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.
Highlights
It is an important measure to break down the bottom layer of the plough to increase soil and air permeability and improve the growth conditions of crop roots without disturbing the structure of soil layer
Based on the DBSCAN clustering algorithm, this paper proposes a method for identifying abnormal tracks of agricultural machinery
If the operation depth attribute of track data is inconsistent with the spatial distribution state, the tracks are judged as abnormal tracks
Summary
It is an important measure to break down the bottom layer of the plough to increase soil and air permeability and improve the growth conditions of crop roots without disturbing the structure of soil layer. The aforementioned agricultural machinery subsoiling operation supervision-oriented service system enables acquisition of a large amount of agricultural machinery movement track data, deeply excavates these track big data, analyzes the information behind the data, which is of great significance to a further improvement of the service level of the platform and to a full use of the application value. The purpose of this study is to detect the abnormal state data in the movement track of agricultural machinery, to prevent possible equipment failure or farmers' cheating conduct, to ensure the calculation correctness of the working area of the supervision system, and to provide effective technical support for the correct appropriation of the state's subsidy for subsoiling [8]-[17]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Machine Learning and Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
