ArticleRSC advances2019
Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network.
Article in RSC advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 80 citations in OpenAlex.
- GMFNet: A GADF-Mamba Fusion Network for Soybean Seed Hyperspectral Classification.Foods (Basel, Switzerland) · 2026Article
- Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning.Food chemistry: X · 2025Article
- Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning.Foods (Basel, Switzerland) · 2024Article
- Hyperspectral Imaging and Machine Learning as a Nondestructive Method for Proso Millet Seed Detection and Classification.Foods (Basel, Switzerland) · 2024Article
- Fully connected-convolutional (FC-CNN) neural network based on hyperspectral images for rapid identification of P. ginseng growth years.Scientific reports · 2024Article
- Single Seed Near-Infrared Hyperspectral Imaging for Classification of Perennial Ryegrass Seed.Sensors (Basel, Switzerland) · 2023Article
- Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping.Plant phenomics (Washington, D.C.) · 2023Article
- Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning.Frontiers in plant science · 2023Article
- A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning.Plant methods · 2022Article
- Nondestructive Testing of Pear Based on Fourier Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2022Article
- Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods.Frontiers in plant science · 2022Article
- Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning.Frontiers in bioengineering and biotechnology · 2021Article
- Application and interpretation of deep learning methods for the geographical origin identification ofRSC advances · 2020Article
- Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2020Article
- A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning.Molecules (Basel, Switzerland) · 2019Article
- Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Variety identification of seeds is critical for assessing variety purity and ensuring crop yield. In this paper, a novel method based on hyperspectral imaging (HSI) and deep convolutional neural network (DCNN) was proposed to discriminate the varieties of oat seeds. The representation ability of DCNN was also investigated. The hyperspectral images with a spectral range of 874-1734 nm were primarily processed by principal component analysis (PCA) for exploratory visual distinguishing. Then a DCNN trained in an end-to-end manner was developed. The deep spectral features automatically learnt by DCNN were extracted and combined with traditional classifiers (logistic regression (LR), support vector machine with RBF kernel (RBF_SVM) and linear kernel (LINEAR_SVM)) to construct discriminant models. Contrast models were built based on the traditional classifiers using full wavelengths and optimal wavelengths selected by the second derivative (2nd derivative) method. The comparison results showed that all DCNN-based models outperformed the contrast models. DCNN trained in an end-to-end manner achieved the highest accuracy of 99.19% on the testing set, which was finally employed to visualize the variety classification. The results demonstrated that the deep spectral features with outstanding representation ability enabled HSI together with DCNN to be a reliable tool for rapid and accurate variety identification, which would help to develop an on-line system for quality detection of oat seeds as well as other grain seeds.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.