Enabling high-throughput image-based phenotyping

Nathan Miller, a scientist in the Spalding Lab at the University of Wisconsin–Madison's Department of Botany and Center for High Throughput Computing, works closely with CyVerse as he writes computer code to develop software programs that analyze images and videos of plants to determine plant phenotype, the measurable physical characteristics of plants.

A key aspect of CyVerse infrastructure, in the eyes of its designers, is its flexibility – the extent to which community members can mix and match various CyVerse resources to suit their development, research, or educational agenda.

"Many aspects of modern phenotyping are now image-based," Miller said. "Computers are more objective than humans, and also faster. Computers can measure with a precision that a human cannot achieve."

Geneticists, agronomists, and botanists have found digital imaging a useful tool to provide detailed data on the measurable proportions of seeds, root structures, the shape of plants as they grow, and plant parts after they've been harvested.

Such data can be used to identify plant varieties exhibiting traits that are preferable for agriculture, such as larger fruits, more robust stems, or more efficient root systems. "Humans were selecting for preferable phenotypic traits long before they were aware that they were modifying the genetics by doing so," Miller noted.

Now, modern technology has taken the Mendelian science of selectively breeding the best vegetables to the next level, in which every minute detail of a plant's physical makeup is precisely recorded to identify and select for desirable traits.

Read more at phys.org