ArticleMolecules (Basel, Switzerland)2019
Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging.
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 30 papers.
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Who cites it
30 citing papers in PubMed.
- Dual-Level Probability Consistency FS-PFCE for Robust Transfer Classification of Rice Seed Vigor Using Near-Infrared Hyperspectral Imaging.Plants (Basel, Switzerland) · 2026Article
- Adaptive RGB-VNIR spectral fusion for non-destructive quality assessment of Canadian wild rice kernels.Frontiers in plant science · 2026Article
- Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy.Foods (Basel, Switzerland) · 2025Article
- Classification of maize seed hyperspectral images based on variable-depth convolutional kernels.Frontiers in plant science · 2025Article
- Paddy seed viability prediction based on feature fusion of color and hyperspectral image with multivariate analysis.Heliyon · 2024Article
- An exploration of the influence of ZnO NPs treatment on germination of radish seeds under salt stress based on the YOLOv8-R lightweight model.Plant methods · 2024Article
- Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics.Foods (Basel, Switzerland) · 2024Article
- Integrated Transcriptomic and Metabolomic Analyses Identify Critical Genes and Metabolites Associated with Seed Vigor of Common Wheat.International journal of molecular sciences · 2023Article
- Mapping the Genomic Regions Controlling Germination Rate and Early Seedling Growth Parameters in Rice.Genes · 2023Article
- Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics.Current research in food science · 2023Article
- An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image.Frontiers in plant science · 2023Article
- Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning.Frontiers in plant science · 2023Article
- YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor.Frontiers in plant science · 2023Article
- Detection of peanut seed vigor based on hyperspectral imaging and chemometrics.Frontiers in plant science · 2023Article
- Pilot Aquaphotomic Study of the Effects of Audible Sound on Water Molecular Structure.Molecules (Basel, Switzerland) · 2022Article
- Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2022Article
- Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning.Plant methods · 2022Article
- Radiographic Imaging as a Quality Index Proxy forPlants (Basel, Switzerland) · 2022Article
- Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging.Molecules (Basel, Switzerland) · 2022Article
- Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning.ACS omega · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Seed vitality is one of the primary determinants of high yield that directly affects the performance of seedling emergence and plant growth. However, seed vitality may be lost during storage because of unfavorable conditions, such as high moisture content and temperatures. It is therefore vital for seed companies as well as farmers to test and determine seed vitality to avoid losses of any kind before sowing. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with multiple data preprocessing methods and classification models was applied to identify the vitality of rice seeds. A total of 2400 seeds of three different years: 2015, 2016 and 2017, were evaluated. The experimental results show that the NIR-HSI technique has great potential for identifying vitality and vigor of rice seeds. When detecting the seed vitality of the three different years, the extreme learning machine model with Savitzky-Golay preprocessing could achieve a high classification accuracy of 93.67% by spectral data from only eight wavebands (992, 1012, 1119, 1167, 1305, 1402, 1629 and 1649 nm), which could be developed for a fast and cost-effective seed-sorting system for industrial online application. When identifying non-viable seeds from viable seeds of different years, the least squares support vector machine model coupled with raw data and selected wavelengths of 968, 988, 1204, 1301, 1409, 1463, 1629, 1646 and 1659 nm achieved better classification performance (94.38% accuracy), and could be adopted as an optimal combination to identify non-viable seeds from viable seeds.
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