Abstract
To meet the application requirements like the automatic recognition of operation status for agricultural machinery, farmland and rural road network data updates, and so on, the matching templates of the agricultural machinery operation status are generated by data mining based on a historical GNSS dataset. Combining the field research and the historical tracks, the agricultural machinery operation is divided into four main types: parking status in the garage, transferring status on the road, working status in the field, and transferring status between the fields. Statistical analysis shows that the three features, which are location, speed and direction, can be used for data mining, and the feature map of agricultural machinery operation status is made and the matching templates are generated. Then the farmland data and road network topological diagram information are extracted. Experiments show that the templates have a good accuracy, better than 90%, and can meet the basic application demand mentioned above.
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Acknowledgments
This work is in part supported by the National High Technology Research and Development Program of China (No. 2012AA101902) and the project of Opening Fund of State Key Laboratory of Soil Plant Machinery System Technology. Thanks for the project funding provided by the Beijing Popularization of Agricultural Machinery Station, as well as the experimental agricultural machinery and the coordination during the experiment provided by the Agricultural Machinery Industry Association in Pinggu District, Beijing, and the Xingnongtianli Agricultural Services Professional Cooperative in Shunyi District, Beijing.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Cai, Y., Wu, C., Luo, M., Ding, L., Su, H. (2012). Automatic Recognition Method of Operation Status for Agricultural Machinery Based on GNSS Data Mining. In: Sun, J., Liu, J., Yang, Y., Fan, S. (eds) China Satellite Navigation Conference (CSNC) 2012 Proceedings. Lecture Notes in Electrical Engineering, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29187-6_13
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DOI: https://doi.org/10.1007/978-3-642-29187-6_13
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