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ARS Home » Midwest Area » Urbana, Illinois » Global Change and Photosynthesis Research » Research » Publications at this Location » Publication #409978

Research Project: Enhancing Photosynthesis for Agricultural Resiliency and Sustainability

Location: Global Change and Photosynthesis Research

Title: Fast, nondestructive and precise biomass measurements are possible using lidar-based convex hull and voxelization algorithms

item Siebers, Matthew
item FU, PENG - Harrisburg University
item BLAKELY, BETHANY - University Of Illinois
item LONG, STEPHEN - University Of Illinois
item Bernacchi, Carl
item McGrath, Justin

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/14/2024
Publication Date: 6/17/2024
Citation: Siebers, M.H., Fu, P., Blakely, B.J., Long, S.P., Bernacchi, C.J., McGrath, J.M. 2024. Fast, nondestructive and precise biomass measurements are possible using lidar-based convex hull and voxelization algorithms. Remote Sensing. 16(12). Article 2191.

Interpretive Summary: Plant biomass is a primary consideration for many crops, especially potential bioenergy crops. Most techniques to measure biomass are destructive, greatly restricting the ability to gather data, which limits some modern techniques that use large amounts of data to find small signals in genetic information, such as with genome-wide association studies. Fast, nondestructive biomass estimate would allow for multiple measurements on a plot to be taken during a growing season, capturing information about a genotypes growth pattern, and allow for measurement of a larger number of genotypes. Here we present algorithms that use 3D lidar scans to estimate biomass nondestructively. by providing two different measures of canopy volume: convex hull volume and voxel count. Both estimate correlate well with hand-harvested estimates of biomass and offer precision as good as or better than hand harvesting or a mechanical harvester. A plot can be scanned in fewer than 10 seconds, and data processing of a scan takes only a few seconds. These techniques could offer data useful for screening genotypes for genes associated with greater biomass, accelerating progress compared to using only end-of-season yield.

Technical Abstract: Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.