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

Abstract

Premise The shape of young cotton ( Gossypium ) fibers varies within and between commercial cotton species, as revealed by previous detailed analyses of one cultivar of G. hirsutum and one of G. barbadense . Both narrow and wide fibers exist in G. hirsutum 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 G. hirsutum and G. barbadense and at least eight times more efficient than manual methods. Narrow fibers dominated in the three G. barbadense accessions analyzed, whereas the three G. hirsutum 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.

Plant phenotyping relevance

若い綿繊維の形状を対象に、半自動イメージングと機械学習による細胞形状検出法を開発し、精度と効率を検証しているため、植物表現型取得法が中心である。

Code and data availability

The paper publicly releases its authors' analysis code/workflow on GitHub and the cotton fiber images of six accessions used for phenotyping on USDA Ag Data Commons, both explicitly stated in the Data Availability statement and Open Data badge sections.