Access to increasing amounts of high-quality DNA sequence data for many plant species is allowing for faster, more accurate gene identification. To maximize the use of this sequence data for gene identification and validation, it must be coupled to phenotype data.

However, phenotype acquisition can present a bottleneck in studies requiring many datapoints, such as diversity panels for genome-wide association studies.

Here we developed a handheld device—the Tricocam—and method for image capture and semi-automatic quantification of leaf edge trichomes in grass species of the Poaceae.

Trichomes have been implicated in abiotic and biotic stress tolerance in grasses. 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 in Poaceae diversity panels.

In making the Tricocam 3D print design and AI visual detection model public, we hope to deliver useful resources for the plant science community to use or adapt for other large-scale phenotyping projects.