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

Ear density ( ) and ear surface area 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 . 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. is finally derived as the ratio between EAI and . We applied the methodology to a panel of 10 commercial bread wheat varieties how both traits vary across 12 environments. The relative error obtained for awnless varieties is 12% (56 ears m -2 ) for and 18% (1.3 cm 2 ) for . For awned varieties, ground-truth observations of were shown to be biased due to an overestimation of awns contribution, leading to an error of 41% (3.6 cm 2 ). 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 was strongly correlated with grain yield ( r 2 = 0.83). These results indicate that both EAI and can be interesting non-destructive proxies for yield and grain dry mass per ear.