157 related articles for article (PubMed ID: 35690826)
21. 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]
22. 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]
23. 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]
24. [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]
25. Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model.
Shi Y; Li J; Yu Z; Li Y; Hu Y; Wu L
Foods; 2022 Nov; 11(21):. PubMed ID: 36360144
[TBL] [Abstract][Full Text] [Related]
26. 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]
27. Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds.
Zhang X; Liu F; He Y; Li X
Sensors (Basel); 2012 Dec; 12(12):17234-46. PubMed ID: 23235456
[TBL] [Abstract][Full Text] [Related]
28. Rapid nondestructive detecting of sorghum varieties based on hyperspectral imaging and convolutional neural network.
Bu Y; Jiang X; Tian J; Hu X; Han L; Huang D; Luo H
J Sci Food Agric; 2023 Jun; 103(8):3970-3983. PubMed ID: 36397181
[TBL] [Abstract][Full Text] [Related]
29. Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties.
Zhu S; Zhou L; Gao P; Bao Y; He Y; Feng L
Molecules; 2019 Sep; 24(18):. PubMed ID: 31500333
[TBL] [Abstract][Full Text] [Related]
30. 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]
31. 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]
32. Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis.
Miao A; Zhuang J; Tang Y; He Y; Chu X; Luo S
Sensors (Basel); 2018 Dec; 18(12):. PubMed ID: 30545028
[TBL] [Abstract][Full Text] [Related]
33. Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager.
Jiao C; Xu Z; Bian Q; Forsberg E; Tan Q; Peng X; He S
Spectrochim Acta A Mol Biomol Spectrosc; 2021 Nov; 261():120054. PubMed ID: 34119773
[TBL] [Abstract][Full Text] [Related]
34. Rapid and visual detection of the main chemical compositions in maize seeds based on Raman hyperspectral imaging.
Yang G; Wang Q; Liu C; Wang X; Fan S; Huang W
Spectrochim Acta A Mol Biomol Spectrosc; 2018 Jul; 200():186-194. PubMed ID: 29680497
[TBL] [Abstract][Full Text] [Related]
35. A multi-layer perceptron-based approach for early detection of BSR disease in oil palm trees using hyperspectral images.
Lee CC; Koo VC; Lim TS; Lee YP; Abidin H
Heliyon; 2022 Apr; 8(4):e09252. PubMed ID: 35445158
[TBL] [Abstract][Full Text] [Related]
36. The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM.
He Y; Zhang W; Ma Y; Li J; Ma B
Molecules; 2022 Jun; 27(13):. PubMed ID: 35807337
[TBL] [Abstract][Full Text] [Related]
37. [Feasibility study on an approach for identifying corn kernel varieties with seed coating agents via near infrared spectroscopy].
Jia SQ; Guo TT; Liu Z; Yan YL; An D; Gu JC; Li SM; Zhang SM; Zhu DH
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Nov; 34(11):2984-8. PubMed ID: 25752043
[TBL] [Abstract][Full Text] [Related]
38. Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network.
Wu N; Zhang Y; Na R; Mi C; Zhu S; He Y; Zhang C
RSC Adv; 2019 Apr; 9(22):12635-12644. PubMed ID: 35515879
[TBL] [Abstract][Full Text] [Related]
39. Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects.
Nansen C; Imtiaz MS; Mesgaran MB; Lee H
Plant Methods; 2022 Jun; 18(1):74. PubMed ID: 35658997
[TBL] [Abstract][Full Text] [Related]
40. [Maize seed identification using hyperspectral imaging and SVDD algorithm].
Zhu QB; Feng ZL; Huang M; Zhu X
Guang Pu Xue Yu Guang Pu Fen Xi; 2013 Feb; 33(2):517-21. PubMed ID: 23697145
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
