ArticleRSC advances2020
Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds.
Article in RSC advances, 2020. 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, 41 citations in OpenAlex.
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- ORBMO-RF: a non-destructive classification method for ginseng seeds based on multimodal fusion and improved red-billed blue magpie optimization algorithm.Frontiers in plant science · 2025Article
- Corn variety identification based on improved EfficientNet lightweight neural network.Frontiers in plant science · 2025Article
- EfficientMaize: A Lightweight Dataset for Maize Classification on Resource-Constrained Devices.Data in brief · 2024Article
- Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging.Foods (Basel, Switzerland) · 2023Article
- Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data.Plant phenomics (Washington, D.C.) · 2023Article
- Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms.PeerJ. Computer science · 2023Article
- A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology.Sensors (Basel, Switzerland) · 2022Article
- A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning.Plant methods · 2022Article
- Evaluation of rice bacterial blight severity from lab to field with hyperspectral imaging technique.Frontiers in plant science · 2022Article
- Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.Frontiers in nutrition · 2021Review
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Common maize seeds and silage maize seeds are similar in appearance and are difficult to identify with the naked eye. Four varieties of common maize seeds and four varieties of silage maize seeds were identified by near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics. The pixel-wise principal component analysis was used to distinguish the differences among different varieties of maize seeds. The object-wise spectra of each single seed sample were extracted to build classification models. Support vector machine (SVM) and radial basis function neural network (RBFNN) classification models were established using two different classification strategies. First, the maize seeds were directly classified into eight varieties with the prediction accuracy of the SVM model and RBFNN model over 86%. Second, the seeds of silage maize and common maize were firstly classified with the classification accuracy over 88%, then the seeds were classified into four varieties, respectively. The classification accuracy of silage maize seeds was over 98%, and the classification accuracy of common maize seeds was over 97%. The results showed that the varieties of common maize seeds and silage maize seeds could be classified by NIR-HSI combined with chemometrics, which provided an effective means to ensure the purity of maize seeds, especially to isolate common seeds and silage seeds.
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Registered trials
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