Abstract
The agricultural industry is increasingly reliant upon the development of technologies that employ real-time monitoring of machine performance to generate pertinent information for machine operators, owners, and managers. Yield mapping in particular is an important component of implementing precision agricultural practices and assessing spatial variability. In an attempt to generate yield maps in sugarcane, this research estimated yield in the field based on GPS data from harvesters, tractors and semi-trucks. The method was based on identifying “fill events”, which represent a distance through which the tractor/wagon combination traveled in parallel with the harvester, indicating that the wagon was being filled. Each wagon was filled to approximately 10 Mg of sugarcane, which was divided by the fill event distance and row width to determine the yield in Mg ha−1. A total of 76 fill events were observed from a 7.1 ha harvested area. Based on the estimated yield per fill event, a rudimentary yield map was developed, which was expanded into a generalized yield map for the 7.1 ha harvested area.
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Acknowledgements
This project was funded by the Energy Biosciences Institute, University of Illinois at Urbana-Champaign. The authors would like to thank Mr. Brandon Gravois from Edgard, LA for his support in conducting the field experiments on his farm.
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Momin, M.A., Grift, T.E., Valente, D.S. et al. Sugarcane yield mapping based on vehicle tracking. Precision Agric 20, 896–910 (2019). https://doi.org/10.1007/s11119-018-9621-2
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DOI: https://doi.org/10.1007/s11119-018-9621-2