ArticleACS omega2022
Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Residual Derivative-Guided Spectral Fusion Module for Few-Shot Classification of Soybean Seed Varieties Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2026Article
- A lightweight deep learning framework for tea cultivar identification based on leaf images.Frontiers in plant science · 2026Article
- Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction.Frontiers in plant science · 2026Review
- Predicting Perennial Ryegrass Cultivars and the Presence of anSensors (Basel, Switzerland) · 2025Article
- Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.Foods (Basel, Switzerland) · 2025Review
- Rice origin traceability using mid-infrared and fluorescence spectral data fusion.Frontiers in plant science · 2025Article
- 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
- Garlic-YOLO-DD: a lightweight object detection algorithm for garlic damage detection.Frontiers in plant science · 2025Article
- Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism.PloS one · 2025Article
- Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies.Plants (Basel, Switzerland) · 2024Review
- Machine learning-based non-destructive terahertz detection of seed quality in peanut.Food chemistry: X · 2024Article
- Accurate and visualiable discrimination of Chenpi age using 2D-CNN and Grad-CAM++ based on infrared spectral images.Food chemistry: X · 2024Article
- Study on the Determination of Flavor Value of Rice Based on Grid Iterative Search Swarm Optimization Support Vector Machine Model and Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2024Article
- Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy.Sensors (Basel, Switzerland) · 2024Article
- Identification and Classification ofFoods (Basel, Switzerland) · 2024Article
- Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet.Frontiers in plant science · 2024Article
- Non-destructive identification ofFrontiers in plant science · 2023Article
- Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning.Frontiers in plant science · 2023Article
- Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination ofMolecules (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
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Authors and funding
7 authors.
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Abstract
Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%. This study further used the deep learning methods to establish variety identification models for 10 varieties of rice seeds without considering their types. Among them, the ResNet model had the best classification results. The classification accuracy on the test set was 86.08%. This study used the saliency map method to visualize each convolutional neural network (CNN) model to find the band region that contributed the most to the data. The results showed that the bands with the largest data contribution were mainly concentrated at approximately 1300-1400 nm and secondarily concentrated at approximately 1050-1250 nm. The overall results showed that NIR hyperspectral imaging technology combined with deep learning could effectively distinguish rice seeds of different varieties. This method provided an effective way to identify rice seed varieties in a quick and nondestructive manner.
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