Jul 2026· Trends in Plant Science· 0 citations· 65 references
Medicine
TL;DR
Here, it is reconsider how HTP can be effectively integrated into breeding by accounting for methodologies, scale-related constraints, and technological limitations.
Abstract
High-throughput phenotyping (HTP) has advanced rapidly in recent decades, driven by technological developments across research and agricultural frameworks. Despite its success in measuring traits, it remains underutilized in field crop breeding programs. While the most critical and labor-intensive selection is conducted on heterozygous single plants or small plots at early stages, most phenotyping research focuses on stable genotypes grown in large plots. Here, we reconsider how HTP can be effectively integrated into breeding by accounting for methodologies, scale-related constraints, and technological limitations. Our focus remains on self-pollinated field crops, the predominant global food source. In light of climate change and food security needs, improving the integration of breeding and phenomics can accelerate genetic and technological advances in developing elite varieties.
Abiotic stress tolerance has been significantly weakened in modern crops during the domestication process. Regaining tolerance has become a critical task in light of current climate trends and their impact on global food security. Abiotic stress tolerance is an extremely complex trait and is conferred at various levels of plant functional organization and developmental stages, with regulatory mechanisms operating across multiple scales, from individual cells to tissues and the entire plant. The emergence of advanced molecular tools such as single-cell RNA sequencing and spatial omics technologies has revolutionized the field, advancing our understanding of plant responses to hostile environments. However, the implementation of this knowledge in crop breeding programmes is handicapped by the lack of appropriate phenotyping platforms. Here, we argue that current phenotyping methods may be excellent tools for functional validation of previously discovered traits but have limited predictive value in stress biology. We also propose that bridging the mismatch between omics technologies and phenotyping is the only way to account for cell-specific operation of key genes conferring stress tolerance and implementing them in breeding programmes. Some practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.
Sergey Shabala, Ping Yun, Zhong-Hua Chen et al.· New Phytologist· 0 citations
Perennial grains represent a promising frontier for sustainable agriculture, but breeding progress is constrained by the accessibility of genotyping and the difficulty of evaluating complex traits expressed for multiple years after establishment across heterogeneous environments. Phenomic selection may help address these challenges by using scalable, high-dimensional phenotypes collected early in development, although the robustness of such predictions across breeding cycles remains uncertain. Here, we compared genomic and phenomic selection across two breeding cycles of Thinopyrum intermedium (intermediate wheatgrass; IWG; Kernza®), comprising approximately 2,280 individuals from maternal half-sib families evaluated across multiple field sites and years. We constructed relationship matrices from genomic markers and early-life stage phenomic data, including seed and leaf color (HSV), CropReporter multispectral reflectance, and hyperspectral reflectance sensors. Genomic models provided the strongest predictions on average across all field traits in both cycles. Among phenomic predictors, leaf HSV was consistently the most informative, whereas CropReporter and hyperspectral data showed lower and more trait-dependent performance. Seed HSV provided little predictive value. Genomic, leaf HSV, and CropReporter models transferred across breeding cycles with little loss of predictive ability relative to within-cycle validation, demonstrating that their predictive signals were not restricted to a single breeding cycle. Despite limited similarity among relationship matrices, multi-relationship-matrix models rarely improved prediction beyond the stronger constituent single-relationship-matrix model. Together, these results show that early-life stage phenomic data provide reproducible information about agronomic performance expressed years later, but that predictor complexity and data integration do not guarantee improved prediction. Plain language summary Perennial grain crops such as intermediate wheatgrass can take several years to evaluate in the field, slowing breeding progress. We tested whether measurements collected from seeds and young seedlings could predict agronomic traits expressed years later in the field. In two breeding cycles, genomic prediction was strongest overall, but leaf color measurements from RGB images provided modest and reproducible predictions across cycles. More complex multispectral and hyperspectral measurements were generally less consistent, and combining multiple predictor data types rarely improved prediction. These results suggest that inexpensive early-life phenotyping may help breeders prioritize plants before mature field traits are available, although it is unlikely to replace genomic selection when genomic resources are already well established.
