Abstract
Conventional cotton (
Gossypium
spp.) variety development requires 8–12 years - a timeline structurally incompatible with the accelerating urgency of climate-change adaptation, soil salinisation, and textile-industry demands for novel varieties within 3–5 years. Speed breeding (SB) compresses the generation interval by 30–80% through synergistic optimisation of LED spectra, temperature, nutrition, and immature embryo rescue (IER). However, SB alone is a temporal accelerator, not a breeding system: its transformative potential is realised only when it functions as the central engine of a genomics-assisted speed breeding (GASB) platform that integrates molecular marker-assisted selection (MAS), genomic selection (GS), CRISPR and New Genomic Technique (NGT) editing, high-throughput phenotyping (HTP), and AI-assisted decision-support tools. Building on previous reviews of cotton speed breeding and genomics-assisted breeding, the present review provides a mechanistic and implementation-oriented synthesis of these complementary technologies within a unified GASB framework. We evaluate: (i) the molecular architecture of photoperiodic flowering in predominantly
Gossypium hirsutum
, together with currently available evidence from other
Gossypium
species where appropriate and its manipulation through LED protocol design and CRISPR targeting of flowering suppressors; (ii) the mechanistic basis of multi-channel LED recipe design from SB 1.0 to SB 3.0; (iii) stage-specific thermal management including the operationally critical 33°C pollen safety ceiling; (iv) nutritional physiology, phytohormones, and biochemical monitoring; (v)
in vitro
biotechnologies—including callus culture, microspore-derived doubled haploids, and immature embryo rescue (IER)—together with the operational coordination of immature embryo rescue and pollen-stage temperature management within speed-breeding facilities; (vi) high-throughput phenotyping technologies experimentally validated in cotton, encompassing five cotton-specific applications reviewed in a dedicated section; and (vii) AI-assisted phenotyping and digital twin architectures for real-time decision support within the GASB pipeline. Critical analysis identifies allotetraploid genome redundancy, genotype × light environment (G×L) interaction with landrace germplasm, IER scalability, bioinformatics infrastructure gaps, SB-to-field correlation uncertainties, and SVT regulatory requirements as the primary barriers to widespread GASB deployment. A structured 2025–2035 roadmap with three time-horizon priorities and measurable KPIs is presented.