ArticleJournal of advanced research2025
Non-destructive detection strategy of maize seed vigor based on seed phenotyping and the potential for accelerating breeding.
Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Early detection and genetic insights into salt tolerance in rice by linking physiology and spectral reflectance during seed germination.Plant phenomics (Washington, D.C.) · 2026Article
- Detection of Frost-Damaged Maize Kernels for Food Quality Screening Using Hyperspectral Imaging and 2D-COS-Assisted Deep Learning.Food science & nutrition · 2026Article
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
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10 authors.
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Abstract
introductionSeeds are fundamental to agricultural production, and their vigor affects seedling quality, quantity, and crop yield. Accurate vigor assessment methods are crucial for agricultural productivity.
objectivesTraditional seed vigor testing and phenotypic trait acquisition methods are complex, time-consuming, or destructive. Thus, this study aims to develop a non-destructive method for assessing maize seed vigor based on seed phenotyping and to delve into the underlying mechanism of this method.
methodsUtilizing 368 maize inbred lines with diverse genetic backgrounds as research material, the cold-soaking germination percentage, closely related to the field emergence percentage, was selected to evaluate seed vigor. High and low-vigor groups were ultimately obtained through mixed grouping based on the consistent performance of seeds harvested across years. Subsequently, non-destructive techniques such as hyperspectral imaging, machine vision, and gas chromatography with ion mobility spectrometry, along with machine learning, were employed to establish models for distinguishing high and low-vigor maize seeds in their natural state. After determining the optimal strategy, key phenotypic features were identified for relevant genetic and metabolic analyses to elucidate the effectiveness of the seed vigor testing model.
resultsAmong the evaluated methods, the machine vision-based emerged as the optimal seed vigor detection method (accuracy ≈ 90%). Subsequently, four key features (B_mean, b_mean, S_mean, and b_std) were selected for genome-wide association analysis, revealing two confident candidate genes involved in hormone regulation affecting seed germination. Further investigations confirmed significant differences in several endogenous hormones' levels and flavonoid, chlorophyll, and anthocyanidin content between high and low-vigor maize seeds.
conclusionThis study validates a reliable, non-destructive seed vigor detection model supported by genetic and physiological-biochemical evidence. The findings enhance the application of non-destructive seed quality testing models and provide reliable and high-throughput measurable phenotypic traits associated with seed vigor, thereby facilitating gene mining and accelerating high-vigor maize variety breeding.
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