Efficient imaging and computer vision detection of two cell shapes in young cotton fibers.

Premise: The shape of young cotton () fibers varies within and between commercial cotton species, as revealed by previous detailed analyses of one cultivar of and one of . Both narrow and wide fibers exist in cv. Deltapine 90, which may impact the quality of our most abundant renewable textile material. More efficient cellular phenotyping methods are needed to empower future research efforts.

Methods: We developed semi-automated imaging methods for young cotton fibers and a novel machine learning algorithm for the rapid detection of tapered (narrow) or hemisphere (wide) fibers in homogeneous or mixed populations.

Results: The new methods were accurate for diverse accessions of and and at least eight times more efficient than manual methods. Narrow fibers dominated in the three accessions analyzed, whereas the three accessions showed a mixture of tapered and hemisphere fibers in varying proportions.

Discussion: The use or adaptation of these improved methods will facilitate experiments with higher throughput to understand the biological factors controlling the variable shapes of young cotton fibers or other elongating single cells. This research also enables the exploration of links between early cell shape and mature cotton fiber quality in diverse field-grown cotton accessions.