LiDARPheno - A Low-Cost LiDAR-Based 3D Scanning System for Leaf Morphological Trait Extraction

Area of Science:

• Agricultural Science
• Biotechnology
• Sensor Technology

Background:

• Global population growth necessitates enhanced food security and crop resilience.
• Plant phenotyping is crucial for crop development but faces limitations in throughput, cost, and accessibility.
• Existing phenotyping technologies often struggle to provide the detailed genotypic and phenotypic data needed for gene-environment interaction studies.

Purpose of the Study:

• To develop and validate a low-cost, accessible LiDAR-based platform for plant phenotyping.
• To assess the feasibility of using inexpensive LiDAR sensors for accurate leaf trait extraction (length, width, area).
• To address the bottleneck of high-throughput, cost-effective plant phenotyping for crop improvement.

Main Methods:

• Designed and implemented LiDARPheno, a low-cost LiDAR system using off-the-shelf components.
• Developed firmware for hardware control and Python scripts for data acquisition and analysis.
• Utilized publicly available libraries and APIs for ease of implementation by non-technical users.
• Applied data processing techniques including conversion, filtering, segmentation, and trait extraction from LiDAR data.

Main Results:

• Demonstrated the feasibility of extracting leaf traits using an inexpensive LiDAR sensor.
• Validated the LiDARPheno system through experiments on indoor and canola plants.
• Compared LiDARPheno's performance with a commercial 2D LiDAR (SICK LMS400) for trait extraction accuracy.

Conclusions:

• The developed LiDARPheno system offers a viable low-cost solution for plant phenotyping.
• This technology can significantly improve the efficiency and accessibility of leaf trait analysis.
• LiDARPheno has the potential to accelerate crop improvement research and contribute to global food security.