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Volume 13
Issue 7
10.3390/agronomy13071949
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Article

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

by
Tuanpeng Tu
1,2,
Lian Hu
1,2,
Xiwen Luo
1,2,
Jie He
1,2,*,
Pei Wang
1,2,
Li Tian
1,2,
Gaolong Chen
1,2,
Zhongxian Man
1,2,
Dawen Feng
1,2,
Weirui Cen
1,2,
Mingjin Li
1,2,
Yuxuan Liu
1,2,
Kang Hou
1,2,
Le Zi
1,2,
Mengdong Yue
1,2 and
Yuqin Li
1,2
1
Key Laboratory of the Ministry of Education of China for Key Technologies for Agricultural Machinery and Equipment for Southern China, South China Agricultural University, Guangzhou 510642, China
2
Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(7), 1949; https://doi.org/10.3390/agronomy13071949
Submission received: 29 June 2023 / Revised: 19 July 2023 / Accepted: 22 July 2023 / Published: 23 July 2023
(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)
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Abstract

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.
Keywords: hard bottom layer; surface profile features; local roughness; unmanned farms; smart farming machines hard bottom layer; surface profile features; local roughness; unmanned farms; smart farming machines

Share and Cite

MDPI and ACS Style

Tu, T.; Hu, L.; Luo, X.; He, J.; Wang, P.; Tian, L.; Chen, G.; Man, Z.; Feng, D.; Cen, W.; et al. Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields. Agronomy 2023, 13, 1949. https://doi.org/10.3390/agronomy13071949

AMA Style

Tu T, Hu L, Luo X, He J, Wang P, Tian L, Chen G, Man Z, Feng D, Cen W, et al. Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields. Agronomy. 2023; 13(7):1949. https://doi.org/10.3390/agronomy13071949

Chicago/Turabian Style

Tu, Tuanpeng, Lian Hu, Xiwen Luo, Jie He, Pei Wang, Li Tian, Gaolong Chen, Zhongxian Man, Dawen Feng, Weirui Cen, and et al. 2023. "Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields" Agronomy 13, no. 7: 1949. https://doi.org/10.3390/agronomy13071949

APA Style

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. (2023). Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields. Agronomy, 13(7), 1949. https://doi.org/10.3390/agronomy13071949

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