Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning

[1]  Yibin Ying,et al.  Peach variety detection using VIS-NIR spectroscopy and deep learning , 2020, Comput. Electron. Agric..

[2]  Jiang Xiao,et al.  Rapid Vitality Estimation and Prediction of Corn Seeds Based on Spectra and Images Using Deep Learning and Hyperspectral Imaging Techniques , 2020, IEEE Access.

[3]  K. Malithong,et al.  Establishment of an Accurate Starch Content Analysis System for Fresh Cassava Roots Using Short-Wavelength Near Infrared Spectroscopy , 2020, ACS omega.

[4]  Chu Zhang,et al.  Noise reduction in the spectral domain of hyperspectral images using denoising autoencoder methods , 2020 .

[5]  Zhanming Li,et al.  Discrimination of Tetrastigma hemsleyanum according to geographical origin by near-infrared spectroscopy combined with a deep learning approach. , 2020, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[6]  Gustavo Marrero Callicó,et al.  Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks , 2020, Sensors.

[7]  Yong He,et al.  Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds , 2020, RSC advances.

[8]  Linsheng Huang,et al.  Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion. , 2020, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[9]  Liu Zhang,et al.  Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[10]  Min Huang,et al.  Maize seed classification using hyperspectral image coupled with multi-linear discriminant analysis , 2019 .

[11]  Zhe Xu,et al.  Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy , 2019, Comput. Intell. Neurosci..

[12]  Yibin Ying,et al.  Deep learning for vibrational spectral analysis: Recent progress and a practical guide. , 2019, Analytica chimica acta.

[13]  Zhengdong Liu,et al.  Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network , 2019, Textile Research Journal.

[14]  Xiaoyi Chen,et al.  1D convolutional neural network for the discrimination of aristolochic acids and their analogues based on near-infrared spectroscopy , 2019, Analytical Methods.

[15]  Yidan Bao,et al.  Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties , 2019, Molecules.

[16]  Yidan Bao,et al.  Hyperspectral imaging for seed quality and safety inspection: a review , 2019, Plant Methods.

[17]  Zafer Cömert,et al.  Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach , 2019, Comput. Electron. Agric..

[18]  Zhuopin Xu,et al.  A calibration transfer optimized single kernel near-infrared spectroscopic method. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[19]  Asifullah Khan,et al.  A survey of the recent architectures of deep convolutional neural networks , 2019, Artificial Intelligence Review.

[20]  Yong He,et al.  Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis , 2018, Sensors.

[21]  Chu Zhang,et al.  Discrimination of Chrysanthemum Varieties Using Hyperspectral Imaging Combined with a Deep Convolutional Neural Network , 2018, Molecules.

[22]  Ruggero G. Pensa,et al.  $M^3\text{Fusion}$: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  Yang Yu,et al.  A novel deep learning-based method for damage identification of smart building structures , 2018, Structural Health Monitoring.

[24]  Xia Ye,et al.  A Sentiment Analysis Method Based on BLSTM and CNN Fusion , 2018, Journal of Physics: Conference Series.

[25]  Yu Liu,et al.  Fusion that matters: convolutional fusion networks for visual recognition , 2018, Multimedia Tools and Applications.

[26]  Chu Zhang,et al.  Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network , 2018 .

[27]  Yong He,et al.  Application of hyperspectral imaging and chemometrics for variety classification of maize seeds , 2018, RSC advances.

[28]  J. Pierna,et al.  Assessment of pesticide coating on cereal seeds by near infrared hyperspectral imaging , 2017 .

[29]  Chu Zhang,et al.  Application of Near-Infrared Hyperspectral Imaging to Detect Sulfur Dioxide Residual in the Fritillaria thunbergii Bulbus Treated by Sulfur Fumigation , 2017 .

[30]  R. Boulton,et al.  Use of Near-Infrared Spectroscopy and Chemometrics for the Nondestructive Identification of Concealed Damage in Raw Almonds (Prunus dulcis). , 2016, Journal of agricultural and food chemistry.

[31]  Huang Min,et al.  Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging , 2016 .

[32]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[33]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Yong He,et al.  Comparison of Infrared Spectroscopy and Nuclear Magnetic Resonance Techniques in Tandem with Multivariable Selection for Rapid Determination of ω-3 Polyunsaturated Fatty Acids in Fish Oil , 2014, Food and Bioprocess Technology.

[35]  J. Stoltzfus,et al.  Logistic regression: a brief primer. , 2011, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[36]  Karin Fackler,et al.  A Review of Band Assignments in near Infrared Spectra of Wood and Wood Components , 2011 .

[37]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[38]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[39]  Yang-Ping Yao,et al.  Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks , 2021 .

[40]  Gongjian Wen,et al.  Learning Fully Convolutional Network for Visual Tracking With Multi-Layer Feature Fusion , 2019, IEEE Access.

[41]  Chu Zhang,et al.  Application of Near-Infrared Hyperspectral Imaging with Variable Selection Methods to Determine and Visualize Caffeine Content of Coffee Beans , 2016, Food and Bioprocess Technology.

[42]  Da-Wen Sun,et al.  Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds , 2015, Food Analytical Methods.

[43]  Ana Soldado,et al.  Assessing the Value of a Portable Near Infrared Spectroscopy Sensor for Predicting Pork Meat Quality Traits of “Asturcelta Autochthonous Swine Breed” , 2013, Food Analytical Methods.