Wheat harvester convoys spatiotemporal patterns mining using a recursive search-based DBSCAN algorithm

Authors

  • Weixin Zhai 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Ruijing Han 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Jiawen Pan 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Caicong Wu 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

DOI:

https://doi.org/10.25165/ijabe.v18i6.9793

Keywords:

trajectory data mining, cross-regional convoy, cross-regional agricultural machinery operations, wheat harvester, spatiotemporal patterns

Abstract

Due to varying crop maturity periods and uneven distribution of agricultural machinery, China has developed a unique service model known as cross-regional agricultural machinery operations. Currently, China’s comprehensive mechanization rate for grain crops is relatively high, creating a substantial market for cross-regional agricultural machinery operations. Research on the behavioral patterns of cross-regional agricultural machinery migration is both urgent and significant. Considering the actual rules of cross-regional migration during the wheat harvest and the characteristics of the trajectory data, this paper proposes a trajectory mining method using a recursive search-based DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. One representative finding of this study is that by mining the trajectory data of wheat harvesters within 25 d of peak harvest period, 131 cross-regional trajectories were identified, consisting of 11 633 harvesters. Three main routes of wheat harvester cross-regional migration were identified, along with several smaller routes outside their range. The overall spatiotemporal pattern aligns with observed realities in China. This study can provide valuable references for operators to optimize cross-regional routes, for agricultural machinery manufacturers to develop location-based services, and for relevant government departments to formulate policies. Key words: trajectory data mining; cross-regional convoy; cross-regional agricultural machinery operations; wheat harvester; spatiotemporal patterns DOI: 10.25165/j.ijabe.20251806.9793 Citation: Zhai W X, Han R J, Pan J W, Wu C C. Wheat harvester convoys spatiotemporal patterns mining using a recursive search-based DBSCAN algorithm. Int J Agric & Biol Eng, 2025; 18(6): 221–229.

References

Zhang X B, Yang J, Thomas R. Mechanization outsourcing clusters and division of labor in Chinese agriculture. China Economic Review, 2017; 43: 184–195.

Wu C C, Li D, Zhang X Q, Pan J W, Quan L, Yang L L, et al. China’s agricultural machinery operation big data system. Computers and Electronics in Agriculture, 2023; 205: 107594.

Ma W Q, Liu T X, Li W Q, Yang H. The role of agricultural machinery in improving green grain productivity in China: Towards trans-regional operation and low-carbon practices. Heliyon, 2023; 9(10): e20279.

Pan J, Wu C, Zhai W. A hybrid genetic slime mould algorithm for parameter optimization of field-road trajectory segmentation models. Information Processing in Agriculture, 2024; 11(4): 590–602.

Zhai W, Zhang X, Liu J, et al. A dual-level interactive cascade network for agricultural machinery trajectory operation mode identification. Computers and Electronics in Agriculture, 2025; 238: 110788.

Zhai W, Xu Z, Pan J, et al. A general image classification model for agricultural machinery trajectory mode recognition. Computers and Electronics in Agriculture, 2024; 227: 109629.

Zhai W, Guo Z, Pan J, et al. Addressing local sparsity in massive agricultural machinery trajectories: A BiLSTM-GRU approach. Computers and Electronics in Agriculture, 2024; 226: 109376.

Zhai W, Kuang X, Cheng X, et al. Reconstruction of missing points in agricultural machinery trajectory based on bidirectional adjacent information. Computers and Electronics in Agriculture, 2024; 220: 108920.

Zhai W, Mo G, Xiao Y, et al. GAN-BiLSTM network for field-road classification on imbalanced GNSS recordings. Computers and Electronics in Agriculture, 2024; 216: 108457.

Zhang Z, Qi S, Zhang M. Spatial flow law of cross-regional operation of wheat combine harvesters in China. Transactions of the CSAE, 2021; 37(23): 19–27.

Li D, Liu X, Zhou K, Sun R Z, Wang C T, Zhai W X, et al. Discovering spatiotemporal characteristics of the trans-regional harvesting operation using big data of GNSS trajectories in China. Computers and Electronics in Agriculture, 2023; 211: 108003.

Li H H, Lam J S L, Yang Z L, Liu J X, Liu R W, Liang M H, et al. Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery. Transportation Research Part C-Emerging Technologies, 2022; 143: 103856.

Park S, Xu Y, Jiang L, Chen Z L, Huang S Y. Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data. Annals of Tourism Research, 2020; 84: 102973.

