Impact of sampling interval on GIS data based productivity calculations and optimal configuration and lead time of corn stover biomass harvesting teams

Analyzing GIS data collected from an agricultural machine is an invaluable tool in machine development, as well as fleet management. This data provides knowledge about machine performance characteristics that helps producers and managers make informed decisions to improve machine productivity. The frequency at which this data is collected, called the sampling interval, can have a significant impact on the machine productivity calculations. This study looks specifically at this impact and how increasing the sampling interval influences those productivity calculations. If the sampling interval can be increased without significantly changing the outcome of the productivity calculations, it would be advantageous to do so because it would decrease the amount of data generated and shorten data processing times. Currently data is collected at one hertz or once per second. Alternative sampling intervals of 2, 5, 10, 15, 30, and 60 seconds were tested in this study. 15 seconds was found to be the longest interval that did not significantly impact the results of the productivity calculations. The 15 second interval provides a 93% decrease in data which will significantly reduce data storage requirements and data processing time. As new telematics data collection systems enter the market place, reducing the amount of data means more reliable remote data transfer from these devices. 14 Introduction With the increase in technology on agricultural equipment comes the opportunity to collect vast amounts of data with relative ease. The machines CAN bus communication system provides information about the entire machine and by looking at the right combination of functions it is possible to determine what operations the machine is performing. When this data is combined with a GPS location, it creates a GIS data set loaded with information about the machines, location, performance, and productivity. Providing this information to producers and managers allows them to make informed data driven decisions that improve machine productivity. These systems also provide great data sets for researchers to glean very specific information about machine performance or other parameters that they may be testing. This sort of research based data set requires a high sampling rate in order to achieve the necessary resolution in the data so that accurate conclusions can be made from it. The current sampling interval used for this type of data collection is one hertz. This was originally chosen because it was easy to calculate performance metrics on a time basis, but was also a high enough frequency that in-depth research analysis could be performed. As the productivity calculations have evolved, the data set used to make them has decreased; there are only a few key parameters used in these calculations now, mainly engine speed, PTO speed and vehicle speed. Also, the focus of these data collection systems has shifted away from research and more towards machine monitoring. The system now directly calculates productivity and other specific machine factors; there is no longer a need to log everything on the CAN bus, only the messages containing the specific machine parameters 15 required for the productivity calculations are logged. This has greatly decreased the data log file size but there is still need to further decrease it. Recently, telematics has become the focus of large fleet management research. These systems allow data to be collected on a machine and then wirelessly transferred back to a central server location, where the data is stored, organized, and processed. This allows for real-time analysis of machine productivity throughout the day, but more importantly it gives managers the information they need to make informed data driven decisions about machine logistics. The large data log file sizes make it difficult to transmit over the wireless networks that these systems utilize. By increasing the sampling interval, it would greatly reduce the amount of data that is generated and therefore decrease the amount of data that needs to be transferred. This increases the speed and reliability of the data transfer process and decreases the risk of lost or corrupted data. This study focuses on increasing the sampling interval time as a solution to decreasing the data log file size. Research Objective The objective of this research is to determine the effects of GIS data sampling interval on machine productivity calculations. Currently the CyCAN system records GIS data samples every second. If data collection intervals can be increased with minimal effects to the productivity metrics, then it will be advantageous to do so. This will involve performing the same productivity calculations on the same data set using alternative sampling intervals.