Author
Listed:
- Lili Yang
(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)
- Xinxin Wang
(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)
- Yuanbo Li
(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)
- Zhongxiang Xie
(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)
- Yuanyuan Xu
(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)
- Rongxin Han
(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)
- Caicong Wu
(Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China)
Abstract
Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the “turning”, “working”, and “abnormal working” trajectories for wheat harvester in-field operation scenarios. This method contains two stages: clustering and correction. The clustering stage performs by the two-step K-means iterative clustering method (D-K-means). In the correction stage, the first step (M1) is performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) is based on the direction change of the “turning” and “abnormal working” trajectories. The third correction step (M3) is based on the operating characteristics to specify the start and stop positions of the turning. The developed method was validated by 50 trajectories. The results for the three trajectories and the five time intervals from 1 s to 5 s both have f1-scores above 0.90, and the f1-score using only the clustering method and the method of this paper increased from 0.55 to 0.95. After removing the turning and abnormal operation trajectories, the error of calculating farmland area with distance algorithm is reduced by 17.04% compared with that before processing.
Suggested Citation
Lili Yang & Xinxin Wang & Yuanbo Li & Zhongxiang Xie & Yuanyuan Xu & Rongxin Han & Caicong Wu, 2022.
"Identifying Working Trajectories of the Wheat Harvester In-Field Based on K-Means Algorithm,"
Agriculture, MDPI, vol. 12(11), pages 1-13, November.
Handle:
RePEc:gam:jagris:v:12:y:2022:i:11:p:1837-:d:961232
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