High-throughput phenotyping of wheat ear surface area and ear density in the field

Ear density ( $D_e$ ) and ear surface area $\left(S_e\right)$ in cereals are important traits for adaptation to low inputs and climate change. Here we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot. First, the YOLOv5 ear detection algorithm is applied to nadir RGB images to estimate $D_e$ . Second, an ear segmentation algorithm is applied to nadir and 45° RGB images to compute the ear gap fraction at different viewing angles. The Beer-Lambert law is then inverted to compute the ear area index (EAI) from the observed ear gap fraction. $S_e$ is finally derived as the ratio between EAI and $D_e$ . We applied the methodology to a panel of 10 commercial bread wheat varieties to analyse how both traits vary across 12 environments. The relative error obtained for awnless varieties is 12% (56 ears m^-2) for $D_e$ and 18% (1.3 cm²) for $S_e$ . For awned varieties, ground-truth observations of $S_e$ were shown to be biased due to an overestimation of awns contribution, leading to an error of 41% (3.6 cm²). $S_e$ was strongly correlated with grain dry mass per ear at harvest (r ² = 0.80 across genotypes and environments, r ² per genotype ranged between 0.80 and 0.95) and $EAI$ was strongly correlated with grain yield (r ² = 0.83). These results indicate that both EAI and $S_e$ can be interesting non-destructive proxies for yield and grain dry mass per ear.