An autonomous navigation method for field phenotyping robot based on ground-air collaboration
Year: 2025
Type: article
Abstract: High-throughput phenotyping technology is important in affecting the efficiency of crop breeding. This study introduces a novel autonomous navigation method for phenotyping robots that leverages ground-space synergy to meet the demands of unmanned crop phenotypic data collection. The proposed method employs a UAV equipped with a Real-Time Kinematic (RTK) module for the construction of high-precision crop maps. It utilizes Paddle-Liteseg and SegFormor-B0 semantic segmentation models to detect crop rows, extracts key coordinate points of these rows, and generates navigation paths for the phenotyping robots by mapping these points to actual geographic coordinates. Furthermore, an adaptive controller based on the Pure Pursuit algorithm is proposed, which dynamically adjusts the moving speed and steering angle of the phenotyping robot in real-time, according to the distance (d), angular deviation (α) and the lateral deviation (
Authors: Zikang Zhang, Zhengda Li, Meng Yang, Jiale Cui, Yang Shao, Youchun Ding, Wanneng Yang, Wen Qiao, Peng Song
Institution: Huazhong Agricultural University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,100
Awards: G5453745338, G5756018602, G6125655272
OpenAlex ID: W4410882002