround time from raw data to final results. Machine data was obtained during a large production harvest of stover during the fall of 2012. A logical approach of filtering and comparing data, through programming, will allow instances to be automatically accounted. These instances will then be compared in order to achieve the desired results, such as efficiencies and productivities of farm machineries. These instant performance metrics will drive overall supply chain evaluation of key indicators including productivity and efficiency.
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