A Highly Accurate Detection Platform for Potato Seedling Canopy in Intelligent Agriculture Based on Phased Array LiDAR Technology

Precision agriculture, rooted in the principles of intelligent agriculture, plays a pivotal role in fostering a sustainable, healthy, and eco-friendly economy. In order to promote the precision and intelligence of potato seedling management, an innovative platform designed using phased array LiDAR technology was used for precise and accurate detection of potato canopy height. The platform is intricately designed, featuring a suite of components that includes a high-precision rotary encoder, a reliable motor, a robust frame, an inclinometer for precise angle measurements, a computer for data processing, a height adjustment mechanism for adaptability, and an advanced LiDAR system. The LiDAR system is tasked with emitting pulses of laser light toward the canopy of the potato plants, which then scans the canopy to ascertain its height. The result of this scanning process is a rich, three-dimensional point cloud data map that provides a detailed representation of the entire experimental population of potato seedlings. Subsequently, a specialized algorithm for potato seedling canopy height was designed based on integrating the altitude of LiDAR’s installation, the precise measurements from the inclinometer sensor, and the meticulously conducted postprocessing of canopy height data. This algorithm meticulously accounts for a multitude of variables, thereby ensuring a high degree of precision and reliability in the assessment of the potato canopy’s dimensions. The minimum relative error between the measured values of the outdoor canopy height detection platform and the manually measured height is 3.67 ± 0.42%, and the maximum relative error is 8.36 ± 3.47%, respectively. The average relative error is between 3 and 9%, comfortably below the 10% benchmark, which meets the rigorous measurement standards. This platform can efficiently, automatically, and accurately scan the canopy information of potato plants, providing a reference for the automated detection of potato canopy height.

Authors

Institutions

Publication Details

Journal
Agriculture
Published
2024-08-15
DOI
https://doi.org/10.3390/agriculture14081369
Citations
2
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.41
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Highly Accurate Detection Platform for Potato Seedling Canopy in Intelligent Agriculture Based on Phased Array LiDAR Technology

Hewen Tan, Xingwei Yan, Zhaoqin Lv, Guizhi Mu et al.
2 citations
Agriculture
Remote Sensing and LiDAR Applications
0.41
article

A Highly Accurate Detection Platform for Potato Seedling Canopy in Intelligent Agriculture Based on Phased Array LiDAR Technology

Hewen Tan, Xingwei Yan, Zhaoqin Lv, Guizhi Mu, Peizhuang Wang, Qingqing Xin
article en
2 citations

Abstract

Precision agriculture, rooted in the principles of intelligent agriculture, plays a pivotal role in fostering a sustainable, healthy, and eco-friendly economy. In order to promote the precision and intelligence of potato seedling management, an innovative platform designed using phased array LiDAR technology was used for precise and accurate detection of potato canopy height. The platform is intricately designed, featuring a suite of components that includes a high-precision rotary encoder, a reliable motor, a robust frame, an inclinometer for precise angle measurements, a computer for data processing, a height adjustment mechanism for adaptability, and an advanced LiDAR system. The LiDAR system is tasked with emitting pulses of laser light toward the canopy of the potato plants, which then scans the canopy to ascertain its height. The result of this scanning process is a rich, three-dimensional point cloud data map that provides a detailed representation of the entire experimental population of potato seedlings. Subsequently, a specialized algorithm for potato seedling canopy height was designed based on integrating the altitude of LiDAR’s installation, the precise measurements from the inclinometer sensor, and the meticulously conducted postprocessing of canopy height data. This algorithm meticulously accounts for a multitude of variables, thereby ensuring a high degree of precision and reliability in the assessment of the potato canopy’s dimensions. The minimum relative error between the measured values of the outdoor canopy height detection platform and the manually measured height is 3.67 ± 0.42%, and the maximum relative error is 8.36 ± 3.47%, respectively. The average relative error is between 3 and 9%, comfortably below the 10% benchmark, which meets the rigorous measurement standards. This platform can efficiently, automatically, and accurately scan the canopy information of potato plants, providing a reference for the automated detection of potato canopy height.

AgricultureVol. 14(8)
Jilin University (CN), Yunnan Agricultural University (CN), Jilin Agricultural University (CN), Shandong Agricultural University (CN), Xi'an Jiaotong University (CN)
Zero hunger
Openalex Percentile: Top 42%
Remote Sensing and LiDAR Applications
0.41
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.