ArticleMolecules (Basel, Switzerland)2019
Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties.
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 34 papers.
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Who cites it
34 citing papers in PubMed.
- Progeny Differentiation in Faba Bean Using Hyperspectral Images and Machine Learning.Plant direct · 2026Article
- A Deep Learning Approach for Pixel-Level Material Classification via Hyperspectral Imaging.Journal of imaging · 2026Article
- 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
- A Comprehensive Review of Deep Learning Applications in Cotton Industry: From Field Monitoring to Smart Processing.Plants (Basel, Switzerland) · 2025Review
- Predicting Perennial Ryegrass Cultivars and the Presence of anSensors (Basel, Switzerland) · 2025Article
- Identification of maize kernel varieties based on interpretable ensemble algorithms.Frontiers in plant science · 2025Article
- HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait.Plant methods · 2024Article
- Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Cotton seed cultivar identification based on the fusion of spectral and textural features.PloS one · 2024Article
- A maize seed variety identification method based on improving deep residual convolutional network.Frontiers in plant science · 2024Article
- Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion.Plant methods · 2023Article
- Single Seed Near-Infrared Hyperspectral Imaging for Classification of Perennial Ryegrass Seed.Sensors (Basel, Switzerland) · 2023Article
- Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Plants (Basel, Switzerland) · 2023Article
- Integrating spectral and image information for prediction of cottonseed vitality.Frontiers in plant science · 2023Article
- A method for detecting the quality of cotton seeds based on an improved ResNet50 model.PloS one · 2023Article
- A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning.Plant methods · 2022Article
- Near-infrared spectroscopy combined with pattern recognition algorithms to quickly classify raisins.Scientific reports · 2022Article
- Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview.Sensors (Basel, Switzerland) · 2022Review
- Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning.ACS omega · 2022Article
- Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice.BMC bioinformatics · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.
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