Row crop grain harvester path optimization in headland patterns - Evans, John T.; Pitla, Santosh K.; Luck, Joe D.; Kocher, Michael (Computers and electronics in agriculture, 2020)
•Genetic Algorithm used to optimized harvester routing.•Yield data used to determine when unloading needed to occur based on route.•Turn automation, without route optimization had the potential to reduce non-working in-field travel by 5.9–17.2 percent.•Route optimization found to reduce non-working travel between 13.8 and 31.5 percent in tested fields. Harvesting is one of the most complex field operations for grain producers requiring route planning that is subject to change based on spatial crop yield and scheduling with support vehicles. Inefficient routing increases operation time which lead to negative impacts including: increased labor cost, crop loss, and unnecessary hours put on expensive machinery. In addition to increased time, inefficient routing also increases the amount of unnecessary in-field travel, which increases fuel cost and the possibility of soil compaction. The goal of this research was to implement an optimization routine that could determine the most efficient harvest pattern for row crop harvesters in actual fields. A Genetic Algorithm was developed that was able to optimize the harvest route and provide a feasible solution in real field conditions. The algorithm was validated using spatial yield data from three fields and the optimized travel path reduced the non-working in-field travel between 13.8 and 31.5 percent.