Cotton fibers (produced by Gossypium species) are the premier natural fibers for textile production. The two tetraploid species, G. barbadense (Gb) and G. hirsutum (Gh), differ significantly in their fiber properties, the former having much longer, finer and stronger fibers that are highly prized. A better understanding of the genetics and underlying biological causes of these differences will aid further improvement of cotton quality through breeding and biotechnology. We evaluated an inter-specific Gh × Gb recombinant inbred line (RIL) population for fiber characteristics in 11 independent experiments under field and glasshouse conditions. Sites were located on 4 continents and 5 countries and some locations were analyzed over multiple years.

Background: Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy.Results: A dataset of $$\sim $$∼ 13,600 leaf images from 27 genotypes of Cotton was generated. Images were collected from leaves at two different positions in the canopy (leaf 3 & leaf 4), from genotypes grown in two consecutive years and in two growth environments (glasshouse & field). This dataset was used to build a 4-part deep learning model called HairNet. On the whole dataset, HairNet achieved accuracies of 89% per image and 95% per leaf. The impact of leaf selection, year and environment on HairNet accuracy was then investigated using subsets of the whole dataset. It was found that as long as examples of the year and environment tested were present in the training population, HairNet achieved very high accuracy per image (86–96%) and per leaf (90–99%). Leaf selection had no effect on HairNet accuracy, making it a robust model.Conclusions: HairNet classifies images of cotton leaves according to their hairiness with very high accuracy. The simple imaging methodology presented in this study and the high accuracy on a single image per leaf achieved by HairNet demonstrates that it is implementable at scale. We propose that HairNet replaces the current visual scoring of this trait. The HairNet code and dataset can be used as a baseline to measure this trait in other species or to score other microscopic but important phenotypes.

Key message: A Bayesian linkage disequilibrium-based multiple-locus mixed model identified QTLs for fibre, seed and oil traits and predicted breeding worthiness of test lines, enabling their simultaneous improvement in cotton. Improving cotton seed and oil yields has become increasingly important while continuing to breed for higher lint yield. In this study, a novel Bayesian linkage disequilibrium-based multiple-locus mixed model was developed for QTL identification and genomic prediction (GP). A multi-parent population consisting of 256 recombinant inbred lines, derived from four elite cultivars with distinct combinations of traits, was used in the analysis of QTLs for lint percentage, seed index, lint index and seed oil content and their interrelations. All four traits were moderately heritable and correlated but with no large influence of genotype × environment interactions across multiple seasons. Seven to ten major QTLs were identified for each trait with many being adjacent or overlapping for different trait pairs. A fivefold cross-validation of the model indicated prediction accuracies of 0.46–0.62. GP results based on any two-season phenotypes were strongly correlated with phenotypic means of a pooled analysis of three-season experiments (r = 0.83–0.92). When used for selection of improvement in lint, seed and oil yields, GP captured 40–100% of individuals with comparable lint yields of those selected based on the three-season phenotypic results. Thus, this quantitative genomics-enabled approach can not only decipher the genomic variation underlying lint, seed and seed oil traits and their interrelations, but can provide predictions for their simultaneous improvement. We discuss future breeding strategies in cotton that will enhance the entire value of the crop, not just its fibre.

Developing and deploying cotton cultivars with high nutrient uptake, use efficiency and tolerance to nutrient related soil stresses is desirable to assist sustainable soil management. Genetic variation, heritability, selection response and quantitative trait loci (QTLs) were investigated for five macronutrients (P, K, Ca, Mg, S) and five micronutrients (Fe, Mn, B, Zn, and Cu) in a recombinant inbred line (RIL) population from an inter-specific cross between Gossypium hirsutum cv. Guazuncho 2, and G. barbadense accession VH8-4602. Na and K/Na ratio were also studied as the imbalance between Na and other nutrients is detrimental to cotton growth and development. The concentrations of nutrients were measured for different plant parts of the two parents and for leaf samples of the whole population collected at early to peak flowering in field experiments over two years in a sodic Vertosol soil. Parental contrast was large for most nutrient concentrations in leaves when compared with other plant parts. Segregation for leaf nutrient concentration was observed within the population with transgression for P, K, K/Na ratio and all micronutrients. Genotypic difference was the major factor behind within-population variation for most nutrients, while narrow sense heritability was moderate (0.27 for Mn and Cu, and 0.43 for B). At least one significant QTL was identified for each nutrient except K and more than half of those QTLs were clustered on chromosomes 14, 18 and 22. Selection response was predicted to be low for P and all micronutrients except B, high for K, Na and B, and very high for K/Na ratio. Correlations were more common between macronutrients, Na and K/Na ratio where the nature and strength of the relations varied (r=-0.69 to 0.76). We conclude that there is sufficient genetic diversity between these two tetraploid cotton species that could be exploited to improve cotton nutrient status by introgressing species-unique favourable alleles.