Spatial-spectral feature extraction of hyperspectral images for wheat seed identification-Reference-Cited by-同舟云学术

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Spatial-spectral feature extraction of hyperspectral images for wheat seed identification

Published:2022-07 Issue: Volume:101 Page:108077
ISSN:0045-7906
Container-title:Computers and Electrical Engineering
language:en
Short-container-title:Computers and Electrical Engineering

Author:

Jin Songlin,Zhang Weidong,Yang Pengfei,Zheng Ying,An Jinliang,Zhang Ziyang,Qu Peixin,Pan Xipeng

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,General Computer Science,Control and Systems Engineering

Reference49 articles.

1. Characterization of agro-diversity by seed storage protein electrophoresis: focus on rice germplasm from Uttarakhand Himalaya, India;Jugran;Rice Sci,2010

2. Non-destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: a review;Su;Crit Rev Food Sci Nutr,2015

3. Classification of viable and non-viable spinach (Spinacia oleracea L.) seeds by single seed near infrared spectroscopy and extended canonical variates analysis;Olesen;J Near Infrared Spectrosc,2011

4. Active learning for multiclass image classification;Joshi;IEEE Trans Pattern Anal Mach Intell,2012

5. Corn seed variety classification based on hyperspectral reflectance imaging and deep convolutional neural network;Zhang;J Food Measur Characterization,2021

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1. Research on nondestructive detection of sweet-waxy corn seed varieties and mildew based on stacked ensemble learning and hyperspectral feature fusion technology;Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy;2024-12

2. Research on an Intelligent Seed-Sorting Method and Sorter Based on Machine Vision and Lightweight YOLOv5n;Agronomy;2024-08-29

3. Maize seed fraud detection based on hyperspectral imaging and one-class learning;Engineering Applications of Artificial Intelligence;2024-07

4. Cotton seed cultivar identification based on the fusion of spectral and textural features;PLOS ONE;2024-05-28

5. Efficient residual network using hyperspectral images for corn variety identification;Frontiers in Plant Science;2024-04-16

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