138 related articles for article (PubMed ID: 36396749)
1. Classification of rice leaf blast severity using hyperspectral imaging.
Zhang G; Xu T; Tian Y; Feng S; Zhao D; Guo Z
Sci Rep; 2022 Nov; 12(1):19757. PubMed ID: 36396749
[TBL] [Abstract][Full Text] [Related]
2. Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages.
Zhang G; Xu T; Tian Y
Plant Methods; 2022 Nov; 18(1):123. PubMed ID: 36403061
[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. 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]
5. [Estimating the severity of rice brown spot disease based on principal component analysis and radial basis function neural network].
Liu ZY; Huang JF; Tao RX; Zhang HZ
Guang Pu Xue Yu Guang Pu Fen Xi; 2008 Sep; 28(9):2156-60. PubMed ID: 19093583
[TBL] [Abstract][Full Text] [Related]
6. Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.
Liu S; Yu H; Sui Y; Zhou H; Zhang J; Kong L; Dang J; Zhang L
PLoS One; 2021; 16(9):e0257008. PubMed ID: 34478465
[TBL] [Abstract][Full Text] [Related]
7. Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images.
Lin F; Guo S; Tan C; Zhou X; Zhang D
Sensors (Basel); 2020 Nov; 20(21):. PubMed ID: 33147714
[TBL] [Abstract][Full Text] [Related]
8. High-Throughput Screening of Free Proline Content in Rice Leaf under Cadmium Stress Using Hyperspectral Imaging with Chemometrics.
Shen T; Zhang C; Liu F; Wang W; Lu Y; Chen R; He Y
Sensors (Basel); 2020 Jun; 20(11):. PubMed ID: 32517150
[TBL] [Abstract][Full Text] [Related]
9. Study on the identification of resistance of rice blast based on near infrared spectroscopy.
He Y; Zhao X; Zhang W; He X; Tong L
Spectrochim Acta A Mol Biomol Spectrosc; 2022 Feb; 266():120439. PubMed ID: 34601366
[TBL] [Abstract][Full Text] [Related]
10. [The estimation model of rice leaf area index using hyperspectral data based on support vector machine].
Yang XH; Huang JF; Wang XZ; Wang FM
Guang Pu Xue Yu Guang Pu Fen Xi; 2008 Aug; 28(8):1837-41. PubMed ID: 18975815
[TBL] [Abstract][Full Text] [Related]
11. [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]
12. [Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status].
Tan CW; Zhou QB; Qi L; Zhuang HY
Ying Yong Sheng Tai Xue Bao; 2008 Jun; 19(6):1261-8. PubMed ID: 18808018
[TBL] [Abstract][Full Text] [Related]
13. Predicting rice blast disease: machine learning versus process-based models.
Nettleton DF; Katsantonis D; Kalaitzidis A; Sarafijanovic-Djukic N; Puigdollers P; Confalonieri R
BMC Bioinformatics; 2019 Oct; 20(1):514. PubMed ID: 31640541
[TBL] [Abstract][Full Text] [Related]
14. [Identification and classification of rice leaf blast based on multi-spectral imaging sensor].
Feng L; Chai RY; Sun GM; Wu D; Lou BG; He Y
Guang Pu Xue Yu Guang Pu Fen Xi; 2009 Oct; 29(10):2730-3. PubMed ID: 20038048
[TBL] [Abstract][Full Text] [Related]
15. Prediction of cadmium concentration in brown rice before harvest by hyperspectral remote sensing.
Zhou W; Zhang J; Zou M; Liu X; Du X; Wang Q; Liu Y; Liu Y; Li J
Environ Sci Pollut Res Int; 2019 Jan; 26(2):1848-1856. PubMed ID: 30456622
[TBL] [Abstract][Full Text] [Related]
16. [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]
17. Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models.
Mandal N; Adak S; Das DK; Sahoo RN; Mukherjee J; Kumar A; Chinnusamy V; Das B; Mukhopadhyay A; Rajashekara H; Gakhar S
Front Plant Sci; 2023; 14():1067189. PubMed ID: 36909416
[TBL] [Abstract][Full Text] [Related]
18. Rice Blast Disease Recognition Using a Deep Convolutional Neural Network.
Liang WJ; Zhang H; Zhang GF; Cao HX
Sci Rep; 2019 Feb; 9(1):2869. PubMed ID: 30814523
[TBL] [Abstract][Full Text] [Related]
19. Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression.
Liu ZY; Huang JF; Shi JJ; Tao RX; Zhou W; Zhang LL
J Zhejiang Univ Sci B; 2007 Oct; 8(10):738-44. PubMed ID: 17910117
[TBL] [Abstract][Full Text] [Related]
20. Identification of Leaf-Scale Wheat Powdery Mildew (
Zhao J; Fang Y; Chu G; Yan H; Hu L; Huang L
Plants (Basel); 2020 Jul; 9(8):. PubMed ID: 32722022
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
