196 related articles for article (PubMed ID: 33240295)
1. Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods.
Feng L; Wu B; Zhu S; Wang J; Su Z; Liu F; He Y; Zhang C
Front Plant Sci; 2020; 11():577063. PubMed ID: 33240295
[TBL] [Abstract][Full Text] [Related]
2. Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin.
Liu Y; Zhao S; Gao X; Fu S; Chao Song ; Dou Y; Shaozhong Song ; Qi C; Lin J
RSC Adv; 2022 Nov; 12(53):34520-34530. PubMed ID: 36545607
[TBL] [Abstract][Full Text] [Related]
3. Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.
Zhao D; Feng S; Cao Y; Yu F; Guan Q; Li J; Zhang G; Xu T
Front Plant Sci; 2022; 13():879668. PubMed ID: 35599890
[TBL] [Abstract][Full Text] [Related]
4. Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion.
Weng S; Tang P; Yuan H; Guo B; Yu S; Huang L; Xu C
Spectrochim Acta A Mol Biomol Spectrosc; 2020 Jun; 234():118237. PubMed ID: 32200232
[TBL] [Abstract][Full Text] [Related]
5. Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System.
Fan Y; Wang T; Qiu Z; Peng J; Zhang C; He Y
Sensors (Basel); 2017 Oct; 17(11):. PubMed ID: 29077040
[TBL] [Abstract][Full Text] [Related]
6. Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution.
Cao Y; Yuan P; Xu H; Martínez-Ortega JF; Feng J; Zhai Z
Front Plant Sci; 2022; 13():963170. PubMed ID: 35909723
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging.
Xiao Q; Bai X; Gao P; He Y
Sensors (Basel); 2020 Sep; 20(17):. PubMed ID: 32882807
[TBL] [Abstract][Full Text] [Related]
9. [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]
10. 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]
11. Monitoring fungal growth on brown rice grains using rapid and non-destructive hyperspectral imaging.
Siripatrawan U; Makino Y
Int J Food Microbiol; 2015 Apr; 199():93-100. PubMed ID: 25662486
[TBL] [Abstract][Full Text] [Related]
12. Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles.
Conrad AO; Li W; Lee DY; Wang GL; Rodriguez-Saona L; Bonello P
Plant Phenomics; 2020; 2020():8954085. PubMed ID: 33313566
[TBL] [Abstract][Full Text] [Related]
13. Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products.
Zhu H; Gowen A; Feng H; Yu K; Xu JL
Sensors (Basel); 2020 Sep; 20(18):. PubMed ID: 32957597
[TBL] [Abstract][Full Text] [Related]
14. [Rice blast prediction model based on analysis of chlorophyll fluorescence spectrum].
Zhou LN; Yu HY; Zhang L; Ren S; Sui YY; Yu LJ
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Apr; 34(4):1003-6. PubMed ID: 25007618
[TBL] [Abstract][Full Text] [Related]
15. Rapid identification of fish species by laser-induced breakdown spectroscopy and Raman spectroscopy coupled with machine learning methods.
Ren L; Tian Y; Yang X; Wang Q; Wang L; Geng X; Wang K; Du Z; Li Y; Lin H
Food Chem; 2023 Jan; 400():134043. PubMed ID: 36058043
[TBL] [Abstract][Full Text] [Related]
16. Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice.
Das B; Manohara KK; Mahajan GR; Sahoo RN
Spectrochim Acta A Mol Biomol Spectrosc; 2020 Mar; 229():117983. PubMed ID: 31896051
[TBL] [Abstract][Full Text] [Related]
17. Data Fusion of Two Hyperspectral Imaging Systems with Complementary Spectral Sensing Ranges for Blueberry Bruising Detection.
Fan S; Li C; Huang W; Chen L
Sensors (Basel); 2018 Dec; 18(12):. PubMed ID: 30562957
[TBL] [Abstract][Full Text] [Related]
18. Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning.
Jin B; Zhang C; Jia L; Tang Q; Gao L; Zhao G; Qi H
ACS Omega; 2022 Feb; 7(6):4735-4749. PubMed ID: 35187294
[TBL] [Abstract][Full Text] [Related]
19. Evaluation of rice varieties using LIBS and FTIR techniques associated with PCA and machine learning algorithms.
Ribeiro MCS; Senesi GS; Cabral JS; Cena C; Marangoni BS; Kiefer C; Nicolodelli G
Appl Opt; 2020 Nov; 59(32):10043-10048. PubMed ID: 33175777
[TBL] [Abstract][Full Text] [Related]
20. Application of Laser-Induced Breakdown Spectroscopy in Detection of Cadmium Content in Rice Stems.
Wang W; Kong W; Shen T; Man Z; Zhu W; He Y; Liu F; Liu Y
Front Plant Sci; 2020; 11():599616. PubMed ID: 33391312
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
