By rapidly analyzing nearly 12,000 individual seeds, the approach uncovers genetic loci that simultaneously influence seed morphology and mineral composition, opening new avenues for crop biofortification and safer food production.
Seeds are fundamental to global food security, providing both calories and essential mineral nutrients to billions of people worldwide. However, the genetic mechanisms that control seed size and the accumulation of beneficial or toxic elements remain incompletely understood, largely because large-scale seed phenotyping is often slow, labor-intensive, and destructive. Plants absorb mineral nutrients from soils and transport them to seeds, where elements such as iron, zinc, calcium, and phosphorus determine yield, nutritional quality, and human health, while toxic metals like arsenic and cadmium can pose serious food safety risks. Although genome-wide association studies offer powerful means to link these complex traits to genes, their effectiveness is constrained by the lack of high-throughput, accurate phenotyping approaches. Conventional analytical methods, such as inductively coupled plasma mass spectrometry, create a critical bottleneck between rapidly advancing genomic data and phenotypic analysis in crop functional genomics.
A study (DOI: 10.1016/j.plaphe.2025.100138) published in Plant Phenomics on 12 November 2025 by Zhong Tang’s & Peng Wang’s team, Nanjing Agricultural University, establishes a high-throughput, non-destructive μ-XRF–based phenotyping framework that bridges the gap between genomics and seed traits, enabling systematic dissection of the genetic control of seed mineral nutrition and morphology for crop improvement.
Using micro–X-ray fluorescence (μ-XRF) imaging coupled with automated computer vision and machine-learning algorithms, the study developed a fully integrated single-seed phenotyping workflow that enables high-throughput, non-destructive quantification of both seed morphology and elemental composition. μ-XRF data cubes were first converted into quantitative RGB and grayscale images, after which image-processing pipelines automatically segmented accession regions and individual seeds. A supervised machine-learning model, ultimately optimized using XGBoost, distinguished seed pixels from background, while a combination of watershed segmentation and convex-defect–based geometric refinement resolved adherent and overlapping seeds. From each accurately segmented seed, seven morphological traits—including area, length, width, perimeter, roundness, aspect ratio, and eccentricity—and the relative abundances of 15 mineral elements were extracted and stored for downstream analysis, allowing a single ultra–high-resolution image containing over 11,000 seeds from 1,163 accessions to be processed within 18–19 hours. Applying this workflow generated robust phenotypic datasets with strong biological relevance. Elemental concentrations derived from μ-XRF showed significant correlations with bulk measurements obtained by ICP-MS, particularly for Ca, Cu, K, Rb, and Zn, confirming reliable detection of these elements, while weaker correlations for As, Se, and S reflected known limitations in penetration depth and tissue-specific accumulation. Morphological traits extracted automatically closely matched manual ImageJ measurements, demonstrating high accuracy and consistency. Multivariate analyses revealed coordinated elemental relationships, such as positive associations between Ca and Sr and among Zn, Cu, and Mn, as well as links between specific elements and seed shape traits. Principal component analysis captured most of the variation in both elemental and morphological datasets and identified accessions with extreme phenotypes. Leveraging these high-quality traits, genome-wide association analysis identified 17 significant loci, including 10 associated with seed elemental concentrations and seven linked to seed morphology, highlighting candidate genes involved in nutrient regulation and seed development and demonstrating the effectiveness of automated μ-XRF phenotyping for dissecting the genetic architecture of complex seed traits.
This integrated phenotyping–genomics framework offers a powerful tool for identifying genes that enhance essential mineral content while limiting toxic elements—an important step toward biofortified crops that address global micronutrient deficiencies. Because the method is non-destructive, seeds can be preserved for further study or breeding, improving efficiency and sustainability.
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References
DOI
10.1016/j.plaphe.2025.100138
Original Source URl
https://doi.org/10.1016/j.plaphe.2025.100138
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.