Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Tuanpeng Tu
,
Lian Hu
ORCID logo ,
Xiwen Luo
,
Jie He
* ORCID logo ,
Pei Wang
,
Li Tian
,
Gaolong Chen
,
Zhongxian Man
,
Dawen Feng
ORCID logo ,
Weirui Cen
,
Mingjin Li
,
Yuxuan Liu
,
Kang Hou
,
Le Zi
,
Mengdong Yue
,
Yuqin Li
Version 1 : Received: 1 July 2023 / Approved: 3 July 2023 / Online: 4 July 2023 (02:07:02 CEST)

A peer-reviewed article of this Preprint also exists.

Tu, T.; Hu, L.; Luo, X.; He, J.; Wang, P.; Tian, L.; Chen, G.; Man, Z.; Feng, D.; Cen, W.; Li, M.; Liu, Y.; Hou, K.; Zi, L.; Yue, M.; Li, Y. Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields. Agronomy 2023, 13, 1949. Tu, T.; Hu, L.; Luo, X.; He, J.; Wang, P.; Tian, L.; Chen, G.; Man, Z.; Feng, D.; Cen, W.; Li, M.; Liu, Y.; Hou, K.; Zi, L.; Yue, M.; Li, Y. Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields. Agronomy 2023, 13, 1949.

Abstract

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.

Keywords

Hard bottom layer; Surface profile features; Local roughness; Unmanned farms; Smart farming machines

Subject

Engineering, Other

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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