Identifying Working Trajectories of the Wheat Harvester In-Field Based on K-Means Algorithm

Lili Yang, Xinxin Wang, Yuanbo Li, Zhongxiang Xie, Yuanyuan Xu, Rongxin Han and Caicong Wu ()
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Lili Yang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xinxin Wang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Yuanbo Li: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Zhongxiang Xie: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Yuanyuan Xu: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Rongxin Han: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Caicong Wu: Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

Agriculture, 2022, vol. 12, issue 11, 1-13

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.

Keywords: agricultural machinery; trajectory recognition; k-means clustering; machine learning; GNSS data (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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