ArticleMolecules (Basel, Switzerland)2019
A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Network pharmacology-guided discovery of quercetin as an anti-osteoporotic agent from cyathulae radix with dual pro-osteogenic and antioxidant effects.Molecular biology reports · 2026Article
- GMFNet: A GADF-Mamba Fusion Network for Soybean Seed Hyperspectral Classification.Foods (Basel, Switzerland) · 2026Article
- Redox-mediated activation of PI3K/PTEN pathways by A. baumannii OMV-V. myrtillus conjugate in pancreatic cancer cells.Archives of microbiology · 2026Article
- Img2Variety: Image-based intraspecific varieties identification across the whole growth period.Plant phenomics (Washington, D.C.) · 2026Article
- Deep learning algorithms enable accurate identification of cassava varieties (Frontiers in plant science · 2026Article
- Melatonin: A Silver Bullet for Fertility and Reproductive Health.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Sensor-Centric Intelligent Systems for Soybean Harvest Mechanization in Challenging Agro-Environments of China: A Review.Sensors (Basel, Switzerland) · 2025Review
- Soybean Synergies: A Comprehensive Review on Novel Extraction Techniques and Their Role in Unlocking Health Potential.Food science & nutrition · 2025Review
- Bioinspired poly-dopamine/nano-hydroxyapatite: an upgrading biocompatible coat for 3D-printed polylactic acid scaffold for bone regeneration.Odontology · 2025Article
- Corn variety identification based on improved EfficientNet lightweight neural network.Frontiers in plant science · 2025Article
- An RGB image dataset for seed germination prediction and vigor detection - maize.Frontiers in plant science · 2024Article
- Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network.Plant methods · 2023Article
- Identification of Soybean Mutant Lines Based on Dual-Branch CNN Model Fusion Framework Utilizing Images from Different Organs.Plants (Basel, Switzerland) · 2023Article
- Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR.Journal of imaging · 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
- Applications of machine learning in pine nuts classification.Scientific reports · 2022Article
- Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification.Frontiers in plant science · 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
Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds.
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