ASHS 2025 Conference

Carrots are one of the most popular vegetables, valued for both their culinary uses and health benefits. While carrot breeders have primarily focused on enhancing appearance to meet consumer expectations, flavor is also an important factor. Sugars, which are key to carrot flavor, are the focus of this project. Carrots accumulate two main types of free sugar-reducing sugars (glucose and fructose) and non-reducing sugars (sucrose). Carrots with high percentage of reducing sugar tend to have sweeter, less harsh and more preferred flavor. The balance between sucrose and reducing sugars is controlled by a dominant gene called Rs, where heterozygous plants have a higher proportion of reducing sugars. In this study, we conducted a genome-wide association study (GWAS) using a diverse set of carrot accessions grown over five years to identify SNP markers linked to sugar composition in carrots. The enzyme acid-soluble invertase isozyme II, which breaks down sucrose into glucose and fructose, was identified as the most significant candidate gene. To further validate this gene’s involvement in the Rs locus, we are using genome editing techniques. Details of this genome editing work will be presented.

Purple carrots (Daucus carota L.) are becoming increasingly popular as a fresh market novelty food and as sources of natural pigments in foods and beverages. Anthocyanins are associated with many health benefits, such as reduced cardiovascular disease risk, fewer types of cancers, and reduced inflammation. Increasing anthocyanin content in purple carrots is therefore worthwhile for plant breeders and consumers. An interesting trait in carrots is heavy pubescence in the highest anthocyanin-producing breeding lines. Pubescence has been shown to be linked to anthocyanin content and abiotic stress resistance in other plant species. An F2 mapping population was created between two purple breeding lines developed from two separate Turkish accessions. The population contrasted in the level of pubescence and anthocyanin content in petioles and roots. Anthocyanin content was determined in the taproots and the level of pubescence was scored visually and through image analysis. Pubescence mapped to two loci that may be related with transcriptional regulation of trichome density and length. Anthocyanin content appears to be inhibited by a single locus that is unlinked with pubescence in this population. Epigenetic silencing was also observed in the purple carrot population and has implications on the development of high anthocyanin content varieties. Information from this study will provide genetic markers for increasing anthocyanin content in purple carrot breeding populations and developing pubescent varieties with abiotic stress resistances.

Chile peppers (Capsicum annuum L.) hold a vital position in global agriculture and diets, valued for their unique flavor, diverse uses, and nutritional benefits. Among their bioactive compounds, carotenoids play a significant role, acting as antioxidants and precursors to vitamin A, with immense implications for human health. This study aims to identify carotenoid diversity and determine the genetic control of carotenoid production in a diverse population of 127 chile pepper genotypes. Fruits grown in Las Cruces, NM, were harvested in the 2024 growing season. High-performance liquid chromatography (HPLC) will be employed to profile individual carotenoids such as β-carotene, lutein, capsanthin, capsorubin, zeaxanthin, and violaxanthin. To optimize carotenoid extraction and minimize degradation, three different saponification methods were tested, varying in incubation time and temperature: 30 minutes at 50°C, 30 minutes at room temperature, and 60 minutes at room temperature. The best results were obtained with 30 minutes of incubation at room temperature. After getting HPLC results for the whole pannel this data will be integrated with genome-wide association studies (GWAS) to identify key genetic loci and candidate genes associated with carotenoid content. The study aims to provide a foundation for marker-assisted selection to improve the nutritional quality of chile peppers. The findings have direct implications for breeding programs, enabling the development of biofortified chile pepper varieties.

Fruit morphology has a significant impact on the agronomic performance of chile peppers, influencing both yield potential and mechanical harvest efficiency. Through the integration of genome-wide association studies (GWAS) with Tomato Analyzer, an image-based phenomics tool, we aim to identify single nucleotide polymorphism (SNP) markers associated with fruit architecture and morphology. A Capsicum association mapping panel (CAMP) consisting of 128 genotypes with three checks evaluated in Las Cruces, NM under an augmented design for the 2024 growing season. The design consisted of ten blocks, each with a different number of test genotypes whereas checks were replicated in each block. Ten green and ten red fruits (N=20) for each genotype were scanned using a flatbed scanner and images were processed using Tomato Analyzer software to record fruit architecture. Best linear unbiased predictions (BLUPs) were calculated for maximum fruit height (MAXH; cm), maximum fruit width (MAXW; cm), curved fruit height (CURH; cm), width mid-height (WMH; cm), area (ARA; cm2), and perimeter (PER; cm). High narrow sense heritability (h2) ranging between 0.80 and 0.98 was observed. A medium to high Pearson correlation (r=0.56–1.00) was observed for all traits except WHM. After filtration and imputation, 40,709 genotyping-by-sequencing (GBS) SNP markers were used to perform multi-locus GWAS. A total of 129 SNP markers associated with seven basic fruit measurements across 10 chromosomes were identified. The SNP marker SCM002812.1_10016804 on chromosome 1 at 10.02 Mb was found to be associated with the potential candidate gene YABBY4, which can regulate fruit developmental processes. Other candidate genes identified included Gibberellin receptor GID1B, Cyclin-L1-1, and U6 small nuclear RNA (adenine-(43)-N(6))-methyltransferase), regulating plant growth hormones, cell division, and methylation, respectively. The findings of this study will be relevant for the development of molecular markers for marker-assisted selection and studying expression levels of genes regulating fruit development in a comparative analysis using chile pepper genotypes with contrasting fruit morphology.

