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: email@example.com, firstname.lastname@example.org, email@example.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: firstname.lastname@example.org, email@example.com).
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).