175 related articles for article (PubMed ID: 36247557)
21. Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning.
Wu N; Liu F; Meng F; Li M; Zhang C; He Y
Front Bioeng Biotechnol; 2021; 9():696292. PubMed ID: 34368096
[TBL] [Abstract][Full Text] [Related]
22. Near-infrared hyperspectral imaging for online measurement of the viability detection of naturally aged watermelon seeds.
Yasmin J; Ahmed MR; Wakholi C; Lohumi S; Mukasa P; Kim G; Kim J; Lee H; Cho BK
Front Plant Sci; 2022; 13():986754. PubMed ID: 36420027
[TBL] [Abstract][Full Text] [Related]
23. [Fast Identification of Transgenic Soybean Varieties Based Near Infrared Hyperspectral Imaging Technology].
Wang HL; Yang XD; Zhang C; Guo DQ; Bao YD; He Y; Liu F
Guang Pu Xue Yu Guang Pu Fen Xi; 2016 Jun; 36(6):1843-7. PubMed ID: 30052403
[TBL] [Abstract][Full Text] [Related]
24. [Purity measurement of hybrid rice seed Yixiang 725 with visible-near infrared reflectance spectra].
Liang L; Yang MH; Liu ZX; Xu HW; Liu FH; He QZ; Luo YF
Guang Pu Xue Yu Guang Pu Fen Xi; 2009 Nov; 29(11):2962-5. PubMed ID: 20101964
[TBL] [Abstract][Full Text] [Related]
25. Rapid and accurate identification of bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning.
Wu N; Weng S; Xiao Q; Jiang H; Zhao Y; He Y
Spectrochim Acta A Mol Biomol Spectrosc; 2024 Apr; 311():123889. PubMed ID: 38340442
[TBL] [Abstract][Full Text] [Related]
26. A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning.
Zhu S; Zhang J; Chao M; Xu X; Song P; Zhang J; Huang Z
Molecules; 2019 Dec; 25(1):. PubMed ID: 31905957
[TBL] [Abstract][Full Text] [Related]
27. [Maize Hybrid Seed Purity Identification Based on Near Infrared Reflectance (NIR) and Transmittance (NIT) Spectra].
Li TX; Jia SQ; Liu X; Zhao SY; Ran H; Yan YL; An D
Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Dec; 35(12):3388-92. PubMed ID: 26964215
[TBL] [Abstract][Full Text] [Related]
28. [Variety recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning].
Cheng SX; Kong WW; Zhang C; Liu F; He Y
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Sep; 34(9):2519-22. PubMed ID: 25532356
[TBL] [Abstract][Full Text] [Related]
29. Cotton seed cultivar identification based on the fusion of spectral and textural features.
Liu X; Guo P; Xu Q; Du W
PLoS One; 2024; 19(5):e0303219. PubMed ID: 38805455
[TBL] [Abstract][Full Text] [Related]
30. [Study on Visual Identification of Corn Seeds Based on Hyperspectral Imaging Technology].
Wu X; Zhang WZ; Lu JF; Qiu ZJ; He Y
Guang Pu Xue Yu Guang Pu Fen Xi; 2016 Feb; 36(2):511-4. PubMed ID: 27209759
[TBL] [Abstract][Full Text] [Related]
31. [The Classification of Wheat Varieties Based on Near Infrared Hyperspectral Imaging and Information Fusion].
Dong G; Guo J; Wang C; Chen ZL; Zheng L; Zhu DZ
Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Dec; 35(12):3369-74. PubMed ID: 26964212
[TBL] [Abstract][Full Text] [Related]
32. SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits.
Jeong SW; Lyu JI; Jeong H; Baek J; Moon JK; Lee C; Choi MG; Kim KH; Park YI
Plant Cell Rep; 2024 Jun; 43(7):164. PubMed ID: 38852113
[TBL] [Abstract][Full Text] [Related]
33. ADFSNet: An Adaptive Domain Feature Separation Network for the Classification of Wheat Seed Using Hyperspectral Images.
Zhao X; Liu S; Que H; Huang M; Zhu Q
Sensors (Basel); 2023 Sep; 23(19):. PubMed ID: 37836946
[TBL] [Abstract][Full Text] [Related]
34. [Purity Detection Model Update of Maize Seeds Based on Active Learning].
Tang JY; Huang M; Zhu QB
Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Aug; 35(8):2136-40. PubMed ID: 26672281
[TBL] [Abstract][Full Text] [Related]
35. The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning.
Kang Z; Fan R; Zhan C; Wu Y; Lin Y; Li K; Qing R; Xu L
Molecules; 2024 Feb; 29(3):. PubMed ID: 38338424
[TBL] [Abstract][Full Text] [Related]
36. Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis.
Kong W; Zhang C; Liu F; Nie P; He Y
Sensors (Basel); 2013 Jul; 13(7):8916-27. PubMed ID: 23857260
[TBL] [Abstract][Full Text] [Related]
37. Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network.
Zhang J; Yang Y; Feng X; Xu H; Chen J; He Y
Front Plant Sci; 2020; 11():821. PubMed ID: 32670316
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification.
Yang X; Hong H; You Z; Cheng F
Sensors (Basel); 2015 Jul; 15(7):15578-94. PubMed ID: 26140347
[TBL] [Abstract][Full Text] [Related]
40. 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]
[Previous] [Next] [New Search]