Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Abstract

Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.

Plant phenotyping relevance

圃場のSfM画像から3D植物構造を再構成し、個体分割と複数形質の自動抽出を行うパイプラインを開発・検証しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The paper describes SFM-based sugar beet phenotyping with an automatic pipeline, but no public phenotype dataset, image set, point clouds, or author code repository is mentioned. The only URL (CloudCompare) is a generic third-party tool, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.