Integrating 3D Sensing and Autonomous Robotics for Enhances Grassland Monitoring

Grasslands are challenging to characterize due to their homogeneous visual texture and diverse species composition, complicating automated data collection for precision livestock farming adoption. Current methods lack detailed spatial information, hindering effective grassland management. Recent advancements in industrial grade field sensing technologies and 3D data processing provide new opportunities for automated phenotyping. We propose an integrated pipeline for grassland phenotyping using an autonomous unmanned ground vehicle (UGV) powered by Robot Operating System 2 (ROS2). The UGV follows predefined waypoints using NAV2 to scan the grassland, capturing geo-referenced data. This platform is designed to be easy to replicate, deploy and use, allowing compatibility with a variety of sensors based on specific needs. It features IP67-certified components, USB and Ethernet connectivity for data transfer, and approximately 8 hours of battery life for active operation. Our current work focuses on combining RGB-D frames into a colorized point cloud using a precise positioning device and registration algorithms to assess sward height, LAI, biomass, and vegetation indices while integrating species composition. Future work will incorporate predictive modeling to assess climate and management impacts on grassland health. Additionally, 3D rendering will simulate virtual environments to optimize acquisition parameters before real data collection. A semantic segmentation pipeline will be developed using synthetic training data to spatially classify plant species and objects of interest. This approach integrates data acquisition, simulation, and predictive modeling to enhance grassland monitoring and management, enabling data-driven precision agriculture for sustainability and productivity