Automation of Rice Transplanter Using Agricultural Navigation

Rice is the predominant grain crop in China, with its consumption showing a steady annual increase. Due to the diminishing labor force, China’s rice cultivation industry faces significant challenges and has an urgent requirement for automated rice transplanters. This study developed an agricultural navigation system integrating mechatronic-hydraulic control with navigation technologies to automate the rice transplanter’s driving and operational processes. The designed automation devices enable precise control over functions such as steering and working clutch. A path planning methodology was proposed to generate straight-line reference paths by giving target points and to determine the headland turning pattern based on the working width and turning radius of the rice transplanter. Additionally, an operational control strategy based on the finite state machine (FSM) was developed, enabling effective switching of the rice transplanter’s operational states through the designation of key points. The test results showed that the maximum lateral error of the rice transplanter along straight-line paths was 4.83 cm on the cement pavement and 6.30 cm in the field, with the maximum error in determining key points being 7.22 cm in the field. These results indicate that the agricultural navigation system developed in this study can achieve the automation of rice transplanters and provide certain inspiration for the research of autonomous agricultural vehicles.

Authors

Institutions

Publication Details

Journal
Agriculture
Published
2025-05-23
DOI
https://doi.org/10.3390/agriculture15111125
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
1.28

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automation of Rice Transplanter Using Agricultural Navigation

Jianyu Zhu, Xiang Yin, Yufei Liu, Juan Du et al.
1 citations
Agriculture
Robotic Path Planning Algorithms
1.28
article

Automation of Rice Transplanter Using Agricultural Navigation

Jianyu Zhu, Xiang Yin, Yufei Liu, Juan Du, Zhidong Zhong, Yifan Yao
article en
1 citations

Abstract

Rice is the predominant grain crop in China, with its consumption showing a steady annual increase. Due to the diminishing labor force, China’s rice cultivation industry faces significant challenges and has an urgent requirement for automated rice transplanters. This study developed an agricultural navigation system integrating mechatronic-hydraulic control with navigation technologies to automate the rice transplanter’s driving and operational processes. The designed automation devices enable precise control over functions such as steering and working clutch. A path planning methodology was proposed to generate straight-line reference paths by giving target points and to determine the headland turning pattern based on the working width and turning radius of the rice transplanter. Additionally, an operational control strategy based on the finite state machine (FSM) was developed, enabling effective switching of the rice transplanter’s operational states through the designation of key points. The test results showed that the maximum lateral error of the rice transplanter along straight-line paths was 4.83 cm on the cement pavement and 6.30 cm in the field, with the maximum error in determining key points being 7.22 cm in the field. These results indicate that the agricultural navigation system developed in this study can achieve the automation of rice transplanters and provide certain inspiration for the research of autonomous agricultural vehicles.

AgricultureVol. 15(11)
Shandong University of Technology (CN), Zhejiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 20%
Robotic Path Planning Algorithms
1.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.