ArticleFood science & nutrition2024
Hyperspectral imaging combined with GA-SVM for maize variety identification.
Article in Food science & nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- CounterfeitFood science & nutrition · 2026Article
- Early Apple Bruise Detection via Discrete Hyperspectral Signatures with SHAP-Guided Feature Selection and a CNN-Transformer Model.Foods (Basel, Switzerland) · 2026Article
- Spectral prediction of anthocyanin concentration inFrontiers in plant science · 2026Article
- RGSGAN-MACRNet: A More Accurate Recognition Method for Imperfect Corn Kernels Under Sample-Size-Limited Conditions.Foods (Basel, Switzerland) · 2025Article
- Variety Identification of Corn Seeds Based on Hyperspectral Imaging and Residual Mamba 1D CNN.Foods (Basel, Switzerland) · 2025Article
- Variety Identification of Corn Seeds Based on Hyperspectral Imaging and Convolutional Neural Network.Foods (Basel, Switzerland) · 2025Article
- Non-Destructive Detection of Chilled Mutton Freshness Using a Dual-Branch Hierarchical Spectral Feature-Aware Network.Foods (Basel, Switzerland) · 2025Article
- Constructing an origin discrimination model of japonica rice in Heilongjiang Province based on confocal microscopy Raman spectroscopy technology.Scientific reports · 2025Article
- Early detection ofFrontiers in plant science · 2025Article
- Estimation Model for Maize Multi-Components Based on Hyperspectral Data.Sensors (Basel, Switzerland) · 2024Article
- Hyperspectral imaging combined with GA-SVM for maize variety identification.Food science & nutrition · 2024Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.
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