4.4 Article

Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network

Related references

Note: Only part of the references are listed.
Article Food Science & Technology

Identification of Corn Seeds with Different Freezing Damage Degree Based on Hyperspectral Reflectance Imaging and Deep Learning Method

Jun Zhang et al.

Summary: This study investigated the feasibility of combining hyperspectral imaging with deep convolutional neural network (DCNN) to classify different freeze-damaged corn seeds, with results showing that the DCNN model performed the best in classification. This method can provide a quick and cost-effective way to detect freezing damage in corn seeds.

FOOD ANALYTICAL METHODS (2021)

Article Food Science & Technology

Corn seed variety classification based on hyperspectral reflectance imaging and deep convolutional neural network

Jun Zhang et al.

Summary: This study investigated the feasibility of combining hyperspectral imaging with deep convolutional neural network for corn seed variety classification. The DCNN model showed superior performance in accuracy and evaluation indexes, effectively improving the classification accuracy of corn seed varieties.

JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION (2021)

Article Engineering, Chemical

Automatic surface defects classification of Kinnow mandarins using combination of multi-feature fusion techniques

Lingaraj Hadimani et al.

Summary: This study proposed a method based on image analysis and machine learning to identify and classify four defects of Kinnow mandarins. The study utilized adaptive thresholding technique for defect segmentation, and explored different texture descriptors and color models. Through training with two machine-learning techniques, significant recognition and classification accuracies were achieved.

JOURNAL OF FOOD PROCESS ENGINEERING (2021)

Review Chemistry, Analytical

Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering

Felix Lussier et al.

TRAC-TRENDS IN ANALYTICAL CHEMISTRY (2020)

Article Chemistry, Analytical

Application of deep learning and near infrared spectroscopy in cereal analysis

Ba Tuan Le

VIBRATIONAL SPECTROSCOPY (2020)

Article Food Science & Technology

Near Infrared Hyperspectral Imaging for White Maize Classification According to Grading Regulations

Kate Sendin et al.

FOOD ANALYTICAL METHODS (2019)

Review Chemistry, Analytical

Deep learning for vibrational spectral analysis: Recent progress and a practical guide

Jie Yang et al.

ANALYTICA CHIMICA ACTA (2019)

Article Agricultural Engineering

Varietal classification of barley by convolutional neural networks

Michal Kozlowski et al.

BIOSYSTEMS ENGINEERING (2019)

Article Automation & Control Systems

A practical convolutional neural network model for discriminating Raman spectra of human and animal blood

Jialin Dong et al.

JOURNAL OF CHEMOMETRICS (2019)

Review Biochemical Research Methods

Hyperspectral imaging for seed quality and safety inspection: a review

Lei Feng et al.

PLANT METHODS (2019)

Article Chemistry, Analytical

Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning

Pengcheng Nie et al.

SENSORS AND ACTUATORS B-CHEMICAL (2019)

Article Chemistry, Analytical

DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis

Xiaolei Zhang et al.

ANALYTICA CHIMICA ACTA (2019)

Review Chemistry, Analytical

Near infrared spectroscopy: A mature analytical technique with new perspectives - A review

Celio Pasquini

ANALYTICA CHIMICA ACTA (2018)

Article Automation & Control Systems

Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration

Chenhao Cui et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2018)

Article Chemistry, Multidisciplinary

Application of hyperspectral imaging and chemometrics for variety classification of maize seeds

Yiying Zhao et al.

RSC ADVANCES (2018)

Article Chemistry, Multidisciplinary

Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network

Zhengjun Qiu et al.

APPLIED SCIENCES-BASEL (2018)

Article Engineering, Chemical

A METHOD FOR RAPID IDENTIFICATION OF RICE ORIGIN BY HYPERSPECTRAL IMAGING TECHNOLOGY

Jun Sun et al.

JOURNAL OF FOOD PROCESS ENGINEERING (2017)

Article Chemistry, Analytical

Convolutional neural networks for vibrational spectroscopic data analysis

Jacopo Acquarelli et al.

ANALYTICA CHIMICA ACTA (2017)

Article Agricultural Engineering

Detection of fungal infection and Ochratoxin A contamination in stored barley using near-infrared hyperspectral imaging

Thiruppathi Senthilkumar et al.

BIOSYSTEMS ENGINEERING (2016)

Article Chemistry, Applied

Classification of maize kernels using NIR hyperspectral imaging

Paul J. Williams et al.

FOOD CHEMISTRY (2016)

Article Agriculture, Multidisciplinary

Identifying barley varieties by computer vision

Piotr M. Szczypinski et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2015)

Article Chemistry, Analytical

Influence of grain topography on near infrared hyperspectral images

Marena Manley et al.

TALANTA (2012)

Article Biochemical Research Methods

Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics

Cushla M. McGoverin et al.

ANALYTICAL AND BIOANALYTICAL CHEMISTRY (2011)

Article Agriculture, Multidisciplinary

Analysis of Pregerminated Barley Using Hyperspectral Image Analysis

Morten Arngren et al.

JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY (2011)

Review Chemistry, Analytical

Review of the most common pre-processing techniques for near-infrared spectra

Asmund Rinnan et al.

TRAC-TRENDS IN ANALYTICAL CHEMISTRY (2009)

Article Agricultural Engineering

Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes

S. Mahesh et al.

BIOSYSTEMS ENGINEERING (2008)