ArticlePlant methods2022
A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning.
Article in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Optimizing XGBoost via mSMA_plus: A Novel Meta-Heuristic Approach for High-Precision Multiclass Dry Bean Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Comparative classification of spectrally overlapping Allium seed genotypes using Vis-NIR spectroscopy and hyperspectral imaging with chemometric, machine, and deep learning models.Scientific reports · 2026Article
- Wheat seed germination prediction in response to temperature, water potential, and salinity using an artificial neural network.Scientific reports · 2026Article
- WSPSorter: a real-time integrated system for non-destructive purity sorting of watermelon seeds based on rotary singulation and near infrared spectroscopy.Frontiers in plant science · 2026Article
- Non-destructive detection strategy of maize seed vigor based on seed phenotyping and the potential for accelerating breeding.Journal of advanced research · 2025Article
- Corn variety identification based on improved EfficientNet lightweight neural network.Frontiers in plant science · 2025Article
- Identification of maize kernel varieties based on interpretable ensemble algorithms.Frontiers in plant science · 2025Article
- Classification of maize seed hyperspectral images based on variable-depth convolutional kernels.Frontiers in plant science · 2025Article
- A novel method for identifying rice seed purity using hybrid machine learning algorithms.Heliyon · 2024Article
- Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging.Frontiers in plant science · 2024Article
- Comparison of Different Machine Learning Algorithms for the Prediction of the Wheat Grain Filling Stage Using RGB Images.Plants (Basel, Switzerland) · 2023Article
- Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network.Plant methods · 2023Article
- Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity.Frontiers in plant science · 2023Article
- Hyperspectral imaging combined with CNN for maize variety identification.Frontiers in plant science · 2023Article
- Hyperspectral imaging with machine learning for non-destructive classification ofFrontiers in plant science · 2022Article
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10 authors.
Funding
Abstract
backgroundVariety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data.
resultsOur results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties.
conclusionsThis strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.
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