ArticleFrontiers in plant science2020
Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Method Adaptation Patterns and a Selection Framework for Near-Infrared Spectroscopy-Based Moisture Prediction of Major Grain Crops.Foods (Basel, Switzerland) · 2026Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Harnessing deep learning for wheat variety classification: a convolutional neural network and transfer learning approach.Journal of the science of food and agriculture · 2025Article
- Detection of Soluble Solid Content in Citrus Fruits Using Hyperspectral Imaging with Machine and Deep Learning: A Comparative Study of Two Citrus Cultivars.Foods (Basel, Switzerland) · 2025Article
- Predicting Perennial Ryegrass Cultivars and the Presence of anSensors (Basel, Switzerland) · 2025Article
- Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS.Sensors (Basel, Switzerland) · 2024Article
- Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review.Food chemistry: X · 2024Review
- ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness.Scientific reports · 2024Article
- A maize seed variety identification method based on improving deep residual convolutional network.Frontiers in plant science · 2024Article
- YOLO-SDL: a lightweight wheat grain detection technology based on an improved YOLOv8n model.Frontiers in plant science · 2024Article
- Rapid and accurate classification of mung bean seeds based on HPMobileNet.Frontiers in plant science · 2024Article
- Identification of Soybean Mutant Lines Based on Dual-Branch CNN Model Fusion Framework Utilizing Images from Different Organs.Plants (Basel, Switzerland) · 2023Article
- Y-Net: Identification of Typical Diseases of Corn Leaves Using a 3D-2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module.Sensors (Basel, Switzerland) · 2023Article
- A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images.Plant phenomics (Washington, D.C.) · 2023Article
- Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping.Plant phenomics (Washington, D.C.) · 2023Article
- Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning.Foods (Basel, Switzerland) · 2022Article
- Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice.BMC bioinformatics · 2022Article
- Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model.Sensors (Basel, Switzerland) · 2021Article
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7 authors.
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Abstract
Near-infrared (NIR) hyperspectroscopy becomes an emerging nondestructive sensing technology for inspection of crop seeds. A large spectral dataset of more than 140,000 wheat kernels in 30 varieties was prepared for classification. Feature selection is a critical segment in large spectral data analysis. A novel convolutional neural network-based feature selector (CNN-FS) was proposed to screen out deeply target-related spectral channels. A convolutional neural network with attention (CNN-ATT) framework was designed for one-dimension data classification. Popular machine learning models including support vector machine (SVM) and partial least square discrimination analysis were used as the benchmark classifiers. Features selected by conventional feature selection algorithms were considered for comparison. Results showed that the designed CNN-ATT produced a higher performance than the compared classifier. The proposed CNN-FS found a subset of features, which made a better representation of raw dataset than conventional selectors did. The CNN-ATT achieved an accuracy of 93.01% using the full spectra and keep its high precision (90.20%) by training on the 60-channel features obtained via the CNN-FS method. The proposed methods have great potential for handling the analyzing tasks on other large spectral datasets. The proposed feature selection structure can be extended to design other new model-based selectors. The combination of NIR hyperspectroscopic technology and the proposed models has great potential for automatic nondestructive classification of single wheat kernels.
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