Development of 3‐D point cloud imaging and 2‐D image processing methods for high‐throughput phenotyping of perennial ryegrass spikes

Abstract Advances in technology have increased the adoption of high‐throughput phenotyping (HTP) methodologies, potentially replacing laborious and time‐consuming measurements data recording. One promising HTP tool for fine‐featured small‐sized characteristics is three‐dimensional (3‐D) scanning imaging system, but utility present two‐dimensional (2‐D) has not been fully explored this purpose. The objective work was to reduce assessment field evaluation time by developing 2‐D photogrammetric 3‐D point cloud methods spike perennial ryegrass ( Lolium perenne L.), with special attention traits that might be associated seed retention. These systems were compared direct capture hand on spikes 21 diverse global accessions ryegrass. Fiji (ImageJ) open‐source software used analysis structure, including length, spikelet number, internode curvature spike. An original approach nondestructive crop characterization developed using commercially available Artec Space Spider. This optical sensor generate dense clouds measure angle, Both found accurately characterize subject; method slower than more precise p ≤ 0.01) image a linear measurement deviation only 0.17%. effectively post‐processing analysis, Spider can directly support collection. non‐destructive system facilitates likely other applications.