Abstract
This paper focuses on the development of a miniaturized mobile mapping platform with advantages over current agricultural phenotyping systems in terms of acquiring data that facilitate under-canopy plant trait extraction. The system is based on an unmanned ground vehicle (UGV) for in-row, under-canopy data acquisition to deliver accurately georeferenced 2D and 3D products. The paper addresses three main aspects pertaining to the UGV development: (a) architecture of the UGV mobile mapping system (MMS), (b) quality assessment of acquired data in terms of georeferencing information as well as derived 3D point cloud, and (c) ability to derive phenotypic plant traits using data acquired by the UGV MMS. The experimental results from this study demonstrate the ability of the UGV MMS to acquire dense and accurate data over agricultural fields that would facilitate highly accurate plant phenotyping (better than above-canopy platforms such as unmanned aerial systems and high-clearance tractors). Plant centers and plant count with an accuracy in the 90% range have been achieved.
Highlights
The ever-increasing human population stipulates for a sustained supply of staple crops.Compared to 2005, it is forecast that the global crop demand will increase at least 100%by 2050 [1]
5, the approaches adopted for quality assessment of the derived trajectory tion 5, the approaches adopted for quality assessment of the derived trajectory and point and point cloud from GNSS/INS and light detection and ranging (LiDAR) units, respectively, are introduced along cloud from GNSS/INS and LiDAR units, respectively, are introduced along with experiwith experimental results to validate the claims of this research
The raw GNSS/INS data collected from these experiments were imported for tightly coupled (TC) processing in Inertial Explorer
Summary
The ever-increasing human population stipulates for a sustained supply of staple crops. Spectral, and temporal resolution of modern remote sensing platforms, they still struggle in providing key phenotypic traits, such as stalk diameter, leaf angles, ear heights, silking stages, and leaf area index (LAI), with high accuracy. This lack is mainly attributed to the following limitations: Remote Sens. Plant Trait Derivation: The last stage of this research is to demonstrate the potential of high-resolution, under-canopy data for deriving plant traits beyond plant height, such as plant location and plant count These traits are visualized in 2D imagery as well as 3D point clouds.
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