Marker-assisted selection is important to facilitate the process of genetic improvement in vegetable breeding programs. A set of 192 trait-associated single nucleotide polymorphism (SNP) markers identified from previous genome-wide mapping studies has been developed at the New Mexico State University (NMSU) Chile Pepper Breeding and Genetics Program (NMSU-192) through the PlexSeq Genotyping Technology of AgriPlex Genomics (https://www.agriplexgenomics.com/plexseq-technology). The NMSU-192 SNP array consists of SNPs associated with easy destemming (14 SNPs), plant architecture and morphology (76), yield and yield components (78), and Phytophthora capsici resistance (24). Genetic diversity analysis using the NMSU-192 demonstrated the feasibility of the SNP array to characterize 188 Capsicum spp. genotypes based on fruit architecture and morphology. Together with parental and reference genotypes, F2:3, F3:4, and F4:5 segregating families of chile pepper breeding lines will be genotyped using the NMSU-192 for marker-assisted breeding and selection at the NMSU Chile Pepper Breeding and Genetics Program. The NMSU-192 will be a valuable component of the breeding toolbox for the genetic improvement of traits relevant to the chile pepper industry in New Mexico and in the pepper genetics community.

Establishing a successful breeding program requires careful planning across multiple dimensions, including crop prioritization, stakeholder engagement, infrastructure development, germplasm acquisition, and definition of breeding goals. With these priorities in mind, we are developing a comprehensive vegetable breeding program focused on pepper (Capsicum spp.), anchored by both statewide and national stakeholder surveys and concept mapping exercises. These efforts have informed infrastructure development, germplasm sourcing, and trait prioritization aligned with end-user needs. As a foundational step, we assembled the UGA-CAPSI-CORE collection, a curated panel of over 450 diverse pepper accessions, including breeding lines, improved landraces, and ex-PVPs. This collection is currently being evaluated for key horticultural traits through conventional field-based assessment and high-throughput phenotyping. In parallel, a preliminary experimental subset is undergoing targeted screening for major biotic stresses, including Phytophthora capsici (Phytophthora blight), Colletotrichum spp. (Anthracnose), Meloidogyne incognita (Root-knot nematode), and insect pests such as pepper weevil (Anthonomus eugenii), green peach aphid (Myzus persicae), and whitefly (Bemisia argentifolii). Fruit quality parameters, including firmness, color, total soluble solids, and vitamin A and C content, are also being evaluated in the same subset. To complement phenotypic evaluation, we have screened the UGA-CAPSI-CORE collection for Phytophthora resistance using publicly available SSR markers, with allele binning conducted via TANDEM software. Whole genome resequencing (WGRS) of the full collection is currently underway to provide a high-resolution view of genetic diversity and trait architecture. Looking ahead, we are expanding the program to include transcriptomics and metabolomics analyses in response to P. capsici infection, enabling a systems-level understanding of host-pathogen interaction. The integration of phenotypic, genotypic, transcriptomic, and metabolic data will accelerate discovery of candidate genes and molecular markers for use in genomics-assisted breeding. This multipronged strategy positions the UGA vegetable breeding program to deliver pest- and disease-resistant, and nutritionally enhanced pepper cultivars for Georgia and beyond.

Modern sweetpotato breeding programs evaluate hundreds of genotypes across successive generations to identify lines with superior storage root quality traits. However, traditional phenotyping methods rely on manual storage root evaluation, limiting both the scale and speed of selection. Small Unmanned Aircraft System (sUAS)-based high-throughput phenotyping offers scalable, image-based alternatives that enable breeders to collect highly detailed data with reduced bias, facilitating genomic selection. By linking image-derived phenotypes to genotypic data, these approaches could shorten the breeding cycle by supporting earlier or more optimal selection decisions. In this study, we developed an image-based yield estimation pipeline for early generation and advanced sweetpotato breeding lines using sUAS-based RGB (0.17 cm pixel⁻¹) and multispectral imagery. The pipeline leveraged a previously developed Mask R-CNN segmentation model for sweetpotato storage root detection that was pre-trained using mobile RGB images and fine-tuned using annotated aerial images to optimize performance for sUAS applications. Imagery was acquired in 2024 from two research fields immediately after harvest. Ground truth plot-level root yield was collected using mechanical singulation in an optical sorter (Exeter Engineering). The Mask R-CNN model generated instance masks of individual storage roots directly from plot-level RGB imagery, with root metrics such as length, diameter, and volume estimated using multiple geometrical methods. The model demonstrated strong predictive performance across both locations. Combined-location analysis yielded a correlation coefficient of 0.94 for storage root weight estimation (0.88 and 0.97 for individual locations) with a root mean squared error (RMSE) of 1.24 kg plot⁻¹. Root count estimation achieved a correlation coefficient of 0.78 (0.73 and 0.92 independently) with an RMSE of 11 roots plot⁻¹. These results indicate robust yield estimation across diverse genotypes and field conditions. Furthermore, these findings highlight the potential of combining aerial imagery and deep learning to streamline yield assessment in sweetpotato breeding programs. Future work will focus on enhancing model accuracy by incorporating root feature analysis, quality classifications, and expanded datasets to further support breeding decisions and accelerate selection pipelines.

A forum for discussion of potential collaborations with regards to fruit, vegetable, and edible crops – i.e. citrus, breeding, production systems, postharvest, pomology, crop management, viticulture, etc.