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Mapping field environments into point clouds using a 3D LIDAR has the abilityto become a new approach for online estimation of crop biomass in the field.The estimation of crop biomass in agriculture is expected to be closelycorrelated to canopy heights. The work presented in this paper contributes tothe mapping and textual analysis of agricultural fields. Crop and environmentalstate information can be used to tailor treatments to the specific site. Thispaper presents the current results with our ground vehicle LiDAR mappingsystems for broad acre crop fields. The proposed vehicle system and methodfacilitates LiDAR recordings in an experimental winter wheat field. LiDAR dataare combined with data from Global Navigation Satellite System (GNSS) andInertial Measurement Unit (IMU) sensors to conduct environment mapping forpoint clouds. The sensory data from the vehicle are recorded, mapped, andanalyzed using the functionalities of the Robot Operating System (ROS) and thePoint Cloud Library (PCL). In this experiment winter wheat (Triticum aestivumL.) in field plots, was mapped using 3D point clouds with a point density onthe centimeter level. The purpose of the experiment was to create 3D LiDARpoint-clouds of the field plots enabling canopy volume and textural analysis todiscriminate different crop treatments. Estimated crop volumes ranging from3500-6200 (m3) per hectare are correlated to the manually collected samples ofcut biomass extracted from the experimental field.

Ground vehicle mapping of fields using LiDAR to enable prediction of crop biomass