169 related articles for article (PubMed ID: 35358780)
1. Intelligent detection of hard seeds of snap bean based on hyperspectral imaging.
Wang J; Sun L; Feng G; Bai H; Yang J; Gai Z; Zhao Z; Zhang G
Spectrochim Acta A Mol Biomol Spectrosc; 2022 Jul; 275():121169. PubMed ID: 35358780
[TBL] [Abstract][Full Text] [Related]
2. Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM.
Yang J; Sun L; Xing W; Feng G; Bai H; Wang J
Spectrochim Acta A Mol Biomol Spectrosc; 2021 May; 253():119585. PubMed ID: 33662700
[TBL] [Abstract][Full Text] [Related]
3. Sugarbeet Seed Germination Prediction Using Hyperspectral Imaging Information Fusion.
Wang J; Sun L; Xing W; Feng G; Yang J; Li J; Li W
Appl Spectrosc; 2023 Jul; 77(7):710-722. PubMed ID: 37246428
[TBL] [Abstract][Full Text] [Related]
4. Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed.
Wang Z; Fan S; Wu J; Zhang C; Xu F; Yang X; Li J
Spectrochim Acta A Mol Biomol Spectrosc; 2021 Jun; 254():119666. PubMed ID: 33744703
[TBL] [Abstract][Full Text] [Related]
5. Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression.
Cui H; Cheng Z; Li P; Miao A
Sensors (Basel); 2020 Aug; 20(17):. PubMed ID: 32842673
[TBL] [Abstract][Full Text] [Related]
6. Research on nondestructive identification of grape varieties based on EEMD-DWT and hyperspectral image.
Xu M; Sun J; Zhou X; Tang N; Shen J; Wu X
J Food Sci; 2021 May; 86(5):2011-2023. PubMed ID: 33885160
[TBL] [Abstract][Full Text] [Related]
7. Variety classification of coated maize seeds based on Raman hyperspectral imaging.
Liu Q; Wang Z; Long Y; Zhang C; Fan S; Huang W
Spectrochim Acta A Mol Biomol Spectrosc; 2022 Apr; 270():120772. PubMed ID: 34973616
[TBL] [Abstract][Full Text] [Related]
8. Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds.
Zhang L; Sun H; Rao Z; Ji H
Spectrochim Acta A Mol Biomol Spectrosc; 2020 Mar; 229():117973. PubMed ID: 31887678
[TBL] [Abstract][Full Text] [Related]
9. Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis.
Pang L; Wang J; Men S; Yan L; Xiao J
Spectrochim Acta A Mol Biomol Spectrosc; 2021 Jan; 245():118888. PubMed ID: 32947159
[TBL] [Abstract][Full Text] [Related]
10. [Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].
Li XL; Yi SL; He SL; Lü Q; Xie RJ; Zheng YQ; Deng L
Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Sep; 35(9):2639-43. PubMed ID: 26669182
[TBL] [Abstract][Full Text] [Related]
11. [Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].
Huang T; Li XY; Xu ML; Jin R; Ku J; Xu SM; Wu ZZ
Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Jan; 35(1):198-202. PubMed ID: 25993848
[TBL] [Abstract][Full Text] [Related]
12. A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds.
Zhang T; Wei W; Zhao B; Wang R; Li M; Yang L; Wang J; Sun Q
Sensors (Basel); 2018 Mar; 18(3):. PubMed ID: 29517991
[TBL] [Abstract][Full Text] [Related]
13. Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning.
Xu P; Sun W; Xu K; Zhang Y; Tan Q; Qing Y; Yang R
Foods; 2022 Dec; 12(1):. PubMed ID: 36613360
[TBL] [Abstract][Full Text] [Related]
14. Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network.
Fan Y; An T; Wang Q; Yang G; Huang W; Wang Z; Zhao C; Tian X
Front Plant Sci; 2023; 14():1248598. PubMed ID: 37711294
[TBL] [Abstract][Full Text] [Related]
15. Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms.
Hu Y; Wang Z; Li X; Li L; Wang X; Wei Y
Sensors (Basel); 2022 Aug; 22(16):. PubMed ID: 36015825
[TBL] [Abstract][Full Text] [Related]
16. Hyperspectral imaging with machine learning for non-destructive classification of
Xu Y; Wu W; Chen Y; Zhang T; Tu K; Hao Y; Cao H; Dong X; Sun Q
Front Plant Sci; 2022; 13():1031849. PubMed ID: 36523615
[TBL] [Abstract][Full Text] [Related]
17. Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods.
Wang Z; Huang W; Tian X; Long Y; Li L; Fan S
Front Plant Sci; 2022; 13():849495. PubMed ID: 35620676
[TBL] [Abstract][Full Text] [Related]
18. Integration of spectroscopy and image for identifying fusarium damage in wheat kernels.
Zhang D; Chen G; Zhang H; Jin N; Gu C; Weng S; Wang Q; Chen Y
Spectrochim Acta A Mol Biomol Spectrosc; 2020 Aug; 236():118344. PubMed ID: 32330824
[TBL] [Abstract][Full Text] [Related]
19. Rapid and Non-Destructive Prediction of Moisture Content in Maize Seeds Using Hyperspectral Imaging.
Xue H; Xu X; Yang Y; Hu D; Niu G
Sensors (Basel); 2024 Mar; 24(6):. PubMed ID: 38544118
[TBL] [Abstract][Full Text] [Related]
20. Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms.
Wei Y; Li X; Pan X; Li L
Sensors (Basel); 2020 Dec; 20(23):. PubMed ID: 33297289
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]