Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR
We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.
Code (1)
Tasks
Plant PhenotypingSimilar Papers 제목 키워드 기반
Field-Based Plot Extraction Using UAV RGB Images
Unmanned Aerial Vehicles (UAVs) have become popular for use in plant phenotyping of field based crops, such as maize and sorghum, due to their ability to acquire high resolution data over field trials. Field experiments,…
ManagementPlant PhenotypingAerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat
Crop production needs to increase in a sustainable manner to meet the growing global demand for food. To identify crop varieties with high yield potential, plant scientists and breeders evaluate the performance of hundre…
Crossmodal learning for Crop Canopy Trait Estimation
Recent advances in plant phenotyping have driven widespread adoption of multi sensor platforms for collecting crop canopy reflectance data. This includes the collection of heterogeneous data across multiple platforms, wi…
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model
Biomass estimation of oilseed rape is crucial for optimizing crop productivity and breeding strategies. While UAV-based imaging has advanced high-throughput phenotyping, current methods often rely on orthophoto images, w…
3DGS3D ReconstructionNeRFPoint Cloud SegmentationSPROUT: A Scalable Diffusion Foundation Model for Agricultural Vision
Image-based plant phenotyping depends on dense structural understanding of crops, yet pixel-level annotation remains expensive across species, organs, growth stages, and field conditions. General-purpose vision foundatio…