A high-throughput pipeline for phenotyping, object detection and quantification of leaf trichomes

We developed a new high-throughput device and AI image detection model capable of rapidly collecting phenotype data for a population of wild grass, facilitating identification of genomic regions associated with trichome density. Access to increasing amounts of high-quality genomic sequence data for many plant species is allowing for faster, more accurate gene identification. To maximize the use of this sequence data for association genetics, gene discovery, and validation, it must be coupled with phenotype data. However, phenotype data acquisition can represent a bottleneck in studies requiring many datapoints, such as large diversity panels for genome-wide association studies. Here we developed a portable handheld imaging device-the Tricocam-and method for image capture and semi-automated quantification of leaf edge trichomes in a grass species. Trichomes have been implicated in abiotic and biotic stress tolerance in grasses, but so far, no trichome genes have been cloned in this plant family. We also refined and implemented the AI detection processes underpinning the web-based image quantification platform from Thya Technology® to rapidly quantify leaf edge trichomes. We used the phenotype acquisition method in the wild wheat progenitor Aegilops tauschii in combination with k-mer-based Genome-Wide Association Study to validate a trichome-controlling genomic region on chromosome arm 4DL and discover a new one on 4DS. By making the Tricocam 3D print design and AI visual detection model public, we aim to deliver useful resources for the plant science community to use or adapt for other large-scale phenotyping projects on diversity panels.