Field Ridge Segmentation and Navigation Line Coordinate Extraction of Paddy Field Images Based on Machine Vision Fused with GNSS

Muhua Liu, Xulong Wu, Peng Fang, Wenyu Zhang, Xiongfei Chen, Runmao Zhao and Zhaopeng Liu ()
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Muhua Liu: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Xulong Wu: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Peng Fang: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Wenyu Zhang: Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education of the People’s Republic of China, South China Agricultural University, Guangzhou 510642, China
Xiongfei Chen: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Runmao Zhao: Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education of the People’s Republic of China, South China Agricultural University, Guangzhou 510642, China
Zhaopeng Liu: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China

Agriculture, 2025, vol. 15, issue 6, 1-28

Abstract: Farmland boundaries distinguish agricultural areas from non-agricultural areas, providing limits for field operations and navigation paths of agricultural machinery. However, in hilly regions, the irregularity of paddy field boundaries complicates the extraction of boundary information, hindering the widespread use of GNSS-based navigation systems in agricultural machinery. This paper focuses on the paddy field border prior to rice planting and utilizes machine vision and GNSS fusion technology to extract navigation line coordinates. First, the BiSeNet semantic segmentation network was employed to extract paddy field ridges. Second, the camera’s 3D attitude was obtained in real time using an Attitude and Heading Reference System (AHRS). A method and device based on the hydraulic profiling system were proposed to measure the camera’s height relative to the paddy field, providing a dynamic external reference. An improved inverse perspective transformation was applied to generate a bird’s-eye view of the paddy field ridges. Finally, a homogeneous coordinate transformation method was used to extract the navigation line coordinates, with the model and algorithms deployed on the Jetson AGX Xavier platform Field tests demonstrated a real-time segmentation speed of 26.31 fps, pixel segmentation accuracy of 92.43%, and an average intersection ratio of 90.62%. The average distance error of the extracted navigation line was 0.071 m, with a standard deviation of 0.039 m. The coordinate extraction took approximately 100 ms, meeting the accuracy and real-time requirements for navigation line extraction at the rice transplanter’s speed of 0.7 m s −1 , providing path information for subsequent autonomous navigation.

Keywords: paddy field ridges; machine vision; GNSS; navigation line coordinate extraction; inverse perspective transformation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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