New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

Crop phenotyping is a major bottleneck in current plant research. Field-based high-throughput phenotyping platforms are an important prerequisite to advance crop breeding. We developed a cable-suspended field phenotyping platform covering an area of ~1 ha. The system operates from 2 to 5 m above the canopy, enabling a high image resolution. It can carry payloads of up to 12 kg and can be operated under adverse weather conditions. This ensures regular measurements throughout the growing period even during cold, windy and moist conditions. Multiple sensors capture the reflectance spectrum, temperature, height or architecture of the canopy. Monitoring from early development to maturity at high temporal resolution allows the determination of dynamic traits and their correlation to environmental conditions throughout the entire season. We demonstrate the capabilities of the system with respect to monitoring canopy cover, canopy height and traits related to thermal and multi-spectral imaging by selected examples from winter wheat, maize and soybean. The system is discussed in the context of other, recently established field phenotyping approaches; such as ground-operating or aerial vehicles, which impose traffic on the field or require a higher distance to the canopy.

The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had little effect. Visually scored delayed senescence was positively correlated with GY ranging from r = 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.