In-field whole plant maize architecture characterized by Latent Space Phenotyping

Abstract

Collecting useful, interpretable, and biologically relevant phenotypes in a resource-efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable sub-canopy rovers are an efficient and scalable way to generate sensor-based datasets of in-field crop plants. Rovers equipped with light detection and ranging (LiDar) can produce three-dimensional reconstructions of entire hybrid maize fields. In this study, we collected 2,103 LiDar scans of hybrid maize field plots and extracted phenotypic data from them by Latent Space Phenotyping (LSP). We performed LSP by two methods, principal component analysis (PCA) and a convolutional autoencoder, to extract meaningful, quantitative Latent Space Phenotypes (LSPs) describing whole-plant architecture and biomass distribution. The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression, indicating the LSPs contain biologically relevant information about plant architecture. These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.

Plant phenotyping relevance

LiDARによる圃場全植物の3次元計測と、PCA・畳み込みオートエンコーダによる形態・バイオマス形質抽出が研究の中心であり、育種利用可能性も検証している。

Code and data availability

The paper's Data availability statement points to public Bitbucket repositories under bucklerlab containing the authors' analysis code, phenotypic data, and the trained autoencoder model (HDF5). The raw LiDar point clouds are only 'DOI in preparation at CyVerse' and thus not yet actionable.