Methods and experiments for collecting information and constructing models of bottom-layer contours in paddy fields

Tu, Tuanpeng; He, Jie; Luo, Xiwen; Hu, Lian; Wang, Pei; Chen, Gaolong; Li, Tian; Feng, Dawen; Wang, Zhimin; Man, Zhongxian; Li, Weicong; Wei, Zhenghui; Peng, Jingyi; Yi, Yanling; Wu, Peitian

doi:10.1016/j.compag.2023.107719

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The spatial position point set for the contact between the rear wheel bottom and the hard bottom layer of the agricultural machine was obtained using a coordinate conversion of the vehicle body's position and attitude information, which can realize the direct and continuous acquisition of a paddy field's hard bottom layer contour information through the paddy field water layer and tillage layer. Moreover, the accuracy of the hard bottom layer contour measurement can reach 1.55 cm [20]. The hard substrate contour sensing platform used in patty fields is shown in Figure 1.…”

Section: Hard Bottom Contour Sensing Platformmentioning

confidence: 99%

“…Starek et al used the kriging spatial interpolation method for wide-area terrain prediction and modeling based on a high-density, laser-scanned point cloud obtained over the total area of an experimental station [19]. Tuanpeng Tu et al combined GNSS and attitude and heading reference system (AHRS) technologies to achieve hard substrate contour acquisition and digital representations of paddy fields using an unmanned direct rice seeding machine chassis with an elevation standard error of less than 2 cm [20].…”

Section: Introductionmentioning

confidence: 99%

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Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields

Luo

et al. 2023

Agronomy

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The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines.

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Section: Hard Bottom Contour Sensing Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields

Luo

et al. 2023

Agronomy

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A review of global precision land-leveling technologies and implements: Current status, challenges and future trends

Chen,

Hu,

Luo

et al. 2024

Computers and Electronics in Agriculture

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Method and Experiment for Quantifying Local Features of Hard Bottom Contours of Intelligent Farm Machinery Driving in Paddy Fields

Tu¹,

Hu²,

Luo³

et al. 2023

Preprint

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The hard bottom layer of paddy field has a great influence on the driving stability and operation quality and efficiency of intelligent farm machinery, and the continuous improvement of unmanned precision operation accuracy and operation efficiency of paddy field operation machin-ery is the support to realize unmanned rice farm. In this paper, in view of the complicated hard bottom layer situation of unmanned operation farm machinery driving is difficult to realize to quantify the local characteristics of hard bottom layer of paddy field, the unmanned rice direct seeding machine chassis is used to operate the operation field and collect the hard bottom layer information simultaneously, and the data processing method of automatic calibration of sensor installation error, abnormal value rejection and 3D sample curve denoising of contour trajectory is designed; a hard bottom layer surface profile evaluation method based on the local sliding surface roughness is proposed. The local characteristics of the hard bottom layer were quantified, and the quantified results of the local characteristics of the hard bottom layer in the test plots showed that the mean value of the local roughness was 0.0065, 68.27% was distributed in the variation range of 0.0042~0.0087, and 99.73% was distributed in the variation range of 0~0.0133. Based on the test field data, the surface roughness features are verified to describe the variability of representative working conditions such as transport, downfield, operation and trapping of unmanned operation of intelligent farm machinery. The method of quantifying the hard-bottom local features of farm machinery driving can provide feedback on the local environmental features of intelligent farm machinery driving at the current position, and provide a reference basis for the design optimization of unmanned system for improving the quality of intelligent farm machinery operation.

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Methods and experiments for collecting information and constructing models of bottom-layer contours in paddy fields

Cited by 3 publications

References 13 publications

Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields

Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields

A review of global precision land-leveling technologies and implements: Current status, challenges and future trends

Method and Experiment for Quantifying Local Features of Hard Bottom Contours of Intelligent Farm Machinery Driving in Paddy Fields

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