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IJMLC 2021 Vol.11(4): 291-297 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.4.1050

Agricultural Machinery Abnormal Trajectory Recognition

Hui Liu, Yujie Qiao, Guofa Zhao, Jingping Cheng, and Zhijun Meng

Abstract—The service system of supervision of agricultural machinery subsoiling operation enables acquisition of a large amount of agricultural machinery movement track data. These trajectories include not only farmland operation track data, but also road driving track data. Their spatial distribution characteristics and attribute data are different. In this paper, we make a study of the abnormal trajectory data in data set, and propose an abnormal trajectory recognition algorithm based on DBSCAN clustering. According to the attribute data of agricultural machinery trajectory, the trajectory is divided to form different types of motion trajectory, then to judge the spatial distribution of different types of agricultural machinery tracks. If the attribute data of the tracks are inconsistent with their spatial distribution, it will be judged as abnormal tracks. The experimental results show that both the accuracy of the algorithm and the recall rate is 98.61%, which can identify the abnormal tracks of agricultural machinery.

Index Terms—Agricultural machinery, subsoiling operation, data mining, clustering algorithm.

Hui Liu, Yujie Qiao, and Guofa Zhao are with the Information Engineering College, Capital Normal University, Beijing 100048, P.R. China (e-mail: liuhui@cnu.edu.cn, qiaoyujie_123@126.com, zgf_mail@163.com).
Jingping Cheng and Zhijun Meng are with the National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R. China (e-mail: chenjp@nercita.org.cn, mengzj@nercita.org.cn).


Cite: Hui Liu, Yujie Qiao, Guofa Zhao, Jingping Cheng, and Zhijun Meng, "Agricultural Machinery Abnormal Trajectory Recognition," International Journal of Machine Learning and Computing vol. 11, no. 4, pp. 291-297, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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