Peach (Prunus persica (L.) Batsch) is one of the most produced and studied stone fruits. Many genetic and genomic resources are available for this species, including a high-quality genome. More recently, a new high-density Illumina peach Single Nucleotide Polymorphism (SNP) chip (9+9K) has been developed by an international consortium as an add-on to the previous 9K array. In the current study, this new array was used to study the genetic diversity and population structure of the National Peach Germplasm Collection of the Agrifood Research and Technology Centre of Aragon (CITA), located in Zaragoza (northern Spain). To accomplish this, 90 peach accessions were genotyped using the new peach SNP chip (9+9K). A total of 9796 SNPs were finally selected for genetic analyses. Through Identity-By-Descent (IBD) estimate analysis, 15 different groups with genetically identical individuals were identified. The genetic diversity and population structure elucidated a possible exchange of germplasm material among regions, mainly in the northern regions of Spain. This study will allow for more efficient management of the National Peach Germplasm Collection by classifying valuable individuals for genetic diversity preservation and will benefit forthcoming Genome-Wide Association Studies (GWAS) of commercially important fruit traits in peach.

Background: High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of artificial intelligence (AI) brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs. Results: The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error of 0.47. Fifty-five heritable morphological, morphometric, and color traits were identified, highlighting their potential as target traits in breeding programs. Conclusion: The proposed workflow demonstrated robust performance across diverse datasets and was effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labeling tools allows users to fully leverage the advantages of these technologies—reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.

High-throughput phenotyping is boosting plant breeding by generating large-scale phenotypic data for traits that were previously expensive and/or time-consuming to measure. A high-throughput phenotyping platform integrating hyperspectral imaging with a Python workflow has been developed to phenotype nutritional components in almond breeding populations, addressing the current phenotyping bottleneck of conventional methods. Kernel and powder samples from a reference set of 112 almond genotypes were scanned using a hyperspectral camera in the SWIR range (900–1700 nm) and subsequently analysed for nutritional components, including fats, protein, fiber, sucrose, fatty acids, and phytosterols. Partial Least Squares (PLS) models were developed to predict nutritional components in almond kernels, achieving cross-validation RMSE (RMSE_CV) values of 0.73, 1.28, 7.90, and 1.96, and corresponding R²_CV values of 0.82, 0.86, 0.66, and 0.57 for protein, fats, β-sitosterol, and oleic acid (C18:1), respectively. Selected PLS models were implemented to predict the nutritional components of 528 genotypes from a germplasm collection and six F₁ populations. Narrow-sense heritability for these predicted traits was estimated using an advanced linear mixed model incorporating pedigree and genomic data using the 60K Almond SNP array, revealing relevant additive effects for predicted traits (ℎ²>0.5). The approach employed here represents a major advance in nutritional almond breeding, enabling the phenotyping of six times more individuals than previous studies and generating the largest phenotypic dataset of nutritional components in almonds and other tree nuts.

In plant breeding and genetics, recent advances in high-throughput phenotyping are beginning to meet the growing demand for large-scale, high-quality phenotypic data that emerged after the development of next-generation sequencing technologies. Recent developments in phenomics have been incorporated into almond breeding programs, facilitating the large-scale acquisition of quantitative phenotypes and the dissection of the genetic architecture underlying morphological and quality-related traits. The implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations and the identification of 567 robust marker–trait associations across 66 traits. These analyses revealed two major genomic hotspots on chromosomes 2 and 5 associated with morphological and quality-related traits. These regions harbored biologically relevant candidate genes, including genes associated with OVATE family proteins, brassinosteroid signaling, protein ubiquitination, and acyl-CoA metabolism, as well as other regulators of organ growth, cell proliferation, hormone signaling, and seed development. Furthermore, a novel candidate gene encoding a COMT-like O-methyltransferase involved in lignin biosynthesis was identified and proposed to contribute to shell hardness, a major genetically controlled trait in almond. Together, these findings demonstrate the potential of integrating high-throughput phenomics and genomics to dissect complex traits, identify candidate genes, and accelerate genomics-informed breeding in almond.

High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of AI brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology and heritable morphometric traits using AI, which can be applied to fruits and other plant organs. This workflow has been implemented in almond (Prunus dulcis ), a species where efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals have been phenotyped, making this the largest morphological study conducted in almond. As result, new heritable morphometric traits of interest have been identified. These findings pave the way for more efficient breeding strategies, ultimately facilitating the development of improved cultivars with desirable traits.