Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR

Area of Science:

• Agricultural Engineering
• Robotics
• Remote Sensing

Background:

• Accurate crop evaluation in early development stages is vital for predicting yield.
• Terrestrial laser scanning platforms generate point clouds for crop phenotyping but are often costly and complex.
• There is a need for accessible, low-cost systems for 3D crop mapping and analysis.

Purpose of the Study:

• To develop and validate a low-cost 3D reconstruction mapping system for orchards and small crops.
• To enable individual plant characterization, including height and canopy radius, for growth monitoring.
• To create a 3D crop map for visualizing canopy density and individual plant metrics.

Main Methods:

• Integration of an Unmanned Ground Vehicle (UGV) with a 2D LiDAR sensor to create 3D point clouds.
• Development of a sensing system encompassing mechanical, electronic, control, and software components.
• Validation using a citrus crop section, comparing LiDAR-derived data with photogrammetry reference values.

Main Results:

• A functional 3D crop map was generated, illustrating tree canopy density.
• Individual plant height and crown diameter were determined using a Python-assisted tool.
• Root mean square errors of approximately 30.8 cm for height and 45.7 cm for diameter were achieved compared to reference data.

Conclusions:

• The developed low-cost UGV-LiDAR system effectively generates 3D point clouds for crop phenotyping.
• The system provides a viable alternative for detailed plant morphological index determination in orchards.
• This technology supports precise monitoring of plant growth and canopy characteristics.