Fast extraction of navigation line and crop position based on LiDAR for cabbage crops
Year: 2025
Type: article
Abstract: This paper describes the design, algorithm development, and experimental verification of a precise spray perception system based on LiDAR were presented to address the issue that the navigation line extraction accuracy of self-propelled sprayers during field operations is low, resulting in wheels rolling over the ridges and excessive pesticide waste. A data processing framework was established for the precision spray perception system. Through data preprocessing, adaptive segmentation of crops and ditches, extraction of navigation lines and crop positioning, which were derived from the original LiDAR point cloud species. Data collection and analysis of the field environment of cabbages in different growth cycles were conducted to verify the stability of the precision spraying system. A controllable constant-speed experimental setup was established to compare the performance of LiDAR and depth camera in the same field environment. The experimental results show that at the self-propelled sprayer of speeds of 0.5 and 1 ms −1 , the maximum lateral error is 0.112 m in a cabbage ridge environment with inter-row weeds, with an mean absolute lateral error of 0.059 m. The processing speed per frame does not exceed 43 ms. Compared to the machine vision algorithm, this method reduces the average processing time by 122 ms. The proposed system demonstrates superior accuracy, processing time, and robustness in crop identification and navigation line extraction compared to the machine vision system. • Adaptive crop and ditch segmentation. • providing basis for developing precise spraying system in operation. • Achieving high-precision detection along with outstanding detection speed. • Effectively addressing curved crop rows under weed-covered conditions.
Authors: Jiang Pin, Tingfeng Guo, Minzi Xv, Xiangjun Zou, Wenwu Hu
Institution: Hunan 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
Funders: Hunan Provincial Science and Technology Department, Guangdong Science and Technology Department, Science and Technology Bureau, Changsha, Key Research and Development Program of Hunan Province of China, Key Technologies Research and Development Program, National Key Research and Development Program of China
OpenAlex ID: W4409204653