Evaluation of corn harvesting operations with the use of geo-referenced data

Operations Management;Harvest Productivity;Decision Support

Niehaus, Chad ; Hansen ; Alan C.

关键词: Operations Management;    Harvest Productivity;    Decision Support;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/50723/Chad_Niehaus.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Many advances have been made in agricultural machines, specifically combine harvesters, in terms of the size of equipment as well as the intelligence of computers on board the machinery.These advances have led to a big increase in productivity of harvesting operations.This is needed though because as productivity has increased so has the cost associated with operating the machinery.Specific needs are to address the inefficiencies in the harvesting operations because cost associated with inefficient agriculture operations are increasing as the price of fuel, labor, and machinery increases.This study presents an analysis of the machine performance of harvesting operations using on-machine data collection on a large-scale corn production farm in Iowa.The data collection method sought to record data from each combine on a one second interval for the entire year of harvest operations.After the data were collected, it was analyzed to determine how much time was spent in each of the eight machine state classifications that were formulated using metrics that could be collected from the machine.The objective of this analysis was to determine the operation efficiency of the combine machine and to determine inefficiencies that exist in the harvesting operations.In analyzing the machine productivity, ArcGISTM was used to present the machine state classification with a spatial representation.The results showed that the machine states could be analyzed using the data and provide valuable decision support on how to improve harvesting operations.Inefficiencies included 16.1% of the total time some form of machine idling was occurring, 9.12% of the time the machine was performing some type of travel either in the field or road transport, 9.32% of the total time the machine was performing a turn within the headlands of the field, and 2.92% of the total time the machine was unloading grain while not harvesting.The geospatial representation also offered possible adjustments that could be made to the machine state rules for more accurate machine state classification.It was also determined that more than just productivity metrics should reflect the overall performance of operations.There needs to be some quality metrics associated with operations such as grain throughput per hour, amount of grain losses, grain cleanliness, and others.Some of these are collected from machines currently but more sensing is needed to collect more quality metrics associated with harvesting operations.Further research is needed to determine how to quantify this quality of operation aspect as well.By having values for productivity and quality, operators can be compared by performance.