134 related articles for article (PubMed ID: 38521920)
41. Prediction of Chlorophyll Content in Multi-Temporal Winter Wheat Based on Multispectral and Machine Learning.
Wang W; Cheng Y; Ren Y; Zhang Z; Geng H
Front Plant Sci; 2022; 13():896408. PubMed ID: 35712585
[TBL] [Abstract][Full Text] [Related]
42. Potential of UAV-Based Active Sensing for Monitoring Rice Leaf Nitrogen Status.
Li S; Ding X; Kuang Q; Ata-Ui-Karim ST; Cheng T; Liu X; Tian Y; Zhu Y; Cao W; Cao Q
Front Plant Sci; 2018; 9():1834. PubMed ID: 30619407
[TBL] [Abstract][Full Text] [Related]
43. 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]
44. Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements.
Chavana-Bryant C; Malhi Y; Wu J; Asner GP; Anastasiou A; Enquist BJ; Cosio Caravasi EG; Doughty CE; Saleska SR; Martin RE; Gerard FF
New Phytol; 2017 May; 214(3):1049-1063. PubMed ID: 26877108
[TBL] [Abstract][Full Text] [Related]
45. Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice.
Zheng H; Cheng T; Li D; Yao X; Tian Y; Cao W; Zhu Y
Front Plant Sci; 2018; 9():936. PubMed ID: 30034405
[TBL] [Abstract][Full Text] [Related]
46. A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data.
Fu B; Li S; Lao Z; Wei Y; Song K; Deng T; Wang Y
Water Res; 2024 Jun; 257():121673. PubMed ID: 38688189
[TBL] [Abstract][Full Text] [Related]
47. 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]
48. Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters.
Wijesingha J; Dayananda S; Wachendorf M; Astor T
Sensors (Basel); 2021 Apr; 21(8):. PubMed ID: 33924176
[TBL] [Abstract][Full Text] [Related]
49. Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.
Singh RN; Krishnan P; Singh VK; Sah S; Das B
Sci Rep; 2023 Nov; 13(1):18814. PubMed ID: 37914800
[TBL] [Abstract][Full Text] [Related]
50. Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning.
Kasimati A; Espejo-García B; Darra N; Fountas S
Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35590939
[TBL] [Abstract][Full Text] [Related]
51. Inversion modeling of japonica rice canopy chlorophyll content with UAV hyperspectral remote sensing.
Cao Y; Jiang K; Wu J; Yu F; Du W; Xu T
PLoS One; 2020; 15(9):e0238530. PubMed ID: 32915830
[TBL] [Abstract][Full Text] [Related]
52. Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset.
Van Wittenberghe S; Verrelst J; Rivera JP; Alonso L; Moreno J; Samson R
J Photochem Photobiol B; 2014 May; 134():37-48. PubMed ID: 24792473
[TBL] [Abstract][Full Text] [Related]
53. Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning.
Feng ZH; Wang LY; Yang ZQ; Zhang YY; Li X; Song L; He L; Duan JZ; Feng W
Front Plant Sci; 2022; 13():828454. PubMed ID: 35386677
[TBL] [Abstract][Full Text] [Related]
54. 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]
55. Improving grain yield prediction through fusion of multi-temporal spectral features and agronomic trait parameters derived from UAV imagery.
Zhou H; Yang J; Lou W; Sheng L; Li D; Hu H
Front Plant Sci; 2023; 14():1217448. PubMed ID: 37908835
[TBL] [Abstract][Full Text] [Related]
56. Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis.
Li H; Lin W; Pang F; Jiang X; Cao W; Zhu Y; Ni J
Sensors (Basel); 2020 May; 20(10):. PubMed ID: 32443796
[TBL] [Abstract][Full Text] [Related]
57. Arbuscular mycorrhizal fungi inoculation and phosphorus application improve growth, physiological traits, and grain yield of rice under alternate wetting and drying irrigation.
Das D; Ullah H; Himanshu SK; Tisarum R; Cha-Um S; Datta A
J Plant Physiol; 2022 Nov; 278():153829. PubMed ID: 36202058
[TBL] [Abstract][Full Text] [Related]
58. [Model construction and application for nitrogen nutrition monitoring and diagnosis in double-cropping rice of Jiangxi Province, China].
Li YD; Cao ZS; Sun BF; Ye C; Shu SF; Huang JB; Wang KJ; Tian YC
Ying Yong Sheng Tai Xue Bao; 2020 Feb; 31(2):433-440. PubMed ID: 32476335
[TBL] [Abstract][Full Text] [Related]
59. Estimation of forest canopy closure in northwest Yunnan based on multi-source remote sensing data colla-boration.
Zhou WW; Shu QT; Wang SW; Yang ZD; Luo SL; Xu L; Xiao JN
Ying Yong Sheng Tai Xue Bao; 2023 Jul; 34(7):1806-1816. PubMed ID: 37694464
[TBL] [Abstract][Full Text] [Related]
60. Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll.
Zhou J; Li F; Wang X; Yin H; Zhang W; Du J; Pu H
Plants (Basel); 2024 May; 13(9):. PubMed ID: 38732485
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
