2022 journal article

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

Applications in Plant Sciences.

author keywords: cotton diversity; fiber morphogenesis; Gossypium barbadense; Gossypium hirsutum; light microscopy; machine learning
TL;DR: Improved semi‐automated imaging methods for young cotton fibers and a novel machine learning algorithm for the rapid detection of tapered or hemisphere fibers in homogeneous or mixed populations are developed. (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (Web of Science)
13. Climate Action (Web of Science)
Source: ORCID
Added: November 28, 2022

AbstractPremiseThe 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.MethodsWe 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.ResultsThe 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.DiscussionThe 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.