Field-scale robotic phenotyping of three-dimensional wheat canopy architectural traits

Abstract

Assessing tools for rapid evaluation of new cultivars across large fields is essential for improving crop yields and ensuring future food security. Current phenotyping approaches remain labor-intensive and imprecise at the field scale, particularly for traits determining light interception and the light extinction coefficient ( K ). While robotic phenotyping and three-dimensional (3D) models have gained interest in estimating light interception in plant canopies, primarily at the single-plant scale or using single-plant-derived virtual canopies, applications at the field-scale canopy level remain limited. In this study, a semi-automated robotic phenotyping platform, PhenoLinc, was deployed to obtain canopy-level multispectral 3D data across 200 diverse wheat genotypes grown under field conditions over two years. 3D canopy architecture revealed substantial genotype-specific variation in inclination angle and K , which influenced radiation use efficiency (RUE), contrasting with the constant K commonly assumed in conventional approaches. This architectural variation was classified as two distinct architectural phenotypes, erectophiles (median angle 65°) and planophiles (59°) at pre-anthesis stages, which converged toward being homogenous by the anthesis stage. Erectophile phenotypes exhibited higher RUE (33.1%) than planophile phenotypes, leading to a higher yield. In yield prediction analyses, 3D-derived architectural traits provided comparable predictive performance to conventional measurements, however, architectural phenotype information reduced prediction error. Together, these findings highlight the value of field-scale robotic canopy phenotyping for characterizing genotype-specific canopy architectural traits and their relationship with yield.

Plant phenotyping relevance

フィールド規模のロボット型3D・マルチスペクトル計測プラットフォームを用いて、コムギ群落の建築形質を取得・評価することが研究の中心である。

Code and data availability

The article describes field-scale robotic 3D canopy phenotyping of wheat with custom Python processing scripts, but no public deposit of phenotype datasets, 3D point clouds, images, or analysis code is stated in the supplied blocks. The supplementary material link is generic and not explicitly stated to contain data or

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