Zachary N. Harris, Jackson Braley, Eric Cassetta et al.· bioRxiv· 0 citations
Rice, wheat, and maize cereals are the major foundation of global food security. However, climate change makes it more challenging to achieve high crop yield, the challenge occurs due to improper management of cereal diseases and pests, and limitations of traditional breeding processes. This study aimed to update the process, limitations, and prospects of molecular tools for cereal breeding, and to explore the significance of marker-assisted selection, marker-assisted backcrossing, gene pyramiding, genomic selection, and modern breeding for improving yield, stress tolerance, and grain quality of cereals. Based on recent studies, we have explored the advances and applications of high-throughput genotyping platforms like the single nucleotide polymorphism (SNP) array and genotyping by sequencing technology in cereals. In this study, we found several limitations, such as a low number of studies with large amounts of data, genotype-environment interactions, lack of study findings at the field level, cost implications, and integration of complex multi-omics data. This study further reveals that many crucial agronomic traits are polygenic in their mode of inheritance, and the hidden genetic links make selection weak and uncertain. However, the application of molecular tools such as CRISPR/Cas genome editing, speed breeding, pan-genomics, artificial intelligence, and high-throughput phenomics provides sustainable solutions to these challenges in cereal improvement. The application of these modern breeding tools, combined with microbiome-assisted breeding and agricultural technologies in precision cereal breeding, opens new opportunities for enhancing yield and climate-smart, sustainable cereal production for global food and nutrition security.
M. Hayat, R. Cengiz, Umair Gull et al.· Plant Trends· 0 citations
Plant breeding has progressed from phenotype-based selection to increasingly precise genetic and agronomic interventions. Advances in molecular breeding, genome engineering, and crop management have improved productivity, but have also promoted the widespread use of genetically uniform cultivars optimized for controlled production systems. While uniformity facilitates predictability and mechanization, it may constrain adaptive capacity under increasingly variable environmental conditions. In parallel, recent developments in digital agriculture, including high-resolution phenotyping, remote-sensing, molecular diagnostics, and AI-assisted decision support, are transforming the ability to monitor and manage biological variation across spatial and temporal scales. In this review, we examine how these technological advances intersect with emerging concepts in crop diversity and reproductive biology. We discuss how digital agriculture enables improved characterization of genotype-environment interactions and consider reproductive mechanisms that expand the accessible breeding space beyond conventional biparental crossing schemes, including haploid induction and multi-parental breeding. These approaches provide opportunities to accelerate trait introgression, generate novel genetic combinations, and overcome reproductive barriers. We argue that digital and diagnostic agriculture provide an informational framework for the deployment and evaluation of genetically heterogeneous plant populations. Together, recent advances suggest that technological precision and biological diversity can be integrated into breeding strategies that improve productivity and resilience.
Generation time is the principal bottleneck constraining genetic gain in plant breeding. Speed breeding (SB) addresses this by simultaneously managing the photoperiod, light spectrum and intensity, temperature, CO2 concentration, mineral nutrition, growing substrate volume, and post-harvest seed dormancy, enabling four to seven generations per year in long-day cereals and legumes, and four to five generations in optimised short-day systems. This review evaluates SB against the conventional pedigree framework; synthesises validated environmental parameters and crop-specific protocols; and examines principal SB applications in hybridisation, genomic selection, disease-resistance screening, and allele introgression. A structured comparison of major review and protocol papers identifies broad consensus on core parameters alongside genuine divergence on far-red light supplementation. Critical evaluation addresses genotype-by-environment interaction, incomplete trait coverage, infrastructure costs, and unresolved questions on the biological integrity of rapidly advanced generations. The review further discusses new genomic techniques (NGTs), particularly CRISPR/Cas9-based gene editing, in the context of the EU’s June 2026 NGT regulation, under which Category 1 plants carrying only targeted endogenous modifications are exempt from GMO authorisation. When combined with SB, this regulatory shift can compress the interval from gene-editing event to registered variety from decades to a few years. Speed breeding, NGTs, and conventional field-based selection are most productively treated as complementary elements of a unified pipeline.
V. Mladenov, Rada Šućur, B. Banjac et al.· Plants· 0 citations
Wheat is one of the world’s main crops. Its improvement is pivotal given the threat of climate change and the growing population. However, enhancing breeding efficiency and improving wheat are challenging due to strong genotype-by-environment (G×E) interactions and the biological complexity underlying the wheat genome and key agronomic traits. In this context, predictive frameworks and data-driven approaches can offer new strategies to address these challenges. This article provides a comprehensive review of the latest developments in wheat breeding, highlighting emerging predictive frameworks and their contributions to modern breeding pipelines. First, we report on genomic selection (GS) applications, emphasizing GS’s ability to improve complex traits by shortening the breeding cycle and increasing selection accuracy. We then describe the applications of phenomics in wheat breeding, including both ground- and unmanned aerial vehicle (UAVs)-based systems. We also discuss the potential for implementing multi-omics strategies to improve complex wheat traits. We debate how predictive breeding frameworks can assist in identifying the best parents and crosses in wheat breeding. Finally, we presented the latest panorama of software for predictive breeding and its integration with other technologies. This review reports recent advances demonstrating how predictive frameworks are reshaping wheat breeding methods, highlighting current progress and outlining future opportunities to accelerate genetic gain in wheat improvement.
P. Vitale, Karim Ammar, Flávio Breseghello et al.· WheatOmics· 0 citations
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