Feeding the world’s population of 9 billion by 2040 is one of the major challenges of the agriculture sector. Wheat (Triticum aestivum L.) is the second most important staple crop, with a global production of 773 million tonnes per year, but the expected yields need to increase by 60% to ensure future food security. Achieving this requires the development of new cultivars with heightened expression of yield-associated traits, such as radiation use efficiency (RUE), which is fundamental to enhancing plant performance and yield. Recently, plant architectural phenotypes involving leaf inclination angle have shown promising traits in improving RUE at the canopy level. Specifically, the erectophile leaf arrangement exhibits a higher yield potential, receiving more even light distribution than the planophile arrangement, which is susceptible to light saturation on the top layer. This study used a mobile robotic phenotyping system with 3D-multispectral laser scanners and hyperspectral cameras. In 2022 and 2023, data were collected from 100 spring wheat canopies at the heading/anthesis and booting/anthesis stages, respectively. Using 3D data, we estimated canopy tangency angles, identified two architectural phenotypes, and incorporated them into one-dimensional (1D) convolutional neural networks (CNN) to predict canopy-based RUE. Canopy architectural phenotypes in CNN models improved the prediction accuracy of RUE. These findings underscore the potential of canopy architectural traits derived from 3D images as a critical parameter for enhancing RUE predictions in wheat canopies. It could potentially be used in other cereal crops.

Global wheat production needs to increase by 60% to ensure food security in the future. Radiation use efficiency (RUE), defined as dry matter production per unit of light energy consumption, is an important trait that contributes to wheat yield potential. Traditionally, RUE is estimated through sequential biomass cuts evaluated against cumulative light interception, which is less precise and non-specific to genotypes. 3D models have recently been shown promise in estimating light interception when used along with ray tracing algorithms, mostly deployed in single plant-based models, while light interception at the canopy level remains to be explored. In this study, a mobile robotic phenotyping platform equipped with dual multispectral laser sensors was used to generate canopy 3D data. Using this platform, 100 spring wheat genotypes were scanned at heading stage to understand the genetic variation for RUE and its associated traits under field conditions. Ray-tracing algorithms were used to estimate the fraction of intercepted photosynthetically active radiation (FIPAR) for all genotypes, validated through a hand-held light ceptometer. Genotype-specific RUE was calculated as a slope between dry biomass and accumulated PAR. 3D model-based FIPAR was in close agreement with ceptometer-derived FIPAR. 3D model-derived RUE showed a large genetic variation across 100 wheat genotypes. It explained a higher variation in grain yield than ceptometer-derived RUE. These results indicate that canopy 3D models can be used as a rapid method for estimating canopy RUE in wheat, and potentially are extendable to other cereals.