Xiao Y, He X, Yang C, Liu H, Liu Y. Dynamic graph computing: A method of finding companion vehicles from traffic streaming data. Information Sciences, 2022; 591: 128–141.

Zhang A S, Shi W Z, Liu Z W, Zhou X L, Geo-SigSPM: mining geographically interesting and significant sequential patterns from trajectories. International Journal of Geographical Information Science, 2024 Feb 2024, doi: 10.1080/13658816.2024.2320149

Gudmundsson J, Kreveld M V. Computing longest duration flocks in trajectory data. Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems, Arlington, Virginia, USA, 2006. Available: https://doi.org/10.1145/1183471.1183479.

Jeung H, Yiu M L, Zhou X, Jensen C S, Shen H T. Discovery of convoys in trajectory databases. Proc. VLDB Endow., 2008; 1(1): 1068–1080.

Li Z, Ding B, Han J, Kays R. Swarm: mining relaxed temporal moving object clusters. Proc. VLDB Endow., 2010; 3(1-2): 723–734.

Li Y X, Bailey J, Kulik L. Efficient mining of platoon patterns in trajectory databases. Data & Knowledge Engineering, Editorial Material, 2015; 100: 167–187.

Li K, Wang H Y, Chen Z W, Chen L S. Relaxed group pattern detection over massive-scale trajectories. Future Generation Computer Systems-the International Journal of Escience, 2023; 144: 131–139.

Liu Y Y, Dai H, Li J W, Chen Y, Yang G, Wang J. BP-Model-based convoy mining algorithms for moving objects. Expert Systems with Applications, 2022; 213: 118860.

Ansari M Y, Mainuddin, Ahmad A, Bhushan G. Spatiotemporal trajectory clustering: A clustering algorithm for spatiotemporal data. Expert Systems with Applications, 2021; 178: 115048.

Niu X Z, Zheng Y H, Fournier-Viger P, Wang B. Parallel grid-based density peak clustering of big trajectory data. Applied Intelligence, 2022; 52(15): 17042–17057.

Tang C H, Chen M Y, Zhao J H, Liu T, Liu K, Yan H Y, et al. A novel ship trajectory clustering method for Finding Overall and Local Features Of Ship Trajectories. Ocean Engineering, 2021; 241: 110108.

Park S, Yuan Y Q, Choe Y. Application of graph theory to mining the similarity of travel trajectories. Tourism Management, 2021; 87: 104391.

Zhou C H, Liu G Y, Huang L, Wen Y Q. Spatiotemporal companion pattern (STCP) mining of ships based on trajectory features. Journal of Marine Science and Engineering, 2023; 11(3): 528.

Dutta S, Das A, Patra B K. CLUSTMOSA: Clustering for GPS trajectory data based on multi-objective simulated annealing to develop mobility application. Applied Soft Computing, 2022; 130: 109655.

MOA. The national “three summer” large-scale wheat harvest basically ended. http://www.moa.gov.cn/xw/bmdt/202206/t20220623_6403139.htm Accessed on [2022-06-23].

Frey B J, Dueck D. Clustering by passing messages between data points. Science, 2007; 315(5814): 972–976.

Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002; 24(5): 603–619.

Beer A, Draganov A, Hohma E, Jahn P, Frey C M M, Assent I. Connecting the dots-density-connectivity distance unifies DBSCAN, k-center and spectral clustering. 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Long Beach, CA, 2023, Aug 06-10. pp.80–92. doi: 10.1145/3580305.3599283.

Campello R J G B, Moulavi D, Sander J. Density-based clustering based on hierarchical density estimates. In Pei J, Tseng V S, Cao L, Motoda H, Xu G, Eds. Advances in knowledge discovery and data mining, Springer Berlin Heidelberg, 2013; pp.160–172.

Jiang B, Yin J J. Ht-index for quantifying the fractal or scaling structure of geographic features. Annals of the Association of American Geographers, 2014; 104(3): 530–541.

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Published

2025-12-26

How to Cite

Zhai, W., Han, R., Pan, J., & Wu, C. (2025). Wheat harvester convoys spatiotemporal patterns mining using a recursive search-based DBSCAN algorithm. International Journal of Agricultural and Biological Engineering, 18(6), 221–229. https://doi.org/10.25165/ijabe.v18i6.9793

Issue

Vol. 18 No. 6 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

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