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Caicong Wu
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

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Journal article
Published: 05 May 2021 in Computers and Electronics in Agriculture
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Field-road segmentation that automatically divides a trajectory into a sequence of field/road segments is an important component in segmentation process for the trajectories of agricultural machinery. A trajectory is a sequence of geospatial coordinates recorded by GNSS receivers during the driving of the machine. The objective of this paper is to develop a field-road segmentation method in the case of the unavailability of field boundary information. The developed method consists of two stages. The first stage uses DBSCAN, a typical clustering algorithm, to do field-road segmentation, and the second stage uses a rule-based inference to correct two types of false segmentation cases from output of DBSCAN-based clustering. Based on the parallel direction distribution that strips in the same field are almost parallel, two inference rules, Field2Road-Cluster and Road2Field-Segment are performed sequentially. Field2Road-Cluster uses the direction distribution difference (parallel in fields vs. not parallel on roads) to correct false field segmentation cases and Road2Field-Segment uses the parallel relationship among strips in the same field to correct false road segmentation cases. The developed method was validated by 60 selected trajectories. The results demonstrated that the rule-based inference achieved an increase of 7.95% in F1 scores, where Field2Road-Cluster and Road2Field-Segment contributed 6.40% and 1.55% increase, respectively.

ACS Style

Ying Chen; Xiaoqiang Zhang; Caicong Wu; Guangyuan Li. Field-road trajectory segmentation for agricultural machinery based on direction distribution. Computers and Electronics in Agriculture 2021, 186, 106180 .

AMA Style

Ying Chen, Xiaoqiang Zhang, Caicong Wu, Guangyuan Li. Field-road trajectory segmentation for agricultural machinery based on direction distribution. Computers and Electronics in Agriculture. 2021; 186 ():106180.

Chicago/Turabian Style

Ying Chen; Xiaoqiang Zhang; Caicong Wu; Guangyuan Li. 2021. "Field-road trajectory segmentation for agricultural machinery based on direction distribution." Computers and Electronics in Agriculture 186, no. : 106180.

Journal article
Published: 11 February 2020 in Energies
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In large-scale arable farming, multiple sequential operations involving multiple machines must be carried out simultaneously due to restrictions of short time windows. However, the coordination and planning of multiple sequential operations is a nontrivial task for farmers, since each operation may have its own set of operational features, e.g., operating width and turning radius. Taking the two sequential operations—hoeing cultivation and seeding—as an example, the seeder has double the width of the hoeing cultivator, and the seeder must remain idle while waiting for the hoeing cultivator to finish two rows before it can commence its seeding operation. A flow-shop working mode can coordinate multiple machines in multiple operations within a field when different operations have different implement widths. To this end, an auto-steering-based collaborative operating system for fleet management (FMCOS) was developed to realize an in-field flow-shop working mode, which is often adopted by the scaled agricultural machinery cooperatives. This paper proposes the structure and composition of the FMCOS, the method of operating strip segmenting, and a new algorithm for strip state updating between successive field operations under an optimal strategy for waiting time conditioning between sequential operations. A simulation model was developed to verify the state-updating algorithm. Then, the prototype system of FMCOS was combined with auto-steering systems on tractors, and the collaborative operating system for the server was integrated. Three field experiments of one operation, two operations, and three operations were carried out to verify the functionality and performance of FMCOS. The results of the experiment showed that the FMCOS could coordinate in-field fleet operations while improving both the job quality and the efficiency of fleet management by adopting the flow-shop working mode.

ACS Style

Caicong Wu; Zhibo Chen; Dongxu Wang; Bingbing Song; Yajie Liang; Lili Yang; Dionysis D. Bochtis. A Cloud-Based In-Field Fleet Coordination System for Multiple Operations. Energies 2020, 13, 775 .

AMA Style

Caicong Wu, Zhibo Chen, Dongxu Wang, Bingbing Song, Yajie Liang, Lili Yang, Dionysis D. Bochtis. A Cloud-Based In-Field Fleet Coordination System for Multiple Operations. Energies. 2020; 13 (4):775.

Chicago/Turabian Style

Caicong Wu; Zhibo Chen; Dongxu Wang; Bingbing Song; Yajie Liang; Lili Yang; Dionysis D. Bochtis. 2020. "A Cloud-Based In-Field Fleet Coordination System for Multiple Operations." Energies 13, no. 4: 775.

Journal article
Published: 12 September 2018 in IFAC-PapersOnLine
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Bad operating behaviors of the tractor or harvester operator are harmful to the agricultural machinery. Agricultural machinery cooperatives usually need to monitor the operating behaviors and to train the operators to improve the machinery health and operation quality. This paper proposes the smartphone based method to monitor and evaluate the operating habits of agricultural machinery. We developed an android APP to collect the information of smartphone sensors with 20 Hz sampling frequency. Then we constructed the operating behavior models including accelerating, operating, braking, idling, and throttle booming, considering their characteristics by using related smartphone sensors, such as GNSS, accelerometer, microphone, and so on. We conducted field experiments in the cooperative of Beijing. The results show that the data of the sensors can identify different operating behaviors. With the thresholds of related operating behavior of further research, we can provide the evaluation report of operating behaviors to the cooperative.

ACS Style

Zhihong Kou; Caicong Wu. Smartphone based operating behaviour modelling of agricultural machinery. IFAC-PapersOnLine 2018, 51, 521 -525.

AMA Style

Zhihong Kou, Caicong Wu. Smartphone based operating behaviour modelling of agricultural machinery. IFAC-PapersOnLine. 2018; 51 (17):521-525.

Chicago/Turabian Style

Zhihong Kou; Caicong Wu. 2018. "Smartphone based operating behaviour modelling of agricultural machinery." IFAC-PapersOnLine 51, no. 17: 521-525.

Journal article
Published: 12 September 2018 in IFAC-PapersOnLine
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Multi-machinery operating within a field is common in the scene of scaled farm machinery service, especially with soaring usage of automated steering system in large and medium machinery cooperatives. The object of this study is to explore a precise and efficient method to realize multi-machinery coordination, which equipped with GNSS based auto-steering system. The core of our method is to share guidance lines via the cloud. To evaluate our method, we developed a cloud-based prototype system to demonstrate the practicability and conducted experiments in Beijing. The result shows that the multi-machinery coordination system can further improve operating precision and production efficiency of the machinery fleet.

ACS Style

Zhu Yuting; Wang Jie; Wu Caicong. Cloud based Precise coordination system for multi-machinery of single-operation. IFAC-PapersOnLine 2018, 51, 626 -630.

AMA Style

Zhu Yuting, Wang Jie, Wu Caicong. Cloud based Precise coordination system for multi-machinery of single-operation. IFAC-PapersOnLine. 2018; 51 (17):626-630.

Chicago/Turabian Style

Zhu Yuting; Wang Jie; Wu Caicong. 2018. "Cloud based Precise coordination system for multi-machinery of single-operation." IFAC-PapersOnLine 51, no. 17: 626-630.