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

Abstract

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

Plant phenotyping relevance

イネ科植物の穂形質を対象に、2D画像処理および3D点群イメージング手法を開発し、手測定と比較検証しているため、表現型取得法が研究の中心です。

Code and data availability

The paper describes custom Python curvature code and Fiji workflows, but these are only referenced as 'Supporting Information' with no public repository, deposit, or authors' URL. No public phenotype datasets, images, point clouds, or code assets are identified in the supplied blocks